CN107085742A - A kind of intelligent optimization algorithm based on simple form neighborhood Yu many role's evolution strategies - Google Patents
A kind of intelligent optimization algorithm based on simple form neighborhood Yu many role's evolution strategies Download PDFInfo
- Publication number
- CN107085742A CN107085742A CN201710138048.2A CN201710138048A CN107085742A CN 107085742 A CN107085742 A CN 107085742A CN 201710138048 A CN201710138048 A CN 201710138048A CN 107085742 A CN107085742 A CN 107085742A
- Authority
- CN
- China
- Prior art keywords
- mrow
- msubsup
- msub
- particle
- search
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000005457 optimization Methods 0.000 title claims abstract description 26
- 239000002245 particle Substances 0.000 claims abstract description 121
- 238000012360 testing method Methods 0.000 claims abstract description 15
- 238000011156 evaluation Methods 0.000 claims description 4
- 230000006641 stabilisation Effects 0.000 claims 1
- 238000011105 stabilization Methods 0.000 claims 1
- 238000013459 approach Methods 0.000 abstract description 3
- 230000004927 fusion Effects 0.000 abstract 1
- 230000002068 genetic effect Effects 0.000 description 2
- 238000000034 method Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 201000010099 disease Diseases 0.000 description 1
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Life Sciences & Earth Sciences (AREA)
- Molecular Biology (AREA)
- Artificial Intelligence (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Medicines Containing Antibodies Or Antigens For Use As Internal Diagnostic Agents (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
Abstract
The present invention relates to a kind of intelligent optimization algorithm based on simple form neighborhood Yu many role's evolution strategies, belong to novel intelligent global optimization approach calculating field.First to be uniformly distributed probability in search space random initializtion particle position；Then each particle searches for new position using simple form neighborhood search operator, and utilize test function, evaluate the quality of each particle new position, further according to the quality of particle new position, it is determined that optimal particle and its optimal location in three character locations of each particle, last minute book iteration cycle, terminate this iterative search cycle, start next iterative search cycle, until particle converges to globe optimum position.The present invention proposes many role's state Evolutionary Search Strategies and simple form neighborhood search mechanism, the search of fusion group collaboration and tournament selection evolution strategy, is a kind of effective intelligent global optimization algorithm.
Description
Technical field
The present invention relates to a kind of intelligent optimization algorithm based on simple form neighborhood Yu many role's evolution strategies, realize and searched given
Rope space search belongs to novel intelligent global optimization approach calculating field to globe optimum position.
Background technology
In recent years, in order to solve the Global Optimal Problem in a large amount of actual application problems, many global optimization approaches are carried
Go out, wherein, the intelligent optimization algorithm based on swarm intelligence increasingly becomes the focus of optimized algorithm research.Such as genetic algorithm, exempt from
Epidemic disease algorithm, particle cluster algorithm, differential evolution algorithm, ant colony algorithm, fireworks algorithm etc..But these algorithms are there is also some shortcomings,
As search is easily trapped into local best points, it is impossible to converge to globe optimum；Algorithmic statement is relied on to the performance of globe optimum
The control parameter of algorithm；The stability of algorithmic statement to globe optimum is bad, and variance is bigger than normal.Meanwhile, for these algorithms
Innovatory algorithm is mostly by increasing algorithm complex, introducing more control parameters and realize more preferable algorithm performance.
The content of the invention
The technical problem to be solved in the present invention is to provide a kind of intelligence based on simple form neighborhood and many role's evolution strategies is excellent
Change algorithm, the global optimization for complex cost function.For these problems of intelligent optimization algorithm, grain is realized with many role's states
Son search diversity, the simple Swarm Intelligent Algorithm of algorithm structure.
The technical scheme is that：
A kind of intelligent optimization algorithm based on simple form neighborhood Yu many role's evolution strategies, is being searched with being uniformly distributed probability first
Rope space random initializtion particle position；Then each particle searches for new position using simple form neighborhood search operator, and utilizes survey
Trial function, evaluates the quality of each particle new position, further according to the quality of particle new position, it is determined that three roles of each particle
Optimal particle and its optimal location in position, last minute book iteration cycle, terminate this iterative search cycle, start next change
For the search cycle, until particle converges to globe optimum position.
The initialization of the search space particle is produced using probability function is uniformly distributed.
Particle simple form neighborhood search mode finally determines that the mode of new particle is as follows in the search space：
In search space RnIn, two dimensions p, q are randomly selected with even distribution pattern, search subspace R is built2, herein
Each particle simple form neighborhood search operator, is defined as follows in search subspace, colony：
Wherein,Be particle i in the t+1 times iteration, search son
Space R2On four new positions searching；Be particle i in the t times iteration, in search subspace R2On search
Original position；Be particle j in the t times iteration, in search subspace R2On the original position that searches；It is optimal in group
Particle o is in the t times iteration, in search subspace R2On the optimal location that searches；It is with positionCentered on,
PositionSymmetric position；It is with positionCentered on, positionSymmetric position, r11,r12,r21,r22,
r31,r32,r41And r42It is 8 random numbers produced on interval [0,1] with even distribution pattern.
Each particle has three role's states in the colony, is respectively：Center role's state, exploits role's state, explores angle
Color state, is defined respectively as：
Center role's state, is defined as the optimal location that each particle search is arrived；
Role's state is exploited, the latest position that each particle search is arrived is defined as；
Role's state is explored, is defined as each particle to be evenly distributed on the position of search space random position.
It is described to search for comprising the following steps that for globe optimum in search space：
S1, by m particle be based on be evenly distributed on search space carry out initialization random position；
Wherein,It is i-th of particle in RnPosition in k-th of dimension of search subspace,Withxk It is search respectively
The upper bound and lower bound of the space in k-th of dimension, rand (0,1) are the equally distributed random numbers on interval [0,1]；
S2, for each particle i in colony, in search space RnIn, two dimensions are randomly selected with even distribution pattern
P, q are spent, search subspace R is built2, in this search subspace, each particle utilizes simple form neighborhood search operator and many role's states
Search strategy searches for new position, is defined as follows：
Wherein,Be particle i in the t+1 times iteration, search son
Space R2On the four Ge Xin centers character locations that search；Be particle i in the t times iteration, in search subspace R2On
The former center character location searched；It is the center role's state produced from the t times iteration of particle j, exploits role's state, survey
Visit and randomly selected in role's state with even distribution pattern, in search subspace R2On position；It is that optimal particle o exists in group
In the t times iteration, in search subspace R2On the optimal center character location that searches；It is with positionFor in
The heart, positionSymmetric position；It is with positionCentered on, positionSymmetric position, r11,r12,
r21,r22,r31,r32,r41And r42It is 8 random numbers produced on interval [0,1] with even distribution pattern；
S3, for each particle i in colony, using step S2 in search subspace R2The 4 new central angles searched
Color bits is put：And keep its position in other dimensions constant, update every
Individual particle is in RnOn 4 Ge Xin centers character locations：
S4, the quality according to each particle of fitness function f (x) evaluations, it is determined that three role's states of each particle：Center
Role's state, exploits role's state, explores role's state, and definition is as follows respectively：
Center role's state --- using greediness as principle, it is defined as the optimal location that each particle search is arrived：xic(t+1)；
Exploitation role's state --- using nearest property as principle, the latest position that each particle search is arrived is defined as, i.e.,：
xil(t+1)={ xic1(t+1),xic2(t+1),xic3(t+1),xic4(t+1)} (14)
Exploration role's state --- using equally distributed randomness as principle, it is defined as each particle to be evenly distributed on search
The position of space random position：xig(t+1)；
The position of optimal particle in S5, record colony：xoc(t+1) S2, is returned, starts next search cycle, until group
Particle converges to the position of optimal particle in optimal location, i.e. colony and stable does not change to given accuracy in body.
The beneficial effects of the invention are as follows：
1st, in global search aspect of performance, property of the test function classical in this field to this intelligent optimization algorithm is utilized
It can be tested.Test result shows：For all test functions, especially multi-modal test function, algorithm is with high accuracy
Globe optimum is converged to.Illustrate that the algorithm is preferable in global search aspect of performance.
2nd, in terms of local convergence performance, property of the test function classical in this field to this intelligent optimization algorithm is utilized
It can be tested.Test result shows：Compared with other classical intelligent optimization algorithms and its innovatory algorithm, for all test letters
Number, the convergence of algorithm speed reaches consistent with them or better than them.
3rd, in terms of control parameter of algorithm, the algorithm only one of which control parameter --- Population.Compare other classics
Have more control parameter with improved intelligent optimization algorithm, and its performance is related to its control parameter, this algorithm embody compared with
Strong advantage.
4th, in terms of algorithm reliability, performance of the test function classical in this field to this intelligent optimization algorithm is utilized
Tested.Test result shows：Under 50 different random initialization, this algorithm does not occur the example for deviateing globe optimum
Outside.Compare other classical and improved intelligent optimization algorithm and occur that exceptionality restrains under different random initialization, show this
The variance index of algorithmic statement performance preferably, there is higher reliability.
Brief description of the drawings
Fig. 1 is algorithm flow chart of the invention；
Fig. 2 is distribution map of the embodiment of the present invention 1 using test function；
Convergent iterations figure of the algorithm search to optimal value in Fig. 3 embodiment of the present invention 1；
Fig. 4 embodiment of the present invention 2 uses the distribution map of test function；
Convergent iterations figure of the algorithm search to optimal value in Fig. 5 embodiment of the present invention 2.
Embodiment
In order to illustrate the performance of the algorithm, the optimization of a multi-modal function and a single mode state function is chosen respectively
Illustrated for example.
Embodiment 1：Implementation process is referring to Fig. 1, Fig. 2, shown in Fig. 3.It is specific as follows：
One object function to be optimized is：
It is a multi-modal function for including a large amount of local best points, and domain of definition is [- 600,600]n, and it is global most
The figure of merit is 0, and its Two dimensional Distribution is as shown in Figure 2.This algorithm is determined at random by the search space that domain of definition is set with being uniformly distributed
The initial position of each particle in position, then, algorithm will guide all particles in colony to converge to globe optimum.
Referring to Fig. 1, the searching algorithm is comprised the following steps that：
S1), m particle is based on to be evenly distributed on search space progress initialization random position；
Wherein,It is i-th of particle in RnPosition in k-th of dimension of search subspace.And xkRespectively -600 and
600, rand (0,1) are the equally distributed random number on interval [0,1]
S2), for each particle i in colony, in search space RnIn, two dimensions are randomly selected with even distribution pattern
P, q are spent, search subspace R is built2.In this search subspace, each particle utilizes simple form neighborhood search operator and many role's states
Search strategy searches for new position, is defined as follows：
Wherein,Be particle i in the t+1 times iteration, search son
Space R2On the four Ge Xin centers character locations that search；Be particle i in the t times iteration, in search subspace R2On
The former center character location searched；It is the center role's state produced from the t times iteration of particle j, exploits role's state, survey
Visit and randomly selected in role's state with even distribution pattern, in search subspace R2On position；It is that optimal particle o exists in group
In the t times iteration, in search subspace R2On the optimal center character location that searches；It is with positionFor in
The heart, positionSymmetric position；It is with positionCentered on, positionSymmetric position.r11,r12,
r21,r22,r31,r32,r41And r42It is 8 random numbers produced on interval [0,1] with even distribution pattern.
S3), for each particle i in colony, using step S2 in search subspace R2The 4 new central angles searched
Color bits is put：And keep its position in other dimensions constant, update every
Individual particle is in RnOn 4 Ge Xin centers character locations：
S4), the quality of each particle is evaluated according to fitness function f (x), it is determined that three role's states of each particle：In
Heart role's state, exploits role's state, explores role's state, and definition is as follows respectively：
Center role's state --- using greediness as principle, it is defined as the optimal location that each particle search is arrived：xic(t+1)。
Exploitation role's state --- using nearest property as principle, the latest position that each particle search is arrived is defined as, i.e.,：
xil(t+1)={ xic1(t+1),xic2(t+1),xic3(t+1),xic4(t+1)} (21)
Exploration role's state --- using equally distributed randomness as principle, it is defined as each particle to be evenly distributed on search
The position of space random position：xig(t+1)。
S5), the position of optimal particle in colony is recorded：xoc(t+1) S2, is returned), start next search cycle, until
Particle converges to the position of optimal particle in optimal location, i.e. colony and stable does not change to given accuracy in colony.
Fig. 3 is illustrated in this example, when Population is respectively 20 and 30, invention algorithmic statement to optimization object function most
The evolution curve of advantage, wherein, abscissa is iterations, and ordinate is that each iterative search (is searched to optimal value in order to embody
Rope has taken denary logarithm to the precision of optimal value to searching optimal value).It can be seen that from the evolution curve：With
Iterations increase, algorithm progressively converges to globe optimum, and precision is 10-16。
Embodiment 2：Implementation process is referring to Fig. 1, Fig. 4, shown in Fig. 5.It is specific as follows：
One object function to be optimized is：
It is a multi-modal function for including a large amount of local best points, and domain of definition is：[- 100,100]n, and it is global
Optimum point is：0, its Two dimensional Distribution is as shown in Figure 4.It is every that this algorithm will be uniformly distributed random position in domain of definition search space
The initial position of individual particle, then, algorithm will guide all particles in colony to converge to globe optimum.
Referring to Fig. 1, the searching algorithm is comprised the following steps that：
S1), m particle is based on to be evenly distributed on search space progress initialization random position；
Wherein,It is i-th of particle in RnPosition in k-th of dimension of search subspace.Withxk Be respectively -100 and
100, rand (0,1) are the equally distributed random number on interval [0,1]
S2), for each particle i in colony, in search space RnIn, two dimensions are randomly selected with even distribution pattern
P, q are spent, search subspace R is built2.In this search subspace, each particle utilizes simple form neighborhood search operator and many role's states
Search strategy searches for new position, is defined as follows：
Wherein,Be particle i in the t+1 times iteration, search son
Space R2On the four Ge Xin centers character locations that search；Be particle i in the t times iteration, in search subspace R2On
The former center character location searched；It is the center role's state produced from the t times iteration of particle j, exploits role's state, survey
Visit and randomly selected in role's state with even distribution pattern, in search subspace R2On position；It is that optimal particle o exists in group
In the t times iteration, in search subspace R2On the optimal center character location that searches；It is with positionFor in
The heart, positionSymmetric position；It is with positionCentered on, positionSymmetric position.r11,r12,
r21,r22,r31,r32,r41And r42It is 8 random numbers produced on interval [0,1] with even distribution pattern.
S3), for each particle i in colony, using step S2 in search subspace R2The 4 new central angles searched
Color bits is put：And keep its position in other dimensions constant, update every
Individual particle is in RnOn 4 Ge Xin centers character locations：
S4), the quality of each particle is evaluated according to fitness function f (x), it is determined that three role's states of each particle：In
Heart role's state, exploits role's state, explores role's state, and definition is as follows respectively：
Center role's state --- using greediness as principle, it is defined as the optimal location that each particle search is arrived：xic(t+1)。
Exploitation role's state --- using nearest property as principle, the latest position that each particle search is arrived is defined as, i.e.,：
xil(t+1)={ xic1(t+1),xic2(t+1),xic3(t+1),xic4(t+1)} (32)
Exploration role's state --- using equally distributed randomness as principle, it is defined as each particle to be evenly distributed on search
The position of space random position：xig(t+1)。
S5), the position of optimal particle in colony is recorded：xoc(t+1) S2, is returned), start next search cycle, until
Particle converges to the position of optimal particle in optimal location, i.e. colony and stable does not change to given accuracy in colony.
Shown in Fig. 5 in this example, when Population is respectively 20 and 30, invention algorithmic statement to optimization object function most
The evolution curve of advantage, wherein, abscissa is iterations, and ordinate is that each iterative search (is searched to optimal value in order to embody
Rope has taken denary logarithm to the precision of optimal value to searching optimal value).It can be seen that from the evolution curve：With
Iterations increase, algorithm progressively converges to globe optimum, and precision is 10-60。
In order to more intuitively can clearly represent the effect of the present invention, 14 classical evaluation functions are set forth below out, such as the institute of table 1
Show.Example test is carried out under Matlab platforms to these evaluation functions using inventive algorithm, reflection algorithmic statement is have recorded
The algorithm iteration number of times index of speed, reflects the optimal value average value index of algorithmic statement precision, reflects algorithmic statement stability
Optimal value variance index, as a result such as table 2 and table 3.In table simultaneously with other algorithms --- tachytelic evolution planning algorithm (FEP) and
The performance indications of Orthogonal Genetic Algorithm (OGA/Q) are contrasted.
Summary analysis 1, table 2, the data of table 3 illustrate inventive algorithm no matter in convergence rate, convergence precision, convergence
Preferable advantage is respectively provided with terms of stability.
Table 1：Test of heuristics function
Table 2：Algorithm performance compares
Table 3：Algorithm performance compares
Above in association with accompanying drawing to the present invention embodiment be explained in detail, but the present invention be not limited to it is above-mentioned
Embodiment, can also be before present inventive concept not be departed from the knowledge that those of ordinary skill in the art possess
Put that various changes can be made.
Claims (5)
1. a kind of intelligent optimization algorithm based on simple form neighborhood Yu many role's evolution strategies, it is characterised in that：
First to be uniformly distributed probability in search space random initializtion particle position；Then each particle is searched using simple form neighborhood
Rope operator searches for new position, and utilizes test function, the quality of each particle new position is evaluated, further according to the excellent of particle new position
It is bad, it is determined that optimal particle and its optimal location in three character locations of each particle, last minute book iteration cycle, terminate this
In the iterative search cycle, start next iterative search cycle, until particle converges to globe optimum position.
2. the intelligent optimization algorithm according to claim 1 based on simple form neighborhood Yu many role's evolution strategies, its feature exists
In：The initialization of the search space particle is produced using probability function is uniformly distributed.
3. the intelligent optimization algorithm according to claim 1 based on simple form neighborhood Yu many role's evolution strategies, its feature exists
In：Particle simple form neighborhood search mode finally determines that the mode of new particle is as follows in the search space：
In search space RnIn, two dimensions p, q are randomly selected with even distribution pattern, search subspace R is built2, search for herein
Each particle simple form neighborhood search operator, is defined as follows in subspace, colony：
<mrow>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mn>1</mn>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>r</mi>
<mn>11</mn>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>r</mi>
<mn>12</mn>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mi>x</mi>
<mi>j</mi>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>r</mi>
<mn>11</mn>
</msub>
<mo>-</mo>
<msub>
<mi>r</mi>
<mn>12</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<msubsup>
<mi>x</mi>
<mi>o</mi>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mn>2</mn>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>r</mi>
<mn>21</mn>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>r</mi>
<mn>22</mn>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mi>j</mi>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>r</mi>
<mn>21</mn>
</msub>
<mo>-</mo>
<msub>
<mi>r</mi>
<mn>22</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<msubsup>
<mi>x</mi>
<mi>o</mi>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>2</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mn>3</mn>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>r</mi>
<mn>31</mn>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>r</mi>
<mn>32</mn>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mi>j</mi>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>r</mi>
<mn>31</mn>
</msub>
<mo>-</mo>
<msub>
<mi>r</mi>
<mn>32</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<msubsup>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mi>o</mi>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>3</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mn>4</mn>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>r</mi>
<mn>41</mn>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>r</mi>
<mn>42</mn>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mi>x</mi>
<mi>j</mi>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>r</mi>
<mn>41</mn>
</msub>
<mo>-</mo>
<msub>
<mi>r</mi>
<mn>42</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<msubsup>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mi>o</mi>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>4</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein,Be particle i in the t+1 times iteration, in search subspace R2
On four new positions searching；Be particle i in the t times iteration, in search subspace R2On the original position that searches；Be particle j in the t times iteration, in search subspace R2On the original position that searches；It is that optimal particle o exists in group
In the t times iteration, in search subspace R2On the optimal location that searches；It is with positionCentered on, position
Symmetric position；It is with positionCentered on, positionSymmetric position, r11,r12,r21,r22,r31,r32,r41
And r42It is 8 random numbers produced on interval [0,1] with even distribution pattern.
4. the intelligent optimization algorithm according to claim 1 based on simple form neighborhood Yu many role's evolution strategies, its feature exists
In：Each particle has three role's states in the colony, is respectively：Center role's state, exploits role's state, explores role's state,
It is defined respectively as：
Center role's state, is defined as the optimal location that each particle search is arrived；
Role's state is exploited, the latest position that each particle search is arrived is defined as；
Role's state is explored, is defined as each particle to be evenly distributed on the position of search space random position.
5. the intelligent optimization algorithm according to claim 1 based on simple form neighborhood Yu many role's evolution strategies, its feature exists
In：It is described to search for comprising the following steps that for globe optimum in search space：
S1, by m particle be based on be evenly distributed on search space carry out initialization random position；
<mrow>
<msubsup>
<mi>x</mi>
<mi>i</mi>
<mi>k</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mn>0</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munder>
<msup>
<mi>x</mi>
<mi>k</mi>
</msup>
<mo>&OverBar;</mo>
</munder>
<mo>+</mo>
<mi>r</mi>
<mi>a</mi>
<mi>n</mi>
<mi>d</mi>
<mrow>
<mo>(</mo>
<mn>0</mn>
<mo>,</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>&times;</mo>
<mrow>
<mo>(</mo>
<mover>
<msup>
<mi>x</mi>
<mi>k</mi>
</msup>
<mo>&OverBar;</mo>
</mover>
<mo>-</mo>
<munder>
<msup>
<mi>x</mi>
<mi>k</mi>
</msup>
<mo>&OverBar;</mo>
</munder>
<mo>)</mo>
</mrow>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>5</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein,It is i-th of particle in RnPosition in k-th of dimension of search subspace,Withxk It is search subspace respectively
The upper bound and lower bound in k-th of dimension, rand (0,1) are the equally distributed random numbers on interval [0,1]；
S2, for each particle i in colony, in search space RnIn, two dimensions p, q are randomly selected with even distribution pattern,
Build search subspace R2, in this search subspace, each particle utilizes simple form neighborhood search operator and many role's states search plan
New position is slightly searched for, is defined as follows：
<mrow>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>1</mn>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>r</mi>
<mn>11</mn>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>r</mi>
<mn>12</mn>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mo>&lsqb;</mo>
<mi>c</mi>
<mo>,</mo>
<mi>l</mi>
<mo>,</mo>
<mi>g</mi>
<mo>&rsqb;</mo>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>r</mi>
<mn>11</mn>
</msub>
<mo>-</mo>
<msub>
<mi>r</mi>
<mn>12</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>o</mi>
<mi>c</mi>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>6</mn>
<mo>)</mo>
</mrow>
</mrow>
1
<mrow>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>2</mn>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>r</mi>
<mn>21</mn>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>r</mi>
<mn>22</mn>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>j</mi>
<mo>&lsqb;</mo>
<mi>c</mi>
<mo>,</mo>
<mi>l</mi>
<mo>,</mo>
<mi>g</mi>
<mo>&rsqb;</mo>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>r</mi>
<mn>21</mn>
</msub>
<mo>-</mo>
<msub>
<mi>r</mi>
<mn>22</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>o</mi>
<mi>c</mi>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>7</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>3</mn>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>r</mi>
<mn>31</mn>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>r</mi>
<mn>32</mn>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>j</mi>
<mo>&lsqb;</mo>
<mi>c</mi>
<mo>,</mo>
<mi>l</mi>
<mo>,</mo>
<mi>g</mi>
<mo>&rsqb;</mo>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>r</mi>
<mn>31</mn>
</msub>
<mo>-</mo>
<msub>
<mi>r</mi>
<mn>32</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<msubsup>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>o</mi>
<mi>c</mi>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>8</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>4</mn>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<msub>
<mi>r</mi>
<mn>41</mn>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<msub>
<mi>r</mi>
<mn>42</mn>
</msub>
<mo>&CenterDot;</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mo>&lsqb;</mo>
<mi>c</mi>
<mo>,</mo>
<mi>l</mi>
<mo>,</mo>
<mi>g</mi>
<mo>&rsqb;</mo>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<msub>
<mi>r</mi>
<mn>41</mn>
</msub>
<mo>-</mo>
<msub>
<mi>r</mi>
<mn>42</mn>
</msub>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<msubsup>
<mover>
<mi>x</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>o</mi>
<mi>c</mi>
</mrow>
<mrow>
<mi>p</mi>
<mo>,</mo>
<mi>q</mi>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>9</mn>
<mo>)</mo>
</mrow>
</mrow>
Wherein,Be particle i in the t+1 times iteration, in search subspace R2
On the four Ge Xin centers character locations that search；Be particle i in the t times iteration, in search subspace R2On search
Former center character location；It is the center role's state produced from the t times iteration of particle j, exploits role's state, explores role
Randomly selected in state with even distribution pattern, in search subspace R2On position；Be in group optimal particle o at the t times
In iteration, in search subspace R2On the optimal center character location that searches；It is with positionCentered on, position
PutSymmetric position；It is with positionCentered on, positionSymmetric position, r11,r12,r21,r22,
r31,r32,r41And r42It is 8 random numbers produced on interval [0,1] with even distribution pattern；
S3, for each particle i in colony, using step S2 in search subspace R2The 4 new central angle color bits searched
Put：And keep its position in other dimensions constant, update each grain
Son is in RnOn 4 Ge Xin centers character locations：
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>1</mn>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mo>&lsqb;</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>1</mn>
</mrow>
<mn>1</mn>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>1</mn>
</mrow>
<mn>2</mn>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mn>....</mn>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>1</mn>
</mrow>
<mi>p</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>1</mn>
</mrow>
<mi>q</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>1</mn>
</mrow>
<mrow>
<mi>n</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>1</mn>
</mrow>
<mi>n</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>10</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>2</mn>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mo>&lsqb;</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>2</mn>
</mrow>
<mn>1</mn>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>2</mn>
</mrow>
<mn>2</mn>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mn>....</mn>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>2</mn>
</mrow>
<mi>p</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>2</mn>
</mrow>
<mi>q</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mo>...</mo>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>2</mn>
</mrow>
<mrow>
<mi>n</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>2</mn>
</mrow>
<mi>n</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>11</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>3</mn>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mo>&lsqb;</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>3</mn>
</mrow>
<mn>1</mn>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>3</mn>
</mrow>
<mn>2</mn>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mn>....</mn>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>3</mn>
</mrow>
<mi>p</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>3</mn>
</mrow>
<mi>q</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>3</mn>
</mrow>
<mrow>
<mi>n</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>3</mn>
</mrow>
<mi>n</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>12</mn>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>4</mn>
</mrow>
</msub>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mo>&lsqb;</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>4</mn>
</mrow>
<mn>1</mn>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>4</mn>
</mrow>
<mn>2</mn>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mn>....</mn>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>4</mn>
</mrow>
<mi>p</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>4</mn>
</mrow>
<mi>q</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<mo>,</mo>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>4</mn>
</mrow>
<mrow>
<mi>n</mi>
<mo>-</mo>
<mn>1</mn>
</mrow>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>,</mo>
<mn>...</mn>
<msubsup>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mi>c</mi>
<mn>4</mn>
</mrow>
<mi>n</mi>
</msubsup>
<mrow>
<mo>(</mo>
<mi>t</mi>
<mo>)</mo>
</mrow>
<mo>&rsqb;</mo>
<mo>-</mo>
<mo>-</mo>
<mo>-</mo>
<mrow>
<mo>(</mo>
<mn>13</mn>
<mo>)</mo>
</mrow>
</mrow>
S4, the quality according to each particle of fitness function f (x) evaluations, it is determined that three role's states of each particle：Center role
State, exploits role's state, explores role's state, and definition is as follows respectively：
Center role's state --- using greediness as principle, it is defined as the optimal location that each particle search is arrived：xic(t+1)；
Exploitation role's state --- using nearest property as principle, the latest position that each particle search is arrived is defined as, i.e.,：
xil(t+1)={ xic1(t+1),xic2(t+1),xic3(t+1),xic4(t+1)} (14)
Exploration role's state --- using equally distributed randomness as principle, it is defined as each particle to be evenly distributed on search space
The position of random position：xig(t+1)；
The position of optimal particle in S5, record colony：xoc(t+1) S2, is returned, starts next search cycle, until in colony
The position stabilization that particle converges to optimal particle in optimal location, i.e. colony does not change to given accuracy.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710138048.2A CN107085742A (en) | 2017-03-09 | 2017-03-09 | A kind of intelligent optimization algorithm based on simple form neighborhood Yu many role's evolution strategies |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710138048.2A CN107085742A (en) | 2017-03-09 | 2017-03-09 | A kind of intelligent optimization algorithm based on simple form neighborhood Yu many role's evolution strategies |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107085742A true CN107085742A (en) | 2017-08-22 |
Family
ID=59614815
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710138048.2A Pending CN107085742A (en) | 2017-03-09 | 2017-03-09 | A kind of intelligent optimization algorithm based on simple form neighborhood Yu many role's evolution strategies |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107085742A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109165610A (en) * | 2018-08-31 | 2019-01-08 | 昆明理工大学 | A kind of Handwritten Digital Recognition detection method evolved based on simple form |
-
2017
- 2017-03-09 CN CN201710138048.2A patent/CN107085742A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109165610A (en) * | 2018-08-31 | 2019-01-08 | 昆明理工大学 | A kind of Handwritten Digital Recognition detection method evolved based on simple form |
| CN109165610B (en) * | 2018-08-31 | 2021-01-05 | 昆明理工大学 | Handwritten digit recognition and detection method based on simplex evolution |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Nguyen et al. | A survey on swarm intelligence approaches to feature selection in data mining | |
| Luo et al. | Species-based particle swarm optimizer enhanced by memory for dynamic optimization | |
| Hu et al. | Recent advances in particle swarm | |
| Vrugt et al. | Self-adaptive multimethod search for global optimization in real-parameter spaces | |
| CN112082552A (en) | Unmanned aerial vehicle flight path planning method based on improved hybrid particle swarm optimization algorithm | |
| CN110222885A (en) | The optimization method and system of the autonomous path planning of intelligent body in unmanned systems | |
| Sarkar et al. | Generating and blending game levels via quality-diversity in the latent space of a variational autoencoder | |
| WO2019154215A1 (en) | Robot running path generation method, computing device and storage medium | |
| Ducange et al. | Multi-objective evolutionary fuzzy systems | |
| CN111368936A (en) | Feature selection method based on improved SVM-RFE | |
| CN107085742A (en) | A kind of intelligent optimization algorithm based on simple form neighborhood Yu many role's evolution strategies | |
| CN113821025A (en) | Mobile robot path planning method for optimizing heuristic function through neural network | |
| Tian et al. | Robot global path planning using PSO algorithm based on the interaction mechanism between leaders and individuals | |
| CN112528556A (en) | Micro-electro-mechanical system design optimization method based on integrated model assisted social learning particle swarm algorithm | |
| Grillotti et al. | Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains | |
| CN116149334A (en) | Multi-node path planning method for electric tractor | |
| Zhou et al. | An X-architecture SMT algorithm based on competitive swarm optimizer | |
| CN113419532A (en) | Path planning ant colony algorithm parameter selection method based on uniform design | |
| Saboori et al. | Improving the K-means algorithm using improved downhill simplex search | |
| Wang | Intelligent Path Planning of Mobile Robot Based on Genetic Algorithm | |
| Huang et al. | ACR-Tree: Constructing R-Trees Using Deep Reinforcement Learning | |
| Cao et al. | Intrinsic motivation for deep deterministic policy gradient in multi-agent environments | |
| CN116128330B (en) | Air-ground unmanned system combat effectiveness evaluation method based on machine learning | |
| CN110046708A (en) | A kind of credit-graded approach based on unsupervised deep learning algorithm | |
| CN112417645B (en) | Mars detector brake capture multi-objective optimization method and system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170822 |