A multi-population firefly algorithm for dynamic optimization problems

F. Özsoydan, A. Baykasoğlu
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引用次数: 22

Abstract

In traditional optimization problems, problem domain, constraints and problem related data are assumed to remain stationary throughout the optimization process. However, numerous real life optimization problems are indeed dynamic in their nature due to unpredictable events such as due date changes, arrival of new jobs or cancellations. In the literature, a problem with one of these features is referred as dynamic optimization problem (DOP). In contrast to static optimization problems, in DOPs, the aim is not only to find the optimum of the current configuration of a problem environment, but to track and find the changing optima. The field of dynamic optimization is a hot research area and it has attracted a remarkable attention of researchers. A considerable number of recent studies on DOPs usually employs bio-inspired metaheuristic algorithms, which are efficient on a wide range of static optimization problems. In the present work, a multi-population firefly algorithm with chaotic maps is proposed to solve DOPs. The tests are conducted on the well known moving peaks benchmark problem. In regard to the results, the proposed algorithm is found as a promising approach for the present problem.
动态优化问题的多种群萤火虫算法
在传统的优化问题中,假设问题域、约束和问题相关数据在整个优化过程中保持平稳。然而,许多现实生活中的优化问题在本质上确实是动态的,因为不可预测的事件,如截止日期的变化,新工作的到来或取消。在文献中,具有这些特征之一的问题被称为动态优化问题(DOP)。与静态优化问题相比,在DOPs中,目标不仅是找到问题环境当前配置的最优,而且要跟踪和找到变化的最优。动态优化是一个非常热门的研究领域,引起了研究者们的极大关注。近来相当多的关于DOPs的研究通常采用生物启发的元启发式算法,这种算法在广泛的静态优化问题上是有效的。本文提出了一种基于混沌映射的多种群萤火虫算法。测试是在众所周知的移动峰值基准问题上进行的。结果表明,该算法是解决当前问题的一种很有前途的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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