Abstract

Solving optimization problems with multiple (often conflicting) objectives is generally a quite difficult goal. Evolutionary Algorithms (EAs) were initially extended and applied during the mid-eighties in an attempt to stochastically solve problems of this generic class. During the past decade a variety of Multiobjective EA (MOEA) techniques have been proposed and applied to many scientific and engineering applications. Our discussion's intent is to rigorously define and execute a quantitative MOEA performance comparison methodology. Almost all comparisons cited in the current literature visually compare algorithmic results, resulting in only relative conclusions. Our methodology gives a basis for absolute conclusions regarding MOEA performance. Selected results from its execution with four MOEAs are presented and described. 1 Introduction Multiobjective Evolutionary Algorithms (MOEAs) are now a well-established field within Evolutionary Computation, initially developed in the mid-ei...

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