CIR-DE: A chaotic individual regeneration mechanism for solving the stagnation problem in differential evolution

Abstract

Stagnant evolution is a problem frequently encountered by the population in differential evolution (DE). Aiming at the stagnation phenomenon, a comprehensive interpretation is provided in this paper. Our experiment confirms that the individuals that continuously stop evolving can be classified into two categories: global and local stagnant individuals, whose causes and exhibited characteristics are associated with the search behavior of the population. Based on the above findings, we propose a chaotic individual regeneration framework (CIR) for DEs. In the CIR-DE, a monitor is designed to recognize different types of stagnant individuals by evaluating the whole population’s convergence speed and specific individual’s location. Besides, two chaotic regeneration techniques are proposed to guide the above two types of individuals away from stagnation using the knowledge from solution and objective spaces. The CIR framework is implemented in nine representative DEs and tested in the CEC 2014, CEC 2017, CEC 2022 theoretical benchmarks and five real-world problems. The results reveal that our framework can significantly improve original DEs’ performance and alleviate stagnation in both theoretical and practical scenarios. The CIR framework also shows strong competitiveness compared to the other stagnation-related frameworks and the state-of-the-art DE variants.