Abstract
Various distance minimization problems (DMPs) have been proposed to visualize the search behaviors of evolutionary multiobjective optimization (EMO) algorithms in solving many-objective problems, multiobjective multimodal problems, and dynamic multiobjective problems. Among those DMPs, only the box constraints are considered. In this paper, we propose several constraint DMPs to visualize the behaviors of EMO algorithms with constraint handling techniques. In the proposed constraint DMPs, constraints are simply specified in the two-dimensional decision space. In the same manner, high-dimensional problems and multimodal problems can also be generated. Computational experiments show different behaviors by different algorithms in various constraint DMPs.
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