Parameter Adaptive Differential Evolution Based on Individual Diversity

Differential evolution (DE) has emerged as a numerical optimization technique due to its conceptual simplicity and demonstrated effectiveness across diverse problem domains. However, the algorithm’s performance remains critically dependent on appropriate control parameter settings. This paper introduces a novel diversity-based parameter adaptation (div) mechanism, generates two sets of symmetrical parameters, F and CR, adaptively first, and then dynamically selects the final parameters based on individual diversity rankings. It employs a straightforward approach to identify the more effective option from two sets of symmetrical parameters. Comprehensive experimental evaluation demonstrated that the div mechanism significantly enhanced the performance of the DE algorithm. Furthermore, by incorporating div, our enhanced algorithm exhibited superior optimization capability compared to five state-of-the-art DE variants. The results show that, among the 145 cases studied, DTDE-div outperformed others in 92 cases and underperformed in 32 cases, with the lowest performance ranking of 2.59. Consequently, DTDE-div demonstrated superior performance compared to other advanced DE variants. The results highlight the effectiveness of div in enhancing solution precision while preventing premature convergence.