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首页> 外文期刊>International Journal of Applied Mathematics & Statistics >Multi-particles Learning Intelligent Swarm Optimizer
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Multi-particles Learning Intelligent Swarm Optimizer

机译：多粒子学习智能群优化器

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摘要

Based on the conventional Intelligent Single Particle Optimizer(ISPO), learning the multi-swarm in the swarm intelligence algorithm, the paper proposes Multi-particles Learning Intelligent Swarm Optimizer(MLISO). In the optimization process, MLISO randomly generates multi-particles, according to the evolutionary rule of ISPO.optimization, after sequence and cycle updating each particle to a certain number of iterations, multi-particles are sorted according to fitness value from good to bad, the individual with worst fitness value would learn from the best individual, if the individual with worst fitness value is still not improved after learning from the best individual with a certain number of times, the new individual is generated randomly again. The MLISO repeats the above steps until the termination condition is reached. Experimental results demonstrates that MLISO, whose optimization capacity exceeded the international improved algorithm based on PSO and ISPO, has some advantages in optimizing complex high-dimensional multimodal function with a large number of local optimal point.
机译：在传统智能单粒子优化器（ISPO）的基础上，研究了群体智能算法中的多群算法，提出了多粒子学习智能群优化器（MLISO）。在优化过程中，MLISO根据ISPO的演化规律随机生成多粒子。优化后，将每个粒子按顺序和周期更新为一定的迭代次数后，根据适合度值从好到坏对多粒子进行排序，适应度最差的个体会向最佳个体学习，如果适应性最差的个体经过一定次数向最佳个体学习后仍然没有得到改善，则会再次随机产生新个体。 MLISO重复上述步骤，直到达到终止条件为止。实验结果表明，MLISO的优化能力超过了基于PSO和ISPO的国际改进算法，在优化具有大量局部最优点的复杂高维多峰函数方面具有优势。

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