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
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Efficient and Scalable Pareto Optimization by Evolutionary Local Selection Algorithms
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
An overview of evolutionary algorithms in multiobjective optimization
Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary computation for supersonic wing-shapeoptimization
IEEE Transactions on Evolutionary Computation
A multiobjective optimization approach to solve a parallel machines scheduling problem
Advances in Artificial Intelligence
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
A new scheme for interactive multi-criteria decision making
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
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Evolutionary algorithms are becoming increasingly valuable in solving large-scale, realistic engineering multiobjective optimization (MO) problems, which typically require consideration of conflicting and competing design issues. The new procedure, Constraint Method-Based Evolutionary Algorithm (CMEA), presented in this paper is based upon underlying concepts in the constraint method described in the mathematical programming literature. Pareto optimality is achieved implicitly via a constraint approach, and convergence is enhanced by using beneficial seeding of the initial population. CMEA is evaluated by solving two test problems reported in the multiobjective evolutionary algorithm (MOEA) literature. Performance comparisons based on quantitative metrics for accuracy, coverage, and spread are presented. CMEA is relatively simple to implement and incorporate into existing implementations of evolutionary algorithm-based optimization procedures.