CN109033520A - A kind of system Multipurpose Optimal Method based on random enhancing harmony algorithm - Google Patents
A kind of system Multipurpose Optimal Method based on random enhancing harmony algorithm Download PDFInfo
- Publication number
- CN109033520A CN109033520A CN201810658475.8A CN201810658475A CN109033520A CN 109033520 A CN109033520 A CN 109033520A CN 201810658475 A CN201810658475 A CN 201810658475A CN 109033520 A CN109033520 A CN 109033520A
- Authority
- CN
- China
- Prior art keywords
- harmony
- new
- variable
- random
- optimized
- 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
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computing arrangements based on specific mathematical models
- G06N7/01—Probabilistic graphical models, e.g. probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
Abstract
The present invention discloses a kind of system Multipurpose Optimal Method based on random enhancing harmony algorithm, and this method comprises the following steps: S1, the mathematical model for determining system to be optimized；S2, the multiple optimization aims for determining system to be optimized；S3, optimization system progress multiple-objection optimization is treated based on random enhancing harmony algorithm, ability of searching optimum of the present invention is strong, and convergence rate is good, can effectively realize the multiple-objection optimization of system to be optimized.
Description
Technical field
The present invention relates to Intelligent Optimization Technique fields.It is based on random enhancing harmony algorithm more particularly, to a kind of
System Multipurpose Optimal Method.
Background technique
Optimize the subscience important as one, is constantly subjected to the extensive attention of people, it produces multiple subjects
Significant impact, and obtain rapid promotion and application in many engineering fields, have become much work in different field can not or
Scarce tool.
Optimization algorithm is based on certain thought and mechanism, and the solution for meeting user's requirement is obtained by certain approach.Now,
The theory for solving the various optimization problems such as linear programming, Non-Linear Programming and stochastic programming, geometric programming, integer programming is ground
Study carefully and quickly grow, new method continuously emerges, and practical application is increasingly extensive.
Intelligent optimization algorithm generally refers to certain of the biosystem and optimization process using nature as emerging algorithm
A little similitudes and the optimization algorithm gradually to grow up, such as genetic algorithm, particle swarm algorithm, ant group algorithm.These algorithms are certainly
The search mechanisms of body determine the ability of searching optimum and local search ability of algorithm, to determine the advantage and disadvantage of algorithm.
Lance between big, computationally intensive, algorithm search the randomness in multi-objective optimization question search space and search speed
Shield largely affects algorithm performance.The ability of searching optimum and convergence rate for how weighing algorithm are that multiple target is excellent
Change problem have in face of the problem of, but existing most of intelligent optimization algorithms are due to the defect of itself search mechanisms, then seek
In excellent process or ability of searching optimum is strong, and convergence rate is slow；Fast convergence rate, and ability of searching optimum is poor.
For this purpose, the present invention fully considers random search and the respective advantage and disadvantage of harmonic search algorithm, provide it is a kind of based on
Machine enhances the system Multipurpose Optimal Method of harmony algorithm, and this method can overcome the defect of previous algorithm, has global search
Ability is strong, combines good convergence rate, and the multiple-objection optimization of high performance-price ratio may be implemented.
Summary of the invention
The purpose of the present invention is to provide a kind of system Multipurpose Optimal Methods based on random enhancing harmony algorithm, solve
The problem of algorithm ability of searching optimum is contradicted with convergence rate realizes the multiple-objection optimization of system to be optimized.
The present invention adopts the following technical solutions:
The invention discloses a kind of system Multipurpose Optimal Methods based on random enhancing harmony algorithm, and this method includes such as
Lower step:
Determine the mathematical model of system to be optimized；
Determine multiple optimization aims of system to be optimized；
Optimization system, which is treated, based on random enhancing harmony algorithm carries out multiple-objection optimization.
Preferably,
The mathematical model of the system to be optimized is the fitness function of the system to be optimized.
Preferably, the system to be optimized includes source module, operation control module and loading module.
Preferably, multiple optimization aims of the system to be optimized include economy objectives, reliability objectives and the feature of environmental protection
Target.
Preferably, the economy objectives include system Construction cost, maintenance cost, use cost and maintenance cost.
Preferably, the reliability objectives include system stability, system failure rate and system vulnerability to jamming.
Preferably, the feature of environmental protection target includes that systemic adverse gas emission index, renewable rate and renewable energy use
Rate.
Preferably, the mathematical model of determination system to be optimized specifically includes:
Determine the size HMS and sound memory consideration rate HMCR, fine tuning disturbance preference ξ, tone fine tuning bandwidth of harmony data base
Bw, the step factor λ of random walk function and search range [LB, UB]；Wherein, UB and LB is the bound of search range；
Generate HMS group solution vector X at random in described search range [LB, UB]i, wherein i=1,2 ..., HMS, and utilize
Fitness calculating method generates the corresponding fitness function value f (X of every group of solution vectori)。
Preferably, it is described optimization system treated based on random enhancing harmony algorithm carry out multiple-objection optimization specifically include:
Solution vector XiAnd its fitness function value f (Xi) harmony data base HM is collectively formed；
Solution vector Xi(j) generation formula are as follows:
Xi(j)=LB (j)+(UB (j)-LB (j)) × rand ()
Harmony data base HM are as follows:
In formula, i=1,2 ..., HMS, j=1,2 ..., n, n be harmony variable maximal dimension, rand () be [0,1] it
Between random number；
Random number r is generated between [0,1]；
If r is less than HMCR,
Then variable X is randomly selected from harmony data base HMi, with probability ξ to variable XiIt is disturbed to supremum direction, with general
Rate 1- ξ is to variable XiIt is disturbed to infimum direction, generates new harmony variable Xi_new
Wherein, Xi_new∈ [LB, UB], rand are the random numbers between [0,1], and bw is tone fine tuning bandwidth, ξ be preference because
Son；
If r is more than or equal to HMCR,
Then generate harmony variable X at random in search rangei；
Based on random enhancing new mechanism harmony variable Xi, update the formula of harmony variable are as follows:
Xi_new=Xi+λ·Rand(d)
In formula, λ is step factor, and Rand (d) is random search path, and d is the dimension of harmony variable, Xi_newConstraint condition
For Xi_new∈[LB,UB]；
If newly generated Xi_newBetter than the most bad harmony variable in harmony library, then with new harmony variable Xi_newReplacement and
Most bad harmony variable in sound library, if newly generated Xi_newMost bad harmony variable not better than in harmony library, judges whether to meet
Termination condition, if satisfied, then terminate output as a result, if not satisfied, again between [0,1] generate random number r, carry out a new round
Search.
Beneficial effects of the present invention are as follows:
Compared with prior art, the invention has the following advantages that
First, harmonic search algorithm is combined with random enhancing mechanism, the ability of searching optimum of algorithm is greatly strengthened,
So that algorithm is easier to jump out local extremum.
Second, algorithm introduces disturbance preference heterogeneity, so that algorithm avoids a large amount of unnecessary search, to change significantly
It has been apt to convergence speed of the algorithm.
Third, algorithm structure is simple, implementation steps are clear, are easy to Project Realization.
Detailed description of the invention
Specific embodiments of the present invention will be described in further detail with reference to the accompanying drawing.
Fig. 1 shows a kind of system Multipurpose Optimal Method one specific implementation based on random enhancing harmony algorithm of the present invention
The flow chart of example.
Fig. 2 shows a kind of system Multipurpose Optimal Method one specific implementations based on random enhancing harmony algorithm of the present invention
The algorithm flow chart of example.
Specific embodiment
In order to illustrate more clearly of the present invention, the present invention is done further below with reference to preferred embodiments and drawings
It is bright.Similar component is indicated in attached drawing with identical appended drawing reference.It will be appreciated by those skilled in the art that institute is specific below
The content of description is illustrative and be not restrictive, and should not be limited the scope of the invention with this.
As shown in Figure 1, the invention discloses a kind of system Multipurpose Optimal Methods based on random enhancing harmony algorithm
One specific embodiment, in the present embodiment, which comprises
S1, the mathematical model for determining system to be optimized.The mathematical model is the fitness function f (x) of system to be optimized, to
The optimization aim of optimization system is, under given constraint and boundary condition, by seeking optimal solution vector Xbest, obtain minimum suitable
Response function f (Xbest)。
The mathematical model of the determination system to be optimized specifically includes:
Determine the size HMS and sound memory consideration rate HMCR, fine tuning disturbance preference ξ, tone fine tuning bandwidth of harmony data base
Bw, the step factor λ of random walk function and search range [LB, UB], UB and LB are the bounds of search range；
Generate HMS group solution vector X at random in described search range [LB, UB]i, i=1,2 ..., HMS, and utilize adaptation
Calculating method is spent, the corresponding fitness function value f (X of every group of solution vector is generatedi)。
S2, the multiple optimization aims for determining system to be optimized, the system to be optimized can be by source module, operation control module
With multiple module compositions such as loading module, corresponding multiple optimization aims.In the present embodiment, for most of Engineering to be optimized
System, these optimization aims can be divided into economy, reliability and feature of environmental protection three classes.Economy objectives include system Construction at
Sheet, maintenance cost, use cost and maintenance cost etc., reliability objectives include that system stability, system failure rate and system are anti-
Immunity etc., feature of environmental protection target include systemic adverse gas emission index, renewable rate and renewable energy utilization rate etc..
S3, optimization system progress multiple-objection optimization is treated based on random enhancing harmony algorithm.
Specifically, solution vector XiAnd its fitness function value f (Xi) harmony data base HM is collectively formed；
Solution vector Xi(j) generation formula are as follows:
Xi(j)=LB (j)+(UB (j)-LB (j)) × rand ()
Harmony data base HM are as follows:
In formula, i=1,2 ..., HMS, j=1,2 ..., n, n be harmony variable maximal dimension, rand () be [0,1] it
Between random number；
Random number r is generated between [0,1]；
If r is less than HMCR,
Then variable X is randomly selected from harmony data base HMi, with probability ξ to variable XiIt is disturbed to supremum direction, with general
Rate 1- ξ is to variable XiIt is disturbed to infimum direction, generates new harmony variable Xi_new
Wherein, Xi_new∈ [LB, UB], rand are the random numbers between [0,1], and bw is tone fine tuning bandwidth, ξ be preference because
Son；
If r is more than or equal to HMCR,
Then generate harmony variable X at random in search rangei；
Based on random enhancing new mechanism harmony variable Xi, update the formula of harmony variable are as follows:
Xi_new=Xi+λ·Rand(d)
In formula, λ is step factor, and Rand (d) is random search path, and d is the dimension of harmony variable, Xi_newConstraint condition
For Xi_new∈[LB,UB]；
If newly generated Xi_newBetter than the most bad harmony variable in harmony library, then with new harmony variable Xi_newReplacement and
Most bad harmony variable in sound library, if newly generated Xi_newMost bad harmony variable not better than in harmony library, judges whether to meet
Termination condition, if satisfied, then terminating output as a result, if not satisfied, generating random number r between [0,1] again.
As shown in Fig. 2, giving an algorithm flow schematic diagram of the system Multipurpose Optimal Method of the present embodiment, specifically
Step are as follows:
Step1: algorithm starts, initialization；
The basic parameter of random enhancing harmony algorithm, size HMS and sound memory consideration rate including harmony data base are set
HMCR, fine tuning disturbance preference ξ, tone finely tune bandwidth bw, the step factor λ of random walk function, search range [LB, UB]；Its
Middle LB and UB is the bound of search range.
Step2: HMS group solution vector X is generated at random in search rangei, and using well known fitness calculating method (as weighed
Reassignment method), generate the corresponding fitness function value f (X of every group of solution vectori)；
Step3: solution vector XiAnd its fitness function value f (Xi) harmony data base HM is collectively formed, specific formula is as follows:
Solution vector generates formula are as follows:
Xi(j)=LB (j)+(UB (j)-LB (j)) × rand ()
Harmony data base HM expression formula are as follows:
I=1,2 in formula ..., HMS；J=1,2 ..., n, wherein n indicate harmony variable maximal dimension, rand () be [0,
1] random number between；
Step4: random number r is generated between [0,1]；
Step5: judging whether r is less than HMCR, if r is less than HMCR, turns Step 6-1；If r is more than or equal to HMCR, turn
Step 7-1；
Step6-1: variable X is randomly selected from harmony data base HMi；
Step6-2: with probability ξ to variable XiIt is disturbed to supremum direction, with probability 1- ξ to variable XiTo infimum side
To disturbance, probability ξ here is known as " preference heterogeneity ", when its value range is between [0,0.5], show with greater probability to
The search of the direction LB shows to search for greater probability to the direction UB when its value range is between [0.5,1].It generates as a result, new
Harmony variable Xi_newIt is as follows；
X in above formulai_newConstraint condition is Xi_new∈ [LB, UB], rand are the random numbers between [0,1], and bw is that tone is micro-
Bandwidth is adjusted, ξ is preference heterogeneity；
Step6-3: judge newly generated Xi_newIt is whether better than the most bad harmony variable in harmony library, i.e., newly generated
Xi_newFitness function value f (Xi_new) whether it is less than the maximum adaptation degree functional value f (X in harmony libraryworst)；If so, turning
Step 6-4, if it is not, then turning Step 8；
Step6-4: harmony data base HM is updated, i.e., with new harmony variable Xi_newThe most bad harmony replaced in harmony library becomes
Amount, then turns Step 8；
Step7-1: harmony variable X is generated in search range at randomi；And m=1 is enabled, m here is counting variable；
Step7-2: based on random enhancing new mechanism harmony variable Xi, it may be assumed that
Flight update harmony variable, which is tieed up, based on Lay obtains Xi_new, using random walk mechanism to current harmony solution vector Xi
It is updated, more new formula are as follows:
Xi_new=Xi+λ·Rand(d)
In formula: XiIt is harmony solution vector, λ is step factor, and Rand (d) is random search path, and d is the dimension of harmony variable
Degree, Xi_newConstraint condition is Xi_new∈[LB,UB]；
The expression formula in random search path are as follows:
In formula: u, v obey standardized normal distribution, and β is the variable of control distribution, and value range is between [0,2], φ table
Show variance, the expression is as follows:
In formula: Γ indicates gamma function.
Step7-3: judge newly generated harmony variable Xi_newIt is whether better than the most bad harmony variable in harmony library, i.e., new to produce
Raw Xi_newFitness function value f (Xi_new) whether it is less than the maximum adaptation degree functional value f (X in harmony libraryworst)；If so,
Then turn Step 7-4；If it is not, then turning Step 7-5；
Step7-4: harmony data base HM is updated, i.e., with new harmony variable Xi_newThe most bad harmony replaced in harmony library becomes
Amount, then turns Step 8；
Step7-5: m=m+1 is enabled；
Step7-6: judge m whether be less than k (k is constant, usually it is desirable 100), if so, turning Step7-2, if it is not, then
Turn Step 4；
Step8: judging whether to meet termination condition, if satisfied, then turning Step9, if not satisfied, turning Step4；
Step9: optimum results, the i.e. the smallest harmony variable X of fitness function value in harmony data base HM are exportedbest；
Step10: terminate algorithm.
By the above method, in system solution space to be optimized, the random harmony algorithm search that enhances has arrived optimal solution vector
Xbest, guarantee fitness function f (x) minimum to get f (X is arrivedbest)。
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention may be used also on the basis of the above description for those of ordinary skill in the art
To make other variations or changes in different ways, all embodiments can not be exhaustive here, it is all to belong to this hair
The obvious changes or variations that bright technical solution is extended out are still in the scope of protection of the present invention.
Claims (9)
1. a kind of system Multipurpose Optimal Method based on random enhancing harmony algorithm, which is characterized in that this method includes as follows
Step:
Determine the mathematical model of system to be optimized；
Determine multiple optimization aims of system to be optimized；
Optimization system, which is treated, based on random enhancing harmony algorithm carries out multiple-objection optimization.
2. the method according to claim 1, wherein
The mathematical model of the system to be optimized is the fitness function of the system to be optimized.
3. the method according to claim 1, wherein the system to be optimized includes source module, operation control mould
Block and loading module.
4. the method according to claim 1, wherein multiple optimization aims of the system to be optimized include economy
Property target, reliability objectives and feature of environmental protection target.
5. according to the method described in claim 4, it is characterized in that, the economy objectives include system Construction cost, maintenance
Cost, use cost and maintenance cost.
6. according to the method described in claim 4, it is characterized in that, the reliability objectives include system stability, system event
Barrier rate and system vulnerability to jamming.
7. according to the method described in claim 4, it is characterized in that, the feature of environmental protection target includes the discharge of systemic adverse gas
Rate, renewable rate and renewable energy utilization rate.
8. the method according to claim 1, wherein the mathematical model of determination system to be optimized is specifically wrapped
It includes:
Determine harmony data base size HMS and sound memory consideration rate HMCR, fine tuning disturbance preference ξ, tone fine tuning bandwidth bw,
The step factor λ of random walk function and search range [LB, UB]；Wherein, UB and LB is the bound of search range；
Generate HMS group solution vector X at random in described search range [LB, UB]i, wherein i=1,2 ..., HMS, and utilization adaptation
Calculating method is spent, the corresponding fitness function value f (X of every group of solution vector is generatedi)。
9. according to the method described in claim 8, it is characterized in that, described treat optimization system based on random enhancing harmony algorithm
Multiple-objection optimization is carried out to specifically include:
Solution vector XiAnd its fitness function value f (Xi) harmony data base HM is collectively formed；
Solution vector Xi(j) generation formula are as follows:
Xi(j)=LB (j)+(UB (j)-LB (j)) × rand ()
Harmony data base HM are as follows:
In formula, i=1,2 ..., HMS, j=1,2 ..., n, n are the maximal dimension of harmony variable, and rand () is between [0,1]
Random number；
Random number r is generated between [0,1]；
If r is less than HMCR,
Then variable X is randomly selected from harmony data base HMi, with probability ξ to variable XiIt is disturbed to supremum direction, with probability 1- ξ
To variable XiIt is disturbed to infimum direction, generates new harmony variable Xi_new
Wherein, Xi_new∈ [LB, UB], rand are the random numbers between [0,1], and bw is tone fine tuning bandwidth, and ξ is preference heterogeneity；
If r is more than or equal to HMCR,
Then generate harmony variable X at random in search rangei；
Based on random enhancing new mechanism harmony variable Xi, update the formula of harmony variable are as follows:
Xi_new=Xi+λ·Rand(d)
In formula, λ is step factor, and Rand (d) is random search path, and d is the dimension of harmony variable, Xi_newConstraint condition is
Xi_new∈[LB,UB]；
If newly generated Xi_newBetter than the most bad harmony variable in harmony library, then with new harmony variable Xi_newIt replaces in harmony library
Most bad harmony variable, if newly generated Xi_newMost bad harmony variable not better than in harmony library, judging whether to meet terminates item
Part, if satisfied, then terminate output as a result, if not satisfied, again between [0,1] generate random number r, carry out searching for a new round
Rope.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810658475.8A CN109033520A (en) | 2018-06-25 | 2018-06-25 | A kind of system Multipurpose Optimal Method based on random enhancing harmony algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810658475.8A CN109033520A (en) | 2018-06-25 | 2018-06-25 | A kind of system Multipurpose Optimal Method based on random enhancing harmony algorithm |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109033520A true CN109033520A (en) | 2018-12-18 |
Family
ID=64610684
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810658475.8A Pending CN109033520A (en) | 2018-06-25 | 2018-06-25 | A kind of system Multipurpose Optimal Method based on random enhancing harmony algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109033520A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110135094A (en) * | 2019-05-22 | 2019-08-16 | 长沙理工大学 | A kind of virtual plant Optimization Scheduling based on shrink space harmony algorithm |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102098687A (en) * | 2011-03-02 | 2011-06-15 | 上海大学 | Multi-object optimized deployment method for industrial wireless sensor network |
| CN103631758A (en) * | 2013-11-21 | 2014-03-12 | 陕西理工学院 | Method for solving non-linear programming and absolute value equation through improved harmony search algorithm |
| CN103809458A (en) * | 2014-03-04 | 2014-05-21 | 福州大学 | Magneto-rheological damping control method based on improved leapfrogging algorithm |
| US20170068890A1 (en) * | 2015-09-08 | 2017-03-09 | The Aerospace Corporation | Systems and Methods for Multi-Objective Optimizations with Live Updates |
| CN107015112A (en) * | 2017-04-10 | 2017-08-04 | 福州大学 | A kind of distribution line failure Section Location based on improvement harmony algorithm |
| CN108764508A (en) * | 2018-03-06 | 2018-11-06 | 北京交通大学 | A kind of Fitness analysis method of multiple-objection optimization |
-
2018
- 2018-06-25 CN CN201810658475.8A patent/CN109033520A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102098687A (en) * | 2011-03-02 | 2011-06-15 | 上海大学 | Multi-object optimized deployment method for industrial wireless sensor network |
| CN103631758A (en) * | 2013-11-21 | 2014-03-12 | 陕西理工学院 | Method for solving non-linear programming and absolute value equation through improved harmony search algorithm |
| CN103809458A (en) * | 2014-03-04 | 2014-05-21 | 福州大学 | Magneto-rheological damping control method based on improved leapfrogging algorithm |
| US20170068890A1 (en) * | 2015-09-08 | 2017-03-09 | The Aerospace Corporation | Systems and Methods for Multi-Objective Optimizations with Live Updates |
| CN107015112A (en) * | 2017-04-10 | 2017-08-04 | 福州大学 | A kind of distribution line failure Section Location based on improvement harmony algorithm |
| CN108764508A (en) * | 2018-03-06 | 2018-11-06 | 北京交通大学 | A kind of Fitness analysis method of multiple-objection optimization |
Non-Patent Citations (1)
| Title |
|---|
| 李荣喜: "基于莱维和声搜索算法的微网系统容量多目标优化研究", 《中国优秀硕士论文全文数据库工程科技II辑》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110135094A (en) * | 2019-05-22 | 2019-08-16 | 长沙理工大学 | A kind of virtual plant Optimization Scheduling based on shrink space harmony algorithm |
| CN110135094B (en) * | 2019-05-22 | 2023-05-23 | 长沙理工大学 | Virtual power plant optimal scheduling method based on contraction space and harmony algorithm |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN103955864B (en) | Based on the electric system multiple target differentiation planing method for improving harmonic search algorithm | |
| Chen et al. | Harmony search algorithm with dynamic control parameters | |
| CN104936186B (en) | Cognitive radio network spectrum allocation method based on cuckoo searching algorithm | |
| Tvrdík et al. | A simple framework for constrained problems with application of L-SHADE44 and IDE | |
| Sirjani et al. | Heuristic optimization techniques to determine optimal capacitor placement and sizing in radial distribution networks: a comprehensive review | |
| CN109066710A (en) | A kind of multi-objective reactive optimization method, apparatus, computer equipment and storage medium | |
| CN107203687A (en) | Absorption tower sweetening process multiple target cooperative intelligent optimal control method | |
| CN108830408A (en) | A kind of equipment support task multi-objective planning method that oriented mission is resource matched | |
| Liu et al. | Resource management of heterogeneous cellular networks with hybrid energy supplies: A multi-objective optimization approach | |
| CN112803434A (en) | Reactive power optimization method, device, equipment and storage medium for active power distribution network | |
| CN114741955A (en) | Multi-objective optimization task scheduling method based on security cloud | |
| Xu et al. | Distance-aware edge user allocation with QoE optimization | |
| Wang et al. | Joint service caching, resource allocation and computation offloading in three-tier cooperative mobile edge computing system | |
| CN109033520A (en) | A kind of system Multipurpose Optimal Method based on random enhancing harmony algorithm | |
| Fan et al. | Joint DNN partition and resource allocation for task offloading in edge-cloud-assisted IoT environments | |
| Tang et al. | Nonconvex optimization for power control in wireless CDMA networks | |
| CN108985639B (en) | Electric power system comprehensive decision method based on double-layer fuzzy optimization | |
| Gong et al. | Multi-objective particle swarm optimization based on minimal particle angle | |
| CN115499876A (en) | Computing unloading strategy based on DQN algorithm under MSDE scene | |
| CN108494520A (en) | Distributed intelligence interference system determines power formula jamming resource method for optimizing scheduling | |
| Sharma et al. | Differential evolution aided adaptive resource allocation in OFDMA systems with proportional rate constraints | |
| CN112613654A (en) | Comprehensive energy system flexibility evaluation method based on multi-type energy storage | |
| CN114254545A (en) | Parallel connection refrigerator system load control optimization method, system, equipment and medium | |
| Suganthan et al. | Adaptation methods in differential evolution: A review | |
| Teng et al. | Application research of chaotic binary particle swarm optimization algorithm in dynamic spectrum allocation |
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 |
Application publication date: 20181218 |
|
| RJ01 | Rejection of invention patent application after publication |