NDLI: An evolutionary algorithm for multiobjective optimization: the strength pareto approach (1998).

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An evolutionary algorithm for multiobjective optimization: the strength pareto approach (1998).

Content Provider	CiteSeerX
Author	Zitzler, Eckart Thiele, Lothar
Abstract	Evolutionary algorithms (EA) have proved to be well suited for optimization problems 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 fitness of an individual according to the Pareto dominance relationship; c) unlike the commonly-used fitness 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 SPE...
File Format	PDF
Publisher Date	1998-01-01
Access Restriction	Open
Subject Keyword	Evolutionary Algorithm Multiobjective Optimization Strength Pareto Approach Pareto Dominance Pareto-optimal Solution Inherent Parallelism New Evolutionary Approach Multiple Objective Pareto Set Population Diversity Unique Manner Optimization Problem Pareto Dominance Relationship Commonly-used Fitness Sharing Single Run Previous Multiobjective Ea Various Feature Proof-of-principle Result Strength Pareto Evolutionary Algorithm
Content Type	Text

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