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首页> 外文学位 >An automatic regrouping mechanism to deal with stagnation in Particle Swarm Optimization.
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An automatic regrouping mechanism to deal with stagnation in Particle Swarm Optimization.

机译：一种自动重新组合机制来处理粒子群优化中的停滞。

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摘要

Particle Swarm Optimization (PSO), which was intended to be a population-based global search method, is known to suffer from premature convergence prior to discovering the true global minimizer. In this thesis, a novel regrouping mechanism is proposed, which aims to liberate particles from the state of premature convergence. This is done by automatically regrouping the swarm once particles have converged to within a pre-specified percentage of the diameter of the search space. The degree of uncertainty inferred from the distribution of particles at premature convergence is used to determine the magnitude of the regrouping per dimension. The resulting PSO with regrouping (RegPSO) provides a mechanism more efficient than repeatedly restarting the search by making good use of the state of the swarm at premature convergence. Results suggest that RegPSO is less problem-dependent and consequently provides more consistent performance than the comparison algorithms across the benchmark suite used for testing.
机译：粒子群优化（PSO）旨在成为基于总体的全局搜索方法，但在发现真正的全局最小化器之前，它会遭受过早的收敛。本文提出了一种新的重组机制，旨在将粒子从过早收敛的状态中解放出来。这是通过在粒子收敛到搜索空间直径的预定百分比范围内后自动重新组合群来完成的。由过早收敛时的粒子分布推断出的不确定程度用于确定每维重新组合的大小。生成的带有重新分组的PSO（RegPSO）提供了一种机制，该机制比通过在早熟收敛时充分利用群体状态来重复重新启动搜索更有效。结果表明，与用于测试的基准套件中的比较算法相比，RegPSO的问题依赖性较小，因此提供了更一致的性能。

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