利用增强的头脑风暴优化方法研究种群多样性

S. N. Kofie, S. H. Sackey
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引用次数: 1

摘要

在进化算法中,种群多样性是解决问题的重要因素。为了避免过早收敛,必须在进化过程中保持种群的多样性。种群多样性保证了算法避免过早收敛。人口多样性是研究中广泛使用的一种衡量过早收敛的指标。种群多样性便于测量和动态调整算法的探索或开发能力。脑风暴优化(Brain Storm Optimization, BSO)是受人类在问题解决过程中的合作行为启发而产生的一种新型群体智能方法。BSO遭受过早收敛,部分原因是由于解决方案被聚类。求解集在几次迭代后聚类,表明在搜索过程中多样性水平迅速下降。为了提高原BSO算法的计算效率并保持种群多样性,提出了两种方法对原BSO进行重新聚类。我们在客观空间中引入了一个具有柯西分布的BSO,并且知道柯西分布可以推断出更快的速率。此外,我们引入了一个新的步长方程作为参数来平衡搜索空间的探索和利用。一个好的算法在两个空间(目标空间和参数空间)都保持种群多样性。我们的目标是从种群多样性的角度研究为什么增强的BSO在一组五个基准函数上有效地执行。实验结果表明,该算法在种群多样性度量方面具有较好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Study of Population Diversity Using an Enhanced Brain Storm Optimization
In evolutionary algorithm, population diversity is a vital factor for solving problems. To avoid the premature convergence, it is imperative to preserve the population diversity during the evolution. The population diversity ensures avoiding the premature convergence. Population diversity is a measure that has been used extensively in studies to measure premature convergence. Population diversity is convenient for measuring and dynamically adjusting an algorithm's ability of exploration or exploitation. Brain Storm Optimization (BSO) is a new kind of swarm intelligence method inspired by the cooperative behavior of human beings in the problem-solving process. BSO suffers the premature convergence which happens partly due to the solutions getting clustered. The solution set clustered after a few iterations which indicate that the diversity level decreases rapidly during the search. In order to enhance the computational efficacy of the original BSO algorithm and maintain the population diversity, we propose two ways to re-cluster the original BSO. We introduce a BSO in objective space with a Cauchy distribution with the knowledge that Cauchy distribution infers a faster rate. In addition, we introduce a new step size equation as a parameter to balance the exploration and exploitation in the search space. A good algorithm maintains population diversity in both spaces (objective and parameter spaces). Our goal is to investigate why the enhanced BSO performs efficiently from the perspective of population diversity on a set of five benchmark functions. Experimental figures show that the performance of the proposed algorithms performs better from the population diversity measurement.
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