A self-adaptive multi-objective dynamic differential evolution algorithm and its application in chemical engineering - Zhang, Xiaodong; Jin, Lu; Cui, Chengtian; Sun, Jinsheng (Applied soft computing, 2021)
This paper proposes a new multi-objective dynamic differential evolution algorithm with parameter self-adaptive strategies, named SA-MODDE. All components of the algorithm are synergically designed to reach its full potential, containing parental selection, mutation strategy, parameter setting, survival selection, constraint handling, and termination criteria. The improvement measures emphasize exploiting Pareto dominance information more efficiently. Particularly, parameter adaptation schemes are introduced based on both prior knowledges of current individual and feedback information on previous promising solutions, and their effectiveness is validated by comparison with three fixed-parameter combinations. Extensive numerical tests are conducted on multiple test suites with five state-of-the-art peer competitors. The statistical results demonstrated that the SA-MODDE exhibits good proximity and diversity in dealing with benchmark functions with various characteristics. Three industrial (bio)chemical processes, including two optimal control and one reformulated constrained tri-objective, are investigated to show the feasibility and robustness of the SA-MODDE. •Pareto dominance relationship among the population is fully utilized during evolution processes.•Parameter self-adaptive strategies based on prior and posteriori information are presented.•Only generic control parameters of evolutionary algorithms are required.•A performance-based termination criterion is applied.•Tests on 18 numerical experiments and 3 (bio)chemical processes make results persuasive.