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Li Y, Wang S, Yang H, Chen H. Differential evolution with variable leader-adjoint populations. APPL INTELL 2022. [DOI: 10.1007/s10489-022-04290-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Enhancing differential evolution algorithm using leader-adjoint populations. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.11.106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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Li Y, Wang S, Yang B, Chen H, Wu Z, Yang H. Population reduction with individual similarity for differential evolution. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10264-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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Multi-strategy adaptive cuckoo search algorithm for numerical optimization. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10222-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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An improved generalized normal distribution optimization and its applications in numerical problems and engineering design problems. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10182-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Zhuang Y, Liu S, Ding C, Zhou X. Reversible watermarking based on extreme prediction using modified differential evolution. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03211-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
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SFSADE: an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy. Artif Intell Rev 2021. [DOI: 10.1007/s10462-021-10099-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
AbstractThe differential evolution (DE) algorithm is an efficient random search algorithm based on swarm intelligence for solving optimization problems. It has the advantages of easy implementation, fast convergence, strong optimization ability and good robustness. However, the performance of DE is very sensitive to the design of different operators and the setting of control parameters. To solve these key problems, this paper proposes an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy (SFSADE). It innovatively incorporates the idea of the shuffled frog-leaping algorithm into DE, and at the same time, it cleverly introduces a new strategy of classification mutation, and also designs a new adaptive adjustment mechanism for control parameters. In addition, we have carried out a large number of simulation experiments on the 25 benchmark functions of CEC 2005 and two nonparametric statistical tests to comprehensively evaluate the performance of SFSADE. Finally, the results of simulation experiments and nonparametric statistical tests show that SFSADE is very effective in improving DE, and significantly improves the overall diversity of the population in the process of dynamic evolution. Compared with other advanced DE variants, its global search speed and optimization performance also has strong competitiveness.
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