Li Changhe, Yang Shengxiang, Nguyen Trung Thanh
School of Computer Science, China University of Geosciences, Wuhan, China.
IEEE Trans Syst Man Cybern B Cybern. 2012 Jun;42(3):627-46. doi: 10.1109/TSMCB.2011.2171946. Epub 2011 Nov 4.
Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.
粒子群优化算法（PSO）已被证明是解决全局优化问题的有效工具。到目前为止，大多数PSO算法对所有粒子都使用单一学习模式，这意味着群体中的所有粒子都使用相同的策略。这种单调的学习模式可能会导致特定粒子缺乏智能，使其无法应对不同的复杂情况。本文提出了一种用于全局优化问题的新算法，称为自学习粒子群优化器（SLPSO）。在SLPSO中，每个粒子有一组四种策略来应对搜索空间中的不同情况。这四种策略的协作是通过个体层面的自适应学习框架实现的，这可以使粒子根据自身局部适应度景观选择最优策略。对一组45个测试函数和两个实际问题的实验研究表明，与其他几种同类算法相比，SLPSO具有卓越的性能。
