Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
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
Generalised Regression GA for Handling Inseparable Function Interaction: Algorithm and Applications
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Design and Analysis of Experiments
Design and Analysis of Experiments
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 Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Orthogonal immune algorithm with diversity-based selection for numerical optimization
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
A dominance tree and its application in evolutionary multi-objective optimization
Information Sciences: an International Journal
A fast multi-objective evolutionary algorithm based on a tree structure
Applied Soft Computing
An orthogonal multi-objective evolutionary algorithm with lower-dimensional crossover
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A Novel Hybrid Particle Swarm Optimization for Multi-Objective Problems
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
ISICA'07 Proceedings of the 2nd international conference on Advances in computation and intelligence
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
A synthesis of four-branch microwave antenna by evolution algorithm and orthogonal experiment
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Enhancing the search ability of differential evolution through orthogonal crossover
Information Sciences: an International Journal
An orthogonal dynamic evolutionary algorithm with niches
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
An efficient multi-objective evolutionary algorithm: OMOEA-II
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
An immune genetic algorithm with orthogonal initialization for analog circuit design
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
BSTBGA: A hybrid genetic algorithm for constrained multi-objective optimization problems
Computers and Operations Research
Comprehensive Survey of the Hybrid Evolutionary Algorithms
International Journal of Applied Evolutionary Computation
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In this paper, an orthogonal multi-objective evolutionary algorithm (OMOEA) is proposed for multi-objective optimization problems (MOPs) with constraints. Firstly, these constraints are taken into account when determining Pareto dominance. As a result, a strict partial-ordered relation is obtained, and feasibility is not considered later in the selection process. Then, the orthogonal design and the statistical optimal method are generalized to MOPs, and a new type of multi-objective evolutionary algorithm (MOEA) is constructed. In this framework, an original niche evolves first, and splits into a group of sub-niches. Then every sub-niche repeats the above process. Due to the uniformity of the search, the optimality of the statistics, and the exponential increase of the splitting frequency of the niches, OMOEA uses a deterministic search without blindness or stochasticity. It can soon yield a large set of solutions which converges to the Pareto-optimal set with high precision and uniform distribution. We take six test problems designed by Deb, Zitzler et al., and an engineering problem (W) with constraints provided by Ray et al. to test the new technique. The numerical experiments show that our algorithm is superior to other MOGAS and MOEAs, such as FFGA, NSGAII, SPEA2, and so on, in terms of the precision, quantity and distribution of solutions. Notably, for the engineering problem W, it finds the Pareto-optimal set, which was previously unknown.