Multi-Objective Evolution Strategy for Dynamic Multi-objective Optimization
This paper presents a novel evolution strategy based evolutionary algorithm, named DMOES, which can efficiently and effectively solve multi-objective optimization problems in dynamic environments. DMOES can track the new approximate Pareto set and approximate Pareto front as quickly as possible when the environment changes.
In addition, DMOES can obtain a well-converged and well-diversified Pareto front with much less population size and far lower computational cost. The larger the number of individuals, the sharper the contour of the resulted approximate Pareto front will be.
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All publications which use this Programe or any code in the Programe should acknowledge the use of "MaOES" and reference "Kai Zhang, Chaonan Shen, Xiaoming Liu, Gary G. Yen, Multi-Objective Evolution Strategy for Dynamic Multi-objective Optimization, IEEE Transactions on Evolutionary Computation, 2020, DOI: 10.1109/TEVC.2020.2985323"