Modified Particle Swarm Optimization
Modified Particle Swarm Optimization
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发表时间：
2009
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通讯作者：
S. Agrawal;R. Shimpi
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作者：
S. Agrawal;R. Shimpi
粒子群算法（PSO）是由Kennedy和Eberhart(1)提出的。粒子群优化是一种非常流行的优化技术，但它有一个主要的缺点，即可能过早收敛，即收敛到局部最优，而不是全局最优。本文试图通过解决粒子群算法存在的缺陷来提高粒子群算法的可靠性。导致早熟收敛的主要原因是粒子在迭代周期内受到前一个迭代周期的全局最优和个体最优位置的高度影响。另一个原因可能是在优化过程中粒子之间存在类似的信息流，这可能导致由相似粒子组成的群体（即多样性的损失）。在文献中，有一些尝试提高PSO的可靠性，如Liu等人(2)提出了一种多启动技术。本文提出了一种改进的粒子群优化算法。在迭代周期中，在确定粒子的新位置时，不仅要给最佳位置赋予权重，还要给最差位置赋予权重。这种机制将使PSO摆脱次优解，并使其能够向全局最优方向发展。基准函数的实验正在进行中，结果将在论文中报告。
Particle Swarm Optimization (PSO) was introduced by Kennedy and Eberhart (1). PSO is a very popular optimization technique, but it suffers from a major drawback of a possible premature convergence i.e. convergence to a local optimum and not to the global optimum. This paper attempts to improve on the reliability of PSO by addressing the drawback. The main cause for the premature convergence is the fact that particles get highly influenced during an iteration cycle by the global best and the personal best positions of the previous iteration cycle. Another reason can be a similar kind of information flow between particles during optimization, and this can result in a swarm consisting of similar particles (i.e. a loss in diversity). In literature, there are some attempts to improve the reliability of PSO, for example, Liu et al (2) proposed a multi-start technique. In the present paper, a modified particle swarm optimization is proposed. During an iteration cycle, while deciding new positions of particles, weightage would be given, not only to the best position, but also to the worst position. This mechanism would free PSO from sub-optimal solutions and would enable it to progress towards the global optimum. Experiments on the benchmark functions are in progress and results would be reported in the paper.