An evolutionary algorithm for multiobjective optimization: the strength Pareto approach

Evolutionary algorithms EA have proved to be well suited for optimization prob lems with multiple objectives Due to their inherent parallelism they are able to capture a number of solutions concurrently in a single run In this report we propose a new evolutionary approach to multicriteria optimization the Strength Pareto Evolutionary Algorithm SPEA It combines various features of previous multiobjective EAs in a unique manner and is characterized as follows a besides the population a set of individuals is maintained which contains the Pareto optimal solutions generated so far b this set is used to evaluate the tness of an individual according to the Pareto dominance relationship c unlike the commonly used tness sharing population diversity is preserved on basis of Pareto dominance rather than distance d a clustering method is incorporated to reduce the Pareto set without destroying its characteristics The proof of principle results on two problems suggest that SPEA is very e ective in sampling from along the entire Pareto optimal front and distributing the generated solutions over the tradeo surface Moreover we compare SPEA with four other multiobjective EAs as well as a single objective EA and a random search method in application to an extended knapsack problem Regarding two complementary quantitative measures SPEA outperforms the other approaches at a wide margin on this test problem Finally a number of suggestions for extension and application of the new algorithm are discussed

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