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A New Genetic Algorithm with Elliptical Crossover for Constrained Multi-objective Optimization Problems

机译:求解约束多目标优化问题的椭圆交叉遗传算法

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The crossover operator plays an important role in a genetic algorithm, which produces two or more offspring for each pair of parents. With the help of the crossover operator, the genetic algorithm can explore the search space effectively. In this paper, we propose a new crossover operator called elliptical crossover operator, which can explore the search domain effectively. A local search scheme is designed to get more precise and wider nondominated solutions. In the local search scheme, the square search scheme and uniform design methods are combined. Based on the elliptical crossover operator and the local search scheme, a novel genetic algorithm is designed for constrained multi-objective optimization problems. Simulation results on several test functions indicates the effectiveness of the designed algorithm.
机译:交叉算子在遗传算法中起着重要作用,遗传算法为每对父母产生两个或多个后代。在交叉算子的帮助下,遗传算法可以有效地探索搜索空间。在本文中,我们提出了一种新的交叉算子,称为椭圆交叉算子,它可以有效地探索搜索域。本地搜索方案旨在获取更精确,更广泛的非支配解决方案。在局部搜索方案中,将正方形搜索方案和统一设计方法结合在一起。基于椭圆交叉算子和局部搜索方案,为约束多目标优化问题设计了一种新的遗传算法。在几个测试函数上的仿真结果表明了所设计算法的有效性。

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