Cat swarm optimization with normal mutation for fast convergence of multimodal functions
Cat swarm optimization with normal mutation for fast convergence of multimodal functions
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DOI：
10.1016/j.asoc.2018.02.012
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发表时间：
2018-05
期刊：
影响因子：
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通讯作者：
L. Pappula;D. Ghosh
中科院分区：
文献类型：
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作者：
L. Pappula;D. Ghosh
提出了一种基于正态变异策略的猫群优化算法（NMCSO），该算法具有有效的全局搜索能力，加快了收敛速度。经典的CSO算法由于随机变异过程，容易陷入局部最优，容易早熟收敛。这一弱点限制了经典CSO的广泛应用。为了克服这些缺点，本文在变异过程中采用了正态变异。它使猫在更好的方向上寻找位置，避免了早熟收敛和局部最优的问题。通过对几个典型的单峰、旋转、非旋转和平移多峰问题的实验，验证了该方法的有效性。此外，NMCSO也被应用于解决大参数优化问题。实验结果表明，该方法在收敛速度、全局最优性、求解精度和算法可靠性等方面均上级经典的CSO、粒子群优化（PSO）和一些最先进的进化算法.
A normal mutation strategy based cat swarm optimization (NMCSO) that features effective global search capabilities with accelerating convergence speed is presented. The classical CSO suffers from the premature convergence and gets easily trapped in the local optima because of the random mutation process. This frailty has restricted wider range of applications of the classical CSO. To overcome the drawbacks, the normal mutation is adopted in the mutation process of this paper. It enables the cats to seek the positions in better directions by avoiding the problem of premature convergence and local optima. Experiments are conducted on several benchmark unimodal, rotated, unrotated and shifted multimodal problems to demonstrate the effectiveness of the proposed method. Furthermore, NMCSO is also applied to solve the large parameter optimization problems. The experimental results illustrate that the proposed method is quite superior to classical CSO, particle swarm optimization (PSO) and some of the state of the art evolutionary algorithms in terms of convergence speed, global optimality, solution accuracy and algorithm reliability.