文献类型：期刊文献

英文题名：A local search-based many-objective five-element cycle optimization algorithm

作者：Mao, Zhengyan[1,2];Liu, Mandan[1,2]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China;[2]Minist Educ, Key Lab Smart Mfg Energy Chem Proc, Beijing, Peoples R China

年份：2022

卷号：68

外文期刊名：SWARM AND EVOLUTIONARY COMPUTATION

收录：;EI(收录号：20214711183819);WOS:【SCI-EXPANDED(收录号:WOS:000744237500001)】；

基金：This work was supported by the Fundamental Research Funds for the Central Universities funded by the Chinese government (222201917006).

语种：英文

外文关键词：Multi-objective memetic algorithms; Local search; Five-elements cycle model; Many-objective evolutionary optimization

摘要：The conception of memetic algorithms (MAs) has provided a new perspective in algorithmic design through hybridizing and combining of local search techniques and population-based search. Previous research has proven that the performance of some multi-objective evolutionary algorithms (MOEAs) could be improved by hybridization with local search strategy. However, when designing many-objective evolutionary optimization algorithms, if the ranking scheme relies on the Pareto dominance relation, it is quite challenging to evaluate solutions with several objective values and distinguish the most effective solutions from a population full of "the first rank " solutions. In this study, a new many-objective five-element cycle optimization algorithm based on a gradient-based local search method (termed LSMaOFECO) is proposed. In the evolutionary search part of LSMaOFECO, within the five-element cycle model, the forces exerted on every individual on different objective dimensions are summed up as an evaluating criterion to decide whether a solution should be accepted directly or updated. In the selection phase, this evaluation method is used to choose the next generation from the parents and the offspring when the dominance relation ranking loses effectiveness. In the local search part, three search tactics are proposed, among which the worst-objective-search tactic outperforms the other two in the experiment and is subsequently used in the updating phase of LSMaOFECO. The performance of the proposed algorithm is compared with six state-of-theart multi-and many-objective algorithms by solving a set of many-objective test problems. The results obtained verify the utility of the LSMaOFECO in solving many-objective optimization problems (MaOPs).