CN114840141A - Artificial bee colony algorithm based on average cognitive strategy and with double-file storage - Google Patents
Artificial bee colony algorithm based on average cognitive strategy and with double-file storage Download PDFInfo
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Abstract
The invention relates to an artificial bee colony algorithm for double-file storage based on an average cognitive strategy, which belongs to the technical field of artificial intelligence and comprises the following steps: initializing algorithm parameters; randomly generating an initial population, and selecting individuals in the external archive and the individual archive by using non-dominated sorting; calculating an average cognitive position; hiring bees to search neighborhoods and update external files and individual files; selecting a honey source for gambling through a wheel disc in the bee following stage, performing neighborhood search on the honey source, and updating an external file and an individual file; if one food source is iterated through the maximum limit times, if the honey source is not updated, the hired bees are converted into detection bees, and a new honey source is searched to replace the original honey source; judging whether the evaluation times are more than or equal to Maxfes, and if so, outputting an optimal solution; otherwise, go to step four. The invention increases the dominant individual storage file on the variable space, fully utilizes the beneficial information generated in the population evolution process, and improves the evolution speed and the optimization precision of the algorithm.
Description
Technical Field
The invention relates to an artificial bee colony algorithm based on average cognitive strategy double-file storage, and belongs to the technical field of artificial intelligence.
Background
In order to obtain a more accurate solution when solving complex problems, a plurality of intelligent optimization algorithms based on bionics are proposed in succession, for example: artificial bee colony algorithm, fruit fly algorithm, genetic algorithm, whale algorithm, particle swarm algorithm and the like. Among these, the artificial bee colony algorithm was proposed in 2005 by karaboga.d et al, which was originally aimed at solving the single-target problem. A multi-target artificial bee colony algorithm is proposed by improvement of Reza Akbar and the like, and the requirement of a multi-target optimization problem at that time can be effectively met.
With the increasingly complex optimization problem in real production life, the artificial bee colony algorithm is widely concerned due to the advantages of simple structure, few parameters and the like, in recent years, scholars at home and abroad put forward various improved schemes for the bee colony algorithm to improve the artificial bee colony algorithm continuously, but the problems of poor local search capability and easy premature convergence still exist in the artificial bee colony algorithm, and further improvement is still needed.
Disclosure of Invention
The invention aims to provide an artificial bee colony algorithm based on average cognitive strategy and double-file storage, which is used for increasing the diversity of populations, improving the convergence precision and the evolution speed of the algorithm and balancing the problem that the overall development capability and the local search capability of the artificial bee colony algorithm are unbalanced.
In order to achieve the purpose, the invention adopts the technical scheme that:
an average cognition strategy-based artificial bee colony algorithm with double-file storage comprises the following steps:
the method comprises the following steps: initializing algorithm parameters, wherein the parameters comprise population quantity, external files, individual file size NP and maximum evaluation times Maxfes;
step two: randomly generating SN initial populations with D-dimensional variables, and selecting individuals in the external files and the individual files by using non-dominated sorting;
step three: calculating an average cognitive position;
step four: hiring bees to search neighborhoods and update external files and individual files;
step five: selecting a honey source by a roulette mode in the bee following stage, performing neighborhood search on the honey source, and then updating an external file and an individual file;
step six: if the honey source is not updated after the maximum limit times limit iterations of one food source, the hired bees are changed into detection bees, and a new honey source is searched to replace the original honey source;
step seven: judging whether the evaluation times are more than or equal to Maxfes, and if so, outputting an optimal solution; otherwise, go to step four.
The technical scheme of the invention is further improved as follows: the second step randomly generates SN initial populations with D-dimensional variables according to the formula (1),
x i,j ＝x min,j +rand(0,1)(x max,j -x min,j ) (1)
wherein i is 1,2, …, SN, j is 1,2, …, D, wherein each x is i Representing a D-dimensional vector, x max And x min Upper and lower bounds within the search space.
The technical scheme of the invention is further improved as follows: the formula for calculating the average cognitive position in the third step is as follows:
wherein c.p. is the average cognitive position, A.i. i Is the ith individual in the individual file, and n is the number of dominant individuals in the individual file.
The technical scheme of the invention is further improved as follows: the hiring bee in the fourth step searches for neighborhoods using equation (3):
in the formula, v i,j Is a neighborhood solution, elite i,j For randomly selected elite solutions in external archives, leader i,j The average cognitive position of the dominant individual in the individual profile,
updating the external file and the individual file according to equation (4),
in the formula, x m For solutions with minimum crowding distance, X is X m The solution generated by fusion with the solution on the left side of the same, Y being x m Solution, x, generated by fusion with its right solution l Is equal to x m Solution, x, to the nearest left r Is equal to x m The solution closest to the right.
The technical scheme of the invention is further improved as follows: the formula of the roulette betting mode following the bee stage in the fifth step is as follows:
in the formula, p i Probability of being selected for the ith honey source, fit i The ith honey source fitness value is SN, and the SN is the number of the honey sources.
The technical scheme of the invention is further improved as follows: and the step six is to search a new honey source to replace the original honey source through the formula (1).
Due to the adoption of the technical scheme, the invention has the following technical effects:
the invention provides a double-file storage strategy, on the basis of the elite solution files of the target space, the superior individual storage files of the variable space are added, the beneficial information generated in the population evolution process is fully utilized, and the evolution speed and the optimization precision of the algorithm are improved.
The invention adopts an average cognitive strategy, introduces individuals in individual files into a search formula, improves the diversity of population and avoids the phenomenon of premature convergence of the algorithm.
According to the invention, through an improved external archive maintenance mechanism, the phenomenon of environment diversity loss in external archives is avoided, the diversity of the population is ensured, and the optimization efficiency of the algorithm is further improved.
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FIG. 1 is a schematic flow diagram of the present invention.
Detailed Description
The invention is described in further detail below with reference to the following figures and specific embodiments:
an artificial bee colony algorithm (ATMABC) based on average cognition strategy and double-file storage can achieve the purpose of searching the most value of a certain problem, and the performance of the ATMABC is tested by optimizing the simulation of the minimization problem of a UF series test function through MATLAB (commercial mathematical software, high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis and numerical computation), and compared with the MOABC and NSGA II algorithms. The test function characteristics are shown in table 1.
TABLE 1UF series test function characteristics
An average cognitive policy based dual-archive stored artificial bee colony algorithm (atmabs), as shown in fig. 1, comprises the following steps:
the method comprises the following steps: initializing algorithm parameters: population number, external file and individual file size NP, and maximum evaluation times Maxfes;
step two: SN initial populations with D-dimensional variables are randomly generated according to the formula (1), and individuals in the external archive and the individual archive are selected by using non-dominated sorting:
x i,j ＝x min,j +rand(0,1)(x max,j -x min,j ) (1)
wherein i is 1,2, …, SN, j is 1,2, …, D, wherein each x is i Representing a D-dimensional vector, x max And x min Upper and lower bounds within the search space.
Step three: the average cognitive position is calculated according to equation (2):
wherein c.p. is the average cognitive position, A.i. i Is the ith individual in the individual file, and n is the number of dominant individuals in the individual file.
Step four: the hiring bee searches for neighborhoods using equation (3):
in the formula, v i,j Is a neighborhood solution, elite i,j For randomly selected elite solutions in external archives, leader i,j The average cognitive position of the dominant individual in the individual profile,
updating the external file and the individual file according to equation (4),
in the formula, x m For solutions with minimum crowding distance, X is X m The solution generated by fusion with the solution on the left side of the same, Y being x m Solution, x, generated by fusion with its right solution l Is equal to x m Solution, x, to the nearest left r Is equal to x m The solution closest to the right.
Step five: selecting honey source by roulette in formula (5) following bee stage, performing neighborhood search on honey source by formula (3), updating external file and individual file according to formula (4),
in the formula, p i Probability of being selected for the ith honey source, fit i The ith honey source fitness value is SN, and the SN is the number of the honey sources.
Step six: if a food source is not updated after the maximum limit times limit iterations, the employed bees are changed into detection bees, and a new honey source is searched for replacing the original honey source through the formula (1).
Step seven: judging whether the evaluation times are more than or equal to Maxfes, and if so, outputting an optimal solution; otherwise, go to step four.
And (3) carrying out optimization solution on UF series test functions by adopting three algorithms of ATMABC, MOABC and NSGA II under the same condition, independently operating each function for 30 times, recording an IGD mean value (mean) and a variance (std), and using a star to extract an optimal value obtained by the algorithm under the agreement test function. Table 2 compares the IGD index simulation results for the three algorithms.
TABLE 2 IGD index simulation result comparison
As can be seen from table 2: nine of the ten test functions in the UF series of the ATMABC algorithm obtain excellent IGD values, which can show that the ATMABC algorithm can obtain a good solution set on the UF series of test functions. In the dual-target problem (UF1-UF7), the ATMABC algorithm does not obtain an optimal value on a UF3 test function, but the optimal value and the optimal value are on the same order of magnitude, and the solution set also has a good convergence effect. In both the UF6 and UF7 test functions, a better variance value was obtained, but a better average value was not obtained, because the convergence effect of the algorithm under the two test functions is not obvious and needs to be further improved. Under the three-target test function, the optimum average value was not obtained only on the UF9 test. The overall effect is that the ATMABC algorithm is superior to other comparison algorithms, which shows that the solution set obtained by the algorithm has good distributivity and convergence, and can still obtain better results aiming at the three-target problem, and shows that the algorithm has the capability of processing complex problems.
The invention adopts a double-file storage mode, and adds an individual file for storing the dominant individual in the variable space on the basis of storing the elite solution of the target space by the original external file so as to fully utilize the favorable information in the population evolution process, play a role of fully guiding the population evolution direction and improve the evolution efficiency and the search precision of the algorithm. Because the artificial bee colony algorithm has the problem that a global development strategy and a local search strategy are unbalanced, the population guidance is improved, and meanwhile, the diversity of the population is also ensured, new individuals are introduced in the population evolution process by utilizing an average cognition strategy, and the population diversity is improved while the population evolution speed is also ensured.
Although specific embodiments of the invention have been described above, it will be understood by those skilled in the art that the specific embodiments described are illustrative only and are not limiting upon the scope of the invention, and that equivalent modifications and variations can be made by those skilled in the art without departing from the spirit of the invention, which is to be limited only by the appended claims.
Claims (6)
1. An average cognition strategy-based artificial bee colony algorithm with double-file storage is characterized by comprising the following steps:
the method comprises the following steps: initializing algorithm parameters, wherein the parameters comprise population quantity, external files, individual file size NP and maximum evaluation times Maxfes;
step two: randomly generating SN initial populations with D-dimensional variables, and selecting individuals in the external files and the individual files by using non-dominated sorting;
step three: calculating an average cognitive position;
step four: hiring bees to search neighborhoods and update external files and individual files;
step five: selecting a honey source by a roulette mode in the bee following stage, performing neighborhood search on the honey source, and then updating an external file and an individual file;
step six: if the honey source is not updated after the maximum limit times limit iterations of one food source, the hired bees are changed into detection bees, and a new honey source is searched to replace the original honey source;
step seven: judging whether the evaluation times are more than or equal to Maxfes, and if so, outputting an optimal solution; otherwise, go to step four.
2. The average cognitive policy based dual-file storage artificial bee colony algorithm according to claim 1, characterized in that: the second step randomly generates SN initial populations with D-dimensional variables according to the formula (1),
x i,j ＝x min,j +rand(0,1)(x max,j -x min,j ) (1)
wherein i is 1,2, …, SN, j is 1,2, …, D, wherein each x is i Representing a D-dimensional vector, x max And x min Upper and lower bounds within the search space.
3. The average cognitive policy based dual-file storage artificial bee colony algorithm according to claim 1, characterized in that: the formula for calculating the average cognitive position in the third step is as follows:
wherein c.p. is the average cognitive position, A.i. i Is the ith individual in the individual file, and n is the number of dominant individuals in the individual file.
4. The average cognitive policy based dual-file storage artificial bee colony algorithm according to claim 1, characterized in that: the hiring bee in the fourth step searches for neighborhoods using equation (3):
in the formula, v i,j Is a neighborhood solution, elite i,j For randomly selected elite solutions in external archives, leader i,j For the dominant individual in the individual fileIs determined based on the average of the learned positions of,
updating the external file and the individual file according to equation (4),
in the formula, x m For solutions with minimum crowding distance, X is X m The solution generated by fusion with the solution on the left side of the same, Y being x m Solution, x, generated by fusion with its right solution l Is equal to x m Solution, x, to the nearest left r Is equal to x m The solution closest to the right.
5. The average cognitive policy based dual-file storage artificial bee colony algorithm according to claim 4, characterized in that: the formula of the roulette betting mode following the bee stage in the fifth step is as follows:
in the formula, p i Probability of being selected for the ith honey source, fit i The ith honey source fitness value is SN, and the SN is the number of the honey sources.
6. The average cognitive policy based dual-file storage artificial bee colony algorithm according to claim 2, characterized in that: and sixthly, searching a new honey source to replace the original honey source through the formula (1).
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| CN116432689A (en) * | 2023-04-17 | 2023-07-14 | 广州菲利斯太阳能科技有限公司 | Virtual synchronous machine parameter quantization method based on improved quantum artificial bee colony algorithm |
| CN117455222A (en) * | 2023-12-26 | 2024-01-26 | 聊城大学 | Solving method based on distributed heterogeneous flow shop group scheduling problem |
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| CN116432689A (en) * | 2023-04-17 | 2023-07-14 | 广州菲利斯太阳能科技有限公司 | Virtual synchronous machine parameter quantization method based on improved quantum artificial bee colony algorithm |
| CN117455222A (en) * | 2023-12-26 | 2024-01-26 | 聊城大学 | Solving method based on distributed heterogeneous flow shop group scheduling problem |
| CN117455222B (en) * | 2023-12-26 | 2024-03-05 | 聊城大学 | Solving method based on distributed heterogeneous flow shop group scheduling problem |
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