一种新的基于人工蜂群和极限优化的混合优化算法

Vahid Azadehgan, Nafiseh Jafarian, Farshad Jafarieh
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引用次数: 1

摘要

人工蜂群(Artificial Bee Colony, ABC)算法是一种基于蜂群智能行为的优化算法。ABC在解决优化问题方面越来越受到重视。与其他技术杂交后,进一步显示出其优越性。为了更高效、准确、可靠地求解优化问题,本文提出了一种新型的混合人工蜂群。在进化过程中,利用极值优化技术提高了算法的搜索性能,使算法具有更强的开发能力。基于几个经过充分研究的基准的仿真和比较证明了所提出算法的有效性、高效性和鲁棒性
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new hybrid algorithm for optimization based on Artificial Bee Colony and Extremal Optimization
Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behavior of honey bee swarm. ABC has gained increasing attention in tackling optimization problems. Its further superiority when hybridized with other techniques is also shown. In this paper a novel hybrid artificial bee colony is proposed in order to solve optimization problems more efficiently, accurately and reliably. During the course of evolvement Extremal Optimization is used to improve the search performance and this makes proposed algorithms have more powerful exploitation capabilities. Simulation and comparisons based on several well-studied benchmarks demonstrate the effectiveness, efficiency and robustness of the proposed algorithms
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