Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Multiobjective immune algorithm with nondominated neighbor-based selection
Evolutionary Computation
Evolutionary multi-objective optimization: a historical view of the field
IEEE Computational Intelligence Magazine
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Multiobjective optimization problems (MOPs) have attracted intensive efforts from AI community and many multiobjective evolutionary algorithms (MOEAs) were proposed to tackle MOPs. In addition, a few researchers exploited MOEAs to solve constraint optimization problems (COPs). In this paper, we investigate how to tackle a MOP by iteratively solving a series of COPs and propose the algorithm named multiobjective evolutionary algorithm based on constraint optimization (MEACO). In contrast to existing MOEAs, MEACO requires no complex selection mechanism or elitism strategy in solving MOPs. Given a MOP, MEACO firstly constructs a new COP by transforming all but one of objective functions into constraints. Then, the optimal solution of this COP is computed by a subroutine evolutionary algorithm so as to determine some Pareto-optimal solutions. After that, a new COP with dramatically reduced search space can be constructed using existing Pareto-optimal solutions. This new generated COP will be further solved to find more Pareto-optimal solutions. This process is repeated until the stopping criterion is met. Experimental results on 9 well-known MOP test problems show that our new algorithm outperforms existing MOEAs in terms of convergence and spacing metrics.