Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations

Differential evolution (DE) algorithm has been shown to be a very effective and efficient approach for solving global numerical optimization problems, which attracts a great attention of scientific researchers. Generally, most of DE algorithms only evolve one population by using certain kind of DE o...

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Vydané v:	Computers & operations research Ročník 67; s. 155 - 173
Hlavní autori:	Cui, Laizhong, Li, Genghui, Lin, Qiuzhen, Chen, Jianyong, Lu, Nan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Elsevier Ltd 01.03.2016 Pergamon Press Inc
Predmet:	Adaptive algorithms Adaptive parameter control Algorithms Benchmarks Differential evolution Evolutionary algorithms Genetic algorithms Global optimization Mathematical analysis Mathematical models Mathematical problems Multiple sub-populations Optimization Optimization techniques Replacement strategy Strategy Studies Vector space Vectors (mathematics) Differential evolution Multiple sub-populations Global optimization Adaptive parameter control Replacement strategy
ISSN:	0305-0548, 1873-765X, 0305-0548
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Abstract	Differential evolution (DE) algorithm has been shown to be a very effective and efficient approach for solving global numerical optimization problems, which attracts a great attention of scientific researchers. Generally, most of DE algorithms only evolve one population by using certain kind of DE operators. However, as observed in nature, the working efficiency can be improved by using the concept of work specialization, in which the entire group should be divided into several sub-groups that are responsible for different tasks according to their capabilities. Inspired by this phenomenon, a novel adaptive multiple sub-populations based DE algorithm is designed in this paper, named MPADE, in which the parent population is split into three sub-populations based on the fitness values and then three novel DE strategies are respectively performed to take on the responsibility for either exploitation or exploration. Furthermore, a simple yet effective adaptive approach is designed for parameter adjustment in the three DE strategies and a replacement strategy is put forward to fully exploit the useful information from the trial vectors and target vectors, which enhance the optimization performance. In order to validate the effectiveness of MPADE, it is tested on 55 benchmark functions and 15 real world problems. When compared with other DE variants, MPADE performs better in most of benchmark problems and real-world problems. Moreover, the impacts of the MPADE components and their parameter sensitivity are also analyzed experimentally. •Three novel mutation strategies are run in three sub-populations respectively.•A novel adaptive strategy is presented to tune the systemic parameters.•A simple replacement strategy is designed to remain good solutions.
AbstractList	Differential evolution (DE) algorithm has been shown to be a very effective and efficient approach for solving global numerical optimization problems, which attracts a great attention of scientific researchers. Generally, most of DE algorithms only evolve one population by using certain kind of DE operators. However, as observed in nature, the working efficiency can be improved by using the concept of work specialization, in which the entire group should be divided into several sub-groups that are responsible for different tasks according to their capabilities. Inspired by this phenomenon, a novel adaptive multiple sub-populations based DE algorithm is designed in this paper, named MPADE, in which the parent population is split into three sub-populations based on the fitness values and then three novel DE strategies are respectively performed to take on the responsibility for either exploitation or exploration. Furthermore, a simple yet effective adaptive approach is designed for parameter adjustment in the three DE strategies and a replacement strategy is put forward to fully exploit the useful information from the trial vectors and target vectors, which enhance the optimization performance. In order to validate the effectiveness of MPADE, it is tested on 55 benchmark functions and 15 real world problems. When compared with other DE variants, MPADE performs better in most of benchmark problems and real-world problems. Moreover, the impacts of the MPADE components and their parameter sensitivity are also analyzed experimentally. •Three novel mutation strategies are run in three sub-populations respectively.•A novel adaptive strategy is presented to tune the systemic parameters.•A simple replacement strategy is designed to remain good solutions. Differential evolution (DE) algorithm has been shown to be a very effective and efficient approach for solving global numerical optimization problems, which attracts a great attention of scientific researchers. Generally, most of DE algorithms only evolve one population by using certain kind of DE operators. However, as observed in nature, the working efficiency can be improved by using the concept of work specialization, in which the entire group should be divided into several sub-groups that are responsible for different tasks according to their capabilities. Inspired by this phenomenon, a novel adaptive multiple sub-populations based DE algorithm is designed in this paper, named MPADE, in which the parent population is split into three sub-populations based on the fitness values and then three novel DE strategies are respectively performed to take on the responsibility for either exploitation or exploration. Furthermore, a simple yet effective adaptive approach is designed for parameter adjustment in the three DE strategies and a replacement strategy is put forward to fully exploit the useful information from the trial vectors and target vectors, which enhance the optimization performance. In order to validate the effectiveness of MPADE, it is tested on 55 benchmark functions and 15 real world problems. When compared with other DE variants, MPADE performs better in most of benchmark problems and real-world problems. Moreover, the impacts of the MPADE components and their parameter sensitivity are also analyzed experimentally.
Author	Cui, Laizhong Lu, Nan Li, Genghui Chen, Jianyong Lin, Qiuzhen
Author_xml	– sequence: 1 givenname: Laizhong orcidid: 0000-0003-1991-290X surname: Cui fullname: Cui, Laizhong – sequence: 2 givenname: Genghui surname: Li fullname: Li, Genghui – sequence: 3 givenname: Qiuzhen surname: Lin fullname: Lin, Qiuzhen email: qiuzhlin@szu.edu.cn – sequence: 4 givenname: Jianyong surname: Chen fullname: Chen, Jianyong – sequence: 5 givenname: Nan surname: Lu fullname: Lu, Nan
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SubjectTerms	Adaptive algorithms Adaptive parameter control Algorithms Benchmarks Differential evolution Evolutionary algorithms Genetic algorithms Global optimization Mathematical analysis Mathematical models Mathematical problems Multiple sub-populations Optimization Optimization techniques Replacement strategy Strategy Studies Vector space Vectors (mathematics)
Title	Adaptive differential evolution algorithm with novel mutation strategies in multiple sub-populations
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