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Multiobjective Optimization Algorithm Using Evolution Strategies
In order to overcome the shortcomings of traditional multi-objective optimization(MO) methods, several new methods based on evolutionary computation(EC) have been designed and continually improved in these years. Through intensive tests and comparisons, these new methods have been proved to be able to provide high efficiency in producing MO solutions. This paper trys to develop a new MO problem solver based on EC to solve MO problems. The main difference between proposed and other method is that the fitness is calculated based on rank and crowdness instead of domination level used by others. The evolution strategies(ES) is employed to be the tool to simulate the evolution process. Furthermore, both elitism strategy and anti-crowdness skill are used to get high-quality solutions which converge to Pareto front and are evenly distributed on the front. Both constrained and unconstrained MO problems can be solved by the proposed method. The method will be tested against several MO problems including constrained and unconstrained ones. Comparison are made among three methods for some test problems. The results have shown that the proposed method can provide satisfactory Pareto solutions for most test problems, and some drawbacks are observed. The possible reasons behind these drawbacks are discussed and some recommendations are given for further research.
|Appears in Collections:||機械工程學系所|
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