Multi-objective Particle Swarm Optimization based on adaptive mutation

D. Saha, Suman Banerjee, N. D. Jana
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引用次数: 4

Abstract

In recent decade Evolutionary Algorithms plays an important role in many engineering design and optimization problems. Particle Swarm Optimization (PSO) is one of such algorithm which is based on the intelligent food searching behavior of swarm like birds flock, fish schooling. It has been shown that it works efficiently on noisy, multimodal and composite functions. However, it stuck at local optima at later stage of evolution due to unexplore the search space. Several variations of pso and mutation based approached was developed for this problem. In this paper, an adaptive mutation is proposed for multiobjective pso and called it AMPSO. In AMPSO, mutation is applied on the position and velocity of the particles based on the fitness values of the particles. Proposed algorithm carried on 5 multiobjective benchmark functions. The experimental results shown the better performance comparing with other algorithms in terms of best, mean and standard deviation.
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基于自适应突变的多目标粒子群优化
近十年来,进化算法在许多工程设计和优化问题中发挥着重要作用。粒子群优化算法(Particle Swarm Optimization, PSO)就是其中一种基于鸟类、鱼群等群体的智能食物搜索行为的算法。结果表明,该方法对噪声函数、多模态函数和复合函数都能有效地处理。然而,由于未探索搜索空间,该算法在进化后期陷入局部最优状态。针对这一问题,提出了几种pso的变体和基于突变的方法。本文提出了一种多目标粒子群的自适应变异算法,称为AMPSO。在AMPSO中,基于粒子的适应度值对粒子的位置和速度进行突变。该算法进行了5个多目标基准函数。实验结果表明,该算法在最佳、均值和标准差方面都优于其他算法。
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