Design and Analysis of Experiments
Design and Analysis of Experiments
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Multiobjective programming using uniform design and genetic algorithm
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An orthogonal genetic algorithm for multimedia multicast routing
IEEE Transactions on Evolutionary Computation
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
RM-MEDA: A Regularity Model-Based Multiobjective Estimation of Distribution Algorithm
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
Multi-objective optimization with artificial weed colonies
Information Sciences: an International Journal
Clustering-based leaders' selection in multi-objective particle swarm optimisation
IDEAL'11 Proceedings of the 12th international conference on Intelligent data engineering and automated learning
Improved MOPSO based on ε-domination
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
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Designing efficient algorithms for difficult multi-objective optimization problems is a very challenging problem. In this paper a new clustering multi-objective evolutionary algorithm based on orthogonal and uniform design is proposed. First, the orthogonal design is used to generate initial population of points that are scattered uniformly over the feasible solution space, so that the algorithm can evenly scan the feasible solution space once to locate good points for further exploration in subsequent iterations. Second, to explore the search space efficiently and get uniformly distributed and widely spread solutions in objective space, a new crossover operator is designed. Its exploration focus is mainly put on the sparse part and the boundary part of the obtained non-dominated solutions in objective space. Third, to get desired number of well distributed solutions in objective space, a new clustering method is proposed to select the non-dominated solutions. Finally, experiments on thirteen very difficult benchmark problems were made, and the results indicate the proposed algorithm is efficient.