Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Multiobjective programming using uniform design and genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
U-measure: a quality measure for multiobjective programming
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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In this paper, a novel evolutionary algorithm based on adaptive multiple fitness functions and adaptive objective space division for multiobjective optimization is proposed. It can overcome the shortcoming of those using the weighted sum of objectives as the fitness functions, and find uniformly distributed solutions over the entire Pareto front for non-convex and complex multiobjective programming. First, we divide the objective space into multiple regions with about the same size by uniform design adaptively, then adaptively define multiple fitness functions to search these regions, respectively. As a result, the Pareto solutions found on each region are adaptively changed and eventually are uniformly distributed over the entire Pareto front. We execute the proposed algorithm to solve five standard test functions and compare performance with that of four widely used algorithms. The results show that the proposed algorithm can generate widely spread and uniformly distributed solutions over the entire Pareto front, and perform better than the compared algorithms.