A genetic-based framework for solving (multi-criteria) weighted matching problems

Abstract The purpose of this paper is to present a flexible genetic-based framework for solving the multi-criteria weighted matching problem (mc-WMP). In the first part of this paper, we design a genetic-based framework for solving the ordinary weighted matching problem. We present an extensive analysis of the quality of the results and introduce a methodology for tuning its parameters. In the second part, we develop a modified genetic-based algorithm for solving the mc-WMP. The algorithm generates a significant and representative portion of the Pareto optimal set. To assess the performance of the algorithm, we conduct computational experiments with two and three criteria. The potential of the proposed algorithm is demonstrated by comparing to a multi-objective simulated annealing algorithm.

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