An efficient multi-objective evolutionary algorithm: OMOEA-II

Authors:
Sanyou Zeng;Shuzhen Yao;Lishan Kang;Yong Liu
Affiliations:
Dept. of Computer Science, China University of GeoSciences, Wuhan, Hubei, P.R. China;Dept. of Computer Science, China University of GeoSciences, Wuhan, Hubei, P.R. China;State Key Laboratory of Software Engineering, Wuhan University, Wuhan, Hubei, P.R. China;Dept. of Computer Science, China University of GeoSciences, Wuhan, Hubei, P.R. China
Venue:
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Year:
2005

Citing 8
Cited 5

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
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy

Evolutionary Computation
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results

Evolutionary Computation
An Orthogonal Multi-objective Evolutionary Algorithm for Multi-objective Optimization Problems with Constraints

Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II

IEEE Transactions on Evolutionary Computation
Hybrid Taguchi-genetic algorithm for global numerical optimization

IEEE Transactions on Evolutionary Computation
Multiobjective optimization and multiple constraint handling with evolutionary algorithms. I. A unified formulation

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

An efficient multi-objective evolutionary algorithm with steady-state replacement model

Proceedings of the 8th annual conference on Genetic and evolutionary computation
An orthogonal multi-objective evolutionary algorithm with lower-dimensional crossover

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A novel differential evolution algorithm based on Ɛ -domination and orthogonal design method for multiobjective optimization

EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Prediction-based population re-initialization for evolutionary dynamic multi-objective optimization

EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Comprehensive Survey of the Hybrid Evolutionary Algorithms

International Journal of Applied Evolutionary Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

An improved orthogonal multi-objective evolutionary algorithm (OMOEA), called OMOEA-II, is proposed in this paper. Two new crossovers used in OMOEA-II are orthogonal crossover and linear crossover. By using these two crossover operators, only small orthogonal array rather than large orthogonal array is needed for exploiting optimal in the global space. Such reduction in orthogonal array can avoid exponential creation of solutions of OMOEA and improve the performance in robusticity without degrading precision and distribution of solutions. Experimental results show that OMOEA-II can solve problems with high dimensions and large number of local Pareto-optimal fronts better than some existing algorithms recently reported in the literatures.