随机蛙跳算法的研究进展

计算机科学 ›› 2010, Vol. 37 ›› Issue (7): 16-19.

随机蛙跳算法的研究进展

韩毅,蔡建湖,周根贵,李延来,林华珍,唐加福

(浙江工业大学经贸管理学院杭州310023);(东北大学流程工业综合自动化教育部重点实验室沈阳110004)

出版日期:2018-12-01 发布日期:2018-12-01
基金资助:
本文受国家自然科学基金(70625001,70721001,70671095,70971017)，浙江省科技计划软科学研究项目(2009035007)资助。

Advances in Shuffled Frog Leaping Algorithm

HAN Yi,CAI Jian-hu,ZHOU Gen-gui,LI Yan-lai,LIN Hua-zhen,TANG Jia-fu

Online:2018-12-01 Published:2018-12-01

摘要/Abstract

摘要： 随机蛙跳算法(Shuffled Frog Leaping Agorithm, SFLA)是进化计算领域中一种新兴、有效的亚启发式群体计算技术，近几年来逐渐受到学术界和工程优化领域的关注。SFLA结合了具有较强局部搜索(Local Search, LS)能力的元算法(Mcmctic Algorithm, MA)和具有良好全局搜索(Global Search, GS)性能的粒子群算法(Particle Swarm Optimization, PSO)的特点，因此其寻优能力强，易于编程实现。详细阐述了SFLA的基本原理和流程，总结了SFLA目前在优化和工程技术等领域中的研究，展望了SFLA的发展前景。

关键词: 随机蛙跳算法，亚启发式算法，工程优化，元算法，粒子群算法

Abstract: Shuffled Frog Leaping Algorithm (SFLA) is a population-based novel and effective mcta-heuristics computing method, which received increasing focuses from academic and engineering optimization fields in recent years. Since SFLA is a combination of Mcmctic Algorithm (MA) with strong Local Search (LS) ability and Particle Swarm Optimination (PSO) with good Global Search (GS) capability, it is of strong optimum-searching power and easy to be implemented. In this paper, the fundamental principles and framework of SFLA were described. Then, the related researches of SFLA in the current optimization and engineering fields were summed up. Lastly, the future perspectives of SFLA were presented.

Key words: Shuffled frog leaping algorithm, Meta-heuristics algorithm, Engineering optimization, Mcmetic algorithm,Particle swarm optimization

韩毅,蔡建湖,周根贵,李延来,林华珍,唐加福. 随机蛙跳算法的研究进展[J]. 计算机科学, 2010, 37(7): 16-19. https://doi.org/

HAN Yi,CAI Jian-hu,ZHOU Gen-gui,LI Yan-lai,LIN Hua-zhen,TANG Jia-fu. Advances in Shuffled Frog Leaping Algorithm[J]. Computer Science, 2010, 37(7): 16-19. https://doi.org/

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