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
Online-Zugang:Volltext
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Zusammenfassung:•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.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2014.09.046