Dimension reduction by local principal component analysis
Neural Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
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
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Regional coverage Constellation Optimizing Design is a classical dynamic multi-objective optimizing problem. Against low efficiency of traditional multi-objective evolutionary algorithms and poor utilization of Pareto-optimal solutions distribution regularity etc, in this papera new approach OMEA which bases on the probability-model utilizing Pareto-optimal solutions distribution regularity to obtain a good distribution of Pareto-optimal solutions, we also apply the quantization technique and orthogonal design to generate initial points which spread uniformly in the feasible solution space. Considering coverage rate assessment criterions, we accomplish the design and simulation of Leo Constellation. Compared with NSGA-II, Pareto solutions by OMEA are closer to Pareto-optimal Front. The result of experiments shows a group of Pareto solutions with a uniform distribution can be achieved, which gives strong supports to constellation design determination.