Co-Evolutionary Niching Differential Evolution Algorithm for Global Optimization

Le Yan

doi:10.1109/ACCESS.2021.3112906

Co-Evolutionary Niching Differential Evolution Algorithm for Global Optimization

2021-01-01

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DOI：10.1109/ACCESS.2021.3112906

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Preserving an appropriate population diversity is critical for the performance of evolutionary algorithms. In this paper, we present a co-evolutionary niching strategy (CoEN) to dynamically evolve appropriate niching methods and incorporate it into differential evolution (DE) to preserve the population diversity. The proposed CoEN strategy is achieved by optimizing a criterion, which involves both fitness improvement and population diversity resulting from employing the niching methods during evolution of DE. To verify the performance of proposed method, an extensive test on benchmark functions taken from CEC2019 and CEC2014 has been carried out. The results show the significance of proposed CoEN and, by incorporating CoEN, the resulting DE is able to achieve a better or competitive performance than related algorithms.

Keywords:

Statistics

Sociology

Optimization

Evolution (biology)

Standards

Heuristic algorithms

Convergence

Niching method

evolutionary algorithm

crowding

restricted tournament selection

global optimization

Co-Evolutionary Niching Differential Evolution Algorithm for Global Optimization

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