Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Niche distributions on the pareto optimal front
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Multi-objective PSO algorithm based on fitness sharing and online elite archiving
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
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This paper investigates the multi-objective optimization Pareto genetic algorithms (MOPGA) for searching alternative non-dominated Pareto-optimal solutions. A kind of niching approach using clustering crowding and fast elite updating is designed to maintain population diversity and uniform distribution of non-dominated solutions. The time complexity analysis shows clustering crowding and fast elite updating is a cost-efficient niching method. The simulation optimization on various multi-objective 0/1 knapsack problems shows MOPGA is capable of approximating to Pareto front evenly and cost efficiently, and the convergence rate and the distribution uniformity are consistently superior to that of the strength Pareto evolutionary approach (SPEA).