Details

Autor(en) / Beteiligte

• Qiao, Kangjia
• Liang, Jing
• Yu, Kunjie
• Yuan, Minghua
• Qu, Boyang
• Yue, Caitong

Titel

Self-adaptive resources allocation-based differential evolution for constrained evolutionary optimization

Ist Teil von

• Knowledge-based systems, 2022-01, Vol.235, p.107653, Article 107653

Ort / Verlag

Amsterdam: Elsevier B.V

Erscheinungsjahr

2022

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• When using evolutionary algorithms to address constrained optimization problems, it is important to balance not only the diversity and convergence but also the constraints and objective function. To this end, a self-adaptive resources allocation-based differential evolution (SRADE) is presented in this paper. Specifically, during the evolutionary process, three mutation strategies with distinct focuses are collaboratively employed and adaptively assigned to different individuals based on their performance feedback. That is, most of the computing resources will be consumed by the most efficient strategy at different evolutionary stages to mitigate inefficient search under limited resources. These three collaborative strategies focus on maintaining population diversity, driving the population into feasible regions, and promoting the population toward the objective, respectively. Combining the self-adaptive resources allocation scheme and diverse search strategies is expected to satisfy the requirements of the population for diversity, convergence, constraints, and the objective at a certain iteration. Extensive experiments are performed on three benchmark test suites, including a large number of test functions from IEEE CEC 2006, 2010, and 2017. Compared to other well-designed constrained evolutionary approaches, SRADE exhibits superior or very competitive performance.