Abstract

A multiobjective optimization problem involves multiple objectives, often conflicting, to be met or optimized. A Pareto front provides a set of best solutions to determine the trade-offs between the various objectives. New evolutionary approaches demonstrated its ability to build well-delineated Pareto fronts in diverse multiobjective optimization problems, including multicriteria optimization in job shop scheduling with regular and nonregular objective functions. Good parameter settings and appropriate representations can enhance the behavior of an evolutionary algorithm. The present article shows a study of the influence of distinct parameter combinations as well as different chromosome representations. Details of implementation and results are discussed.

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