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A Novel Particle Swarm Optimization Algorithm

机译:一种新的粒子群优化算法

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摘要

A novel particle swarm optimization (NPSO) algorithm with dynamically changing inertia weight based on fitness and iterations was presented for improving the performance of the Particle Swarm Optimization algorithm. The new algorithm was tested with three benchmark functions. The experimental results show that the swarm can escape from local optimum, and it also can speed up the convergence of particles to improve the performance.
机译:为了提高粒子群优化算法的性能,提出了一种基于适应度和迭代的动态改变惯性权重的新型粒子群优化算法。新算法通过三个基准函数进行了测试。实验结果表明,该群体可以逃脱局部最优,并且可以加快粒子的收敛,从而提高性能。

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