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An algorithm is sensitive to parameters; different parameter settings for solving optimization problems can thus have a serious impact on algorithm performance. This leads to an inability to determine the optimal set of parameters for the algorithm to solve the problem at hand. In this study, we propose an adaptive strategy with a multi-population multi-objective algorithm (A-MPMO) framework to select the appropriate set of genetic settings according to the problem to be solved and eliminate the sensitivity of the algorithm to the parameters. Multi-population are often combined with Evolutionary Algorithms (EAs) as an effective strategy to maintain population diversity. First, we divided the population generated by the algorithm into multiple subpopulations to expand the search range and updated them iteratively using operators with different genetic parameters. Second, based on multi-population, subpopulations compete for limited computational resources, implying that the size of each subpopulation adaptively adjusts according to the degree of its contribution to problem solving. Finally, a set of subpopulations that are best suited to solve the problem is selected to improve the adaptability to different problems. For DTLZ, ZDT, and UF, compared to the other algorithms, A-MPMO was experimentally shown to produce better performance. [ABSTRACT FROM AUTHOR]

Following two decades of sustained studies, metaheuristic algorithms have made considerable achievements in the field of multi-objective optimization problems (MOPs). However, under most existing metaheuristic frameworks, an improved scheme introduced to address specific defects usually leads to additional problems that need to be solved further. Emerging optimization mechanisms should be considered to break the bottleneck, and an adaptive multi-objective competitive swarm optimization (AMOCSO) algorithm, a promising option for solving MOPs, is proposed in this paper. Firstly, the competitive mechanism is modified so that it can perform well on MOPs, and an improved learning scheme is designed for the winners and the losers, which can greatly enhance the optimization efficiency and balance the convergence and the diversity of the proposed algorithm. Then, an external archive and its maintenance schemes are introduced to prevent the population from degenerating and make the algorithm framework more comprehensive. Moreover, a practical adaptive strategy is proposed to fill the blank of parameter research, and no human factors exist in AMOCSO, which means that an amazing promotion can be achieved in generalization. Finally, abundant experimental studies are carried out, and the results of comparative experiments show that the proposed algorithm has significant advantages over several state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]