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- W2354615332 abstract "There are usually a lot of Pareto optimal solutions in a complicated multiobjective optimization,and some parts of them are easily to get,but others are not.At present,existing multiobjective evolutionary algorithms neglect the level of difficulty in solving Pareto optimal solutions,and they make use of fixed crossover and mutation probability in all parts,which is not efficient.Using a weighted min-max strategy,a group of fitness functions with the specially designed weight were obtained,and changing crossover and mutation probability following along with evolutionary generation were constructed.The crossover and mutation probability can be automatically regulated according to the level of difficulty in solving Pareto optimal solutions for a problem.Thus the proposed algorithm can enhance performance of algorithm and obtain evenly distributed Pareto optimal solutions.The numerical simulations show the proposed algorithm is very efficient." @default.
- W2354615332 created "2016-06-24" @default.
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- W2354615332 date "2011-01-01" @default.
- W2354615332 modified "2023-09-22" @default.
- W2354615332 title "New Evolutionary Algorithm for Complicated Multiobjective Optimization" @default.
- W2354615332 hasPublicationYear "2011" @default.
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