将遗传算法与粒子群算法混合实现,解决无约束优化问题

S. Nootyaskool
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引用次数: 2

摘要

遗传算法在搜索搜索方面具有优势。粒子群算法在粒子间运动信息共享方面具有优势。本研究提出了遗传算法与粒子群算法的结合。采用粒子群算法设计了混合遗传算法,并与简单遗传算法和简单粒子群算法进行了性能比较,两者的模型都能找到数值函数的五差复杂度解。实验结果表明,基于粒子群算法的混合遗传算法可以快速求解多模态和单模态的噪声信号。
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The hybrid implementation genetic algorithm with particle swarm optimization to solve the unconstrained optimization problems
Genetic algorithm (GA) has an advantage in exploration search. Particle swarm optimization (PSO) has an advantage in sharing movement information between particles. The combining between GA and PSO is proposed in this research. We design hybrid-GA with PSO, and compare the performance with simple GA and simple PSO, which their models will find the solution of five-difference complexity of numerical functions. The experiment result showed that hybrid GA with PSO can find the solution of a multimodal problem and unimodal with noise signal quickly.
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