Self regulating particle swarm optimization algorithm
Self regulating particle swarm optimization algorithm
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DOI：
10.1016/j.ins.2014.09.053
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发表时间：
2015-02
期刊：
影响因子：
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通讯作者：
M. Tanweer;S. Sundaram;N. Sundararajan
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文献类型：
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作者：
M. Tanweer;S. Sundaram;N. Sundararajan
在本文中，我们提出了一种新的粒子群优化算法，将最好的人类学习策略，找到最佳的解决方案，称为自调节粒子群优化（SRPSO）算法。人类认知心理学的研究表明，最好的计划者会根据当前状态和对他人最佳体验的感知来调整自己的策略。利用这些思想，我们提出了两种学习策略的PSO算法。第一种方法使用自调节惯性权重，第二种方法使用全局搜索方向上的自我感知。最优粒子采用自调整惯性权重进行更好的探索，其余粒子采用全局搜索方向的自我感知进行解空间的智能开发。SRPSO算法已被评估使用的25个基准函数从CEC2005和一个现实世界的问题，雷达系统的设计。的结果进行了比较，与六个国家的最先进的粒子群算法的变种，如裸露的骨头粒子群算法（BBPSO），综合学习粒子群算法（CLPSO）等，这两个建议的学习策略，帮助SRPSO实现更快的收敛，并提供更好的解决方案，在大多数问题。此外，统计分析的性能评价的不同算法对CEC 2005问题表明，SRPSO是优于其他算法的95%的置信水平。
In this paper, we propose a new particle swarm optimization algorithm incorporating the best human learning strategies for finding the optimum solution, referred to as a Self Regulating Particle Swarm Optimization (SRPSO) algorithm. Studies in human cognitive psychology have indicated that the best planners regulate their strategies with respect to the current state and their perception of the best experiences from others. Using these ideas, we propose two learning strategies for the PSO algorithm. The first one uses a self-regulating inertia weight and the second uses the self-perception on the global search direction. The self-regulating inertia weight is employed by the best particle for better exploration and the self-perception of the global search direction is employed by the rest of the particles for intelligent exploitation of the solution space. SRPSO algorithm has been evaluated using the 25 benchmark functions from CEC2005 and a real-world problem for a radar system design. The results have been compared with six state-of-the-art PSO variants like Bare Bones PSO (BBPSO), Comprehensive Learning PSO (CLPSO), etc. The two proposed learning strategies help SRPSO to achieve faster convergence and provide better solutions in most of the problems. Further, a statistical analysis on performance evaluation of the different algorithms on CEC2005 problems indicates that SRPSO is better than other algorithms with a 95% confidence level.