Self-adaptive differential evolution algorithm with discrete mutation control parameters

•In DMPSADE, control parameters and mutation strategies could be automatically adjusted.•We first proposed a new encoding for parameter control in DE algorithm.•Roulette wheel is used to implement the selection of mutation strategies. Generally, the optimization problem has different relationships (...

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Veröffentlicht in:	Expert systems with applications Jg. 42; H. 3; S. 1551 - 1572
Hauptverfasser:	Fan, Qinqin, Yan, Xuefeng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Amsterdam Elsevier Ltd 15.02.2015 Elsevier
Schlagworte:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Balancing Computer science; control theory; systems Control parameter adaptation Control system synthesis Control theory. Systems Differential evolution algorithm Discrete mutation parameters Evolution Evolutionary computation Exact sciences and technology Expert systems Mutation strategy adaptation Mutations Nonlinearity Optimization Strategy Theoretical computing Mutation strategy adaptation Differential evolution algorithm Control parameter adaptation Discrete mutation parameters Evolutionary computation Capability index Statistical analysis Balancing Adaptive algorithm Evolutionary algorithm Adaptability Control program Control synthesis Differential evolution Mutation Mathematical programming
ISSN:	0957-4174, 1873-6793
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Abstract	•In DMPSADE, control parameters and mutation strategies could be automatically adjusted.•We first proposed a new encoding for parameter control in DE algorithm.•Roulette wheel is used to implement the selection of mutation strategies. Generally, the optimization problem has different relationships (i.e., linear, approximately linear, non-linear, or highly non-linear) with different optimized variables. The choices of control parameters and mutation strategies would directly affect the performance of differential evolution (DE) algorithm in satisfying the evolution requirement of each optimized variable and balancing its exploitation and exploration capabilities. Therefore, a self-adaptive DE algorithm with discrete mutation control parameters (DMPSADE) is proposed. In DMPSADE, each variable of each individual has its own mutation control parameter, and each individual has its own crossover control parameter and mutation strategy. DMPSADE was compared with 8 state-of-the-art DE variants and 3 non-DE algorithms by using 25 benchmark functions. The statistical results indicate that the average performance of DMPSADE is better than those of all other competitors.
AbstractList	Generally, the optimization problem has different relationships (i.e., linear, approximately linear, non-linear, or highly non-linear) with different optimized variables. The choices of control parameters and mutation strategies would directly affect the performance of differential evolution (DE) algorithm in satisfying the evolution requirement of each optimized variable and balancing its exploitation and exploration capabilities. Therefore, a self-adaptive DE algorithm with discrete mutation control parameters (DMPSADE) is proposed. In DMPSADE, each variable of each individual has its own mutation control parameter, and each individual has its own crossover control parameter and mutation strategy. DMPSADE was compared with 8 state-of-the-art DE variants and 3 non-DE algorithms by using 25 benchmark functions. The statistical results indicate that the average performance of DMPSADE is better than those of all other competitors. •In DMPSADE, control parameters and mutation strategies could be automatically adjusted.•We first proposed a new encoding for parameter control in DE algorithm.•Roulette wheel is used to implement the selection of mutation strategies. Generally, the optimization problem has different relationships (i.e., linear, approximately linear, non-linear, or highly non-linear) with different optimized variables. The choices of control parameters and mutation strategies would directly affect the performance of differential evolution (DE) algorithm in satisfying the evolution requirement of each optimized variable and balancing its exploitation and exploration capabilities. Therefore, a self-adaptive DE algorithm with discrete mutation control parameters (DMPSADE) is proposed. In DMPSADE, each variable of each individual has its own mutation control parameter, and each individual has its own crossover control parameter and mutation strategy. DMPSADE was compared with 8 state-of-the-art DE variants and 3 non-DE algorithms by using 25 benchmark functions. The statistical results indicate that the average performance of DMPSADE is better than those of all other competitors.
Author	Fan, Qinqin Yan, Xuefeng
Author_xml	– sequence: 1 givenname: Qinqin surname: Fan fullname: Fan, Qinqin email: forever123fan@163.com – sequence: 2 givenname: Xuefeng surname: Yan fullname: Yan, Xuefeng email: xfyan@ecust.edu.cn
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Keywords	Mutation strategy adaptation Differential evolution algorithm Control parameter adaptation Discrete mutation parameters Evolutionary computation Capability index Statistical analysis Balancing Adaptive algorithm Evolutionary algorithm Adaptability Control program Control synthesis Differential evolution Mutation Mathematical programming
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Snippet	•In DMPSADE, control parameters and mutation strategies could be automatically adjusted.•We first proposed a new encoding for parameter control in DE... Generally, the optimization problem has different relationships (i.e., linear, approximately linear, non-linear, or highly non-linear) with different optimized...
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SubjectTerms	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Balancing Computer science; control theory; systems Control parameter adaptation Control system synthesis Control theory. Systems Differential evolution algorithm Discrete mutation parameters Evolution Evolutionary computation Exact sciences and technology Expert systems Mutation strategy adaptation Mutations Nonlinearity Optimization Strategy Theoretical computing
Title	Self-adaptive differential evolution algorithm with discrete mutation control parameters
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