Abstract
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.
Highlights
Optimization problems are frequently encountered in many fields such as engineering design, data analysis, financial planning and business [1]
We can see that CoENDE generally performs significantly better than ENA, EPSO and NShde
Niching technique has been widely investigated in Evolutionary algorithms (EAs) community and existing studies generally focus on employing a single fixed niching scheme or several niching schemes in an ensemble manner
Summary
Optimization problems are frequently encountered in many fields such as engineering design, data analysis, financial planning and business [1]. The global optimization algorithms aim to identify the optimal or near-optimal solution. As the solution space usually involves in many local optima, avoiding these local optima is a major challenge for global optimization. Evolutionary algorithms (EAs) [2]–[4], which are able to avoid local optima of solution space, have been widely employed for global optimization [5], [6]. Unlike other EAs, DE generates offspring by employing scaled differences among randomly sampled individuals in the population, which makes it self-adaptive to the fitness landscape of search space [9]. Like other EAs, DE suffers from premature convergence, especially, for the problem involving in complex search space with many local optima
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