A Self-Learning Particle Swarm Optimizer for Global Optimization Problems
A Self-Learning Particle Swarm Optimizer for Global Optimization Problems
复制标题
DOI：
10.1109/tsmcb.2011.2171946
复制
发表时间：
2012-06-01
影响因子：
--
通讯作者：
Nguyen, Trung Thanh
中科院分区：
文献类型：
--
作者：
Li, Changhe;Yang, Shengxiang;Nguyen, Trung Thanh
粒子群算法（PSO）已被证明是解决全局优化问题的有效工具。到目前为止，大多数粒子群算法对所有粒子使用单一的学习模式，这意味着群体中的所有粒子使用相同的策略。这种单调的学习模式可能会导致特定粒子缺乏智能，使其无法处理不同的复杂情况。提出了一种求解全局优化问题的自学习粒子群优化算法（SLPSO）。在SLPSO中，每个粒子都有一组四种策略来应对搜索空间中的不同情况。四种策略之间的合作是通过个体层面的自适应学习框架来实现的，该框架可以使粒子根据自身的局部适应度景观选择最优策略。在45个测试函数和两个实际问题上的实验研究表明，SLPSO与其他几种同类算法相比具有优越的性能。
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.