计算机科学与探索 ›› 2022, Vol. 16 ›› Issue (1): 88-105.DOI: 10.3778/j.issn.1673-9418.2107028

• 综述·探索 • 上一篇    下一篇

若干新型智能优化算法对比分析研究

张九龙1,+(), 王晓峰1,2, 芦磊1, 牛鹏飞1   

  1. 1.北方民族大学 计算机科学与工程学院,银川 750021
    2.北方民族大学 图像图形智能处理国家民委重点实验室,银川 750021
  • 收稿日期:2021-06-10 修回日期:2021-08-18 出版日期:2022-01-01 发布日期:2021-08-26
  • 通讯作者: + E-mail: 1137073437@qq.com
  • 作者简介:张九龙(1997—),男,山东济宁人,硕士研究生,主要研究方向为算法分析与设计。
    王晓峰(1980—),男,甘肃会宁人,博士,副教授,硕士生导师,CCF会员,主要研究方向为算法分析与设计。
    芦磊(1995—),男,山东聊城人,硕士研究生,主要研究方向为算法分析与设计。
    牛鹏飞(1997—),男,宁夏银川人,硕士研究生,主要研究方向为算法分析与设计。
  • 基金资助:
    国家自然科学基金(62062001);宁夏自然科学基金(2020AAC03214);北方民族大学重大专项(ZDZX201901)

Analysis and Research of Several New Intelligent Optimization Algorithms

ZHANG Jiulong1,+(), WANG Xiaofeng1,2, LU Lei1, NIU Pengfei1   

  1. 1. School of Computer Science and Engineering, North Minzu University, Yinchuan 750021, China
    2. Key Laboratory for Intelligent Processing of Computer Images and Graphics of National Ethnic Affairs Commission of the PRC, North Minzu University, Yinchuan 750021, China
  • Received:2021-06-10 Revised:2021-08-18 Online:2022-01-01 Published:2021-08-26
  • About author:ZHANG Jiulong, born in 1997, M.S. candidate. His research interests include algorithm analysis and design.
    WANG Xiaofeng, born in 1980, Ph.D., associate professor, M.S. supervisor, member of CCF. His research interests include algorithm analysis and design.
    LU Lei, born in 1995, M.S. candidate. His res-earch interests include algorithm analysis and design.
    NIU Pengfei, born in 1997, M.S. candidate. His research interests include algorithm analysis and design.
  • Supported by:
    National Natural Science Foundation of China(62062001);Natural Science Foundation of Ningxia(2020AAC03214);Major Scientific Research Projects of North Minzu University(ZDZX201901)

摘要:

智能优化算法(IOA)指的是一类以自然界的生物生存进化过程或物理现象为算法原理,用于解决最优化问题的算法,较为知名的智能优化算法有遗传算法、粒子群算法、模拟退火算法等。智能优化算法属于启发式方法,广泛应用在解决最优化问题上,传统的群智能算法为解决一些实际问题提供了新思路。随着科学技术的进步和应用场景的改变,传统的智能优化算法在收敛速度、求解精度等方面已无法满足日益复杂的优化问题,因此不断有新的更高效的智能优化算法被提出。选取了近几年国内外提出的几种新型智能优化算法:蝴蝶优化算法(BOA)、飞蛾扑火算法(MFO)、正弦余弦优化算法(SCA)、蝗虫优化算法(GOA)、哈里斯鹰优化算法(HHO)、麻雀搜索算法(SSA)。阐述了各算法的基本原理、算法步骤、相关的改进策略及存在的优缺点。为客观对比各算法性能,进一步通过3种类型共21个测试函数及6个指标评价各算法性能,最后归纳总结各算法的特点并对智能优化算法的发展前景进行展望。

关键词: 智能优化算法(IOA), 蝴蝶优化算法(BOA), 飞蛾扑火算法(MFO), 正弦余弦优化算法(SCA), 蝗虫优化算法(GOA), 哈里斯鹰优化算法(HHO), 麻雀搜索算法(SSA)

Abstract:

Intelligent optimization algorithms (IOA) refer to a kind of algorithm that is used to solve optimization problems by imitating the survival and evolution process of natural creatures or physical phenomena as the algorithm principle. The well-known intelligent optimization algorithms include genetic algorithm, particle swarm optimization, simulated annealing algorithm, etc. Intelligent optimization algorithm is a heuristic method, which is widely used in solving optimization problems and provides some new ideas for solving some practical problems. With the advan-cement of science and technology and the increase in the complexity of application scenarios, traditional intelligent optimization algorithms can no longer satisfy optimization problems in terms of solving effects and accuracy. Therefore, new and more efficient intelligent optimization algorithms are constantly being proposed. Several new intelligent optimization algorithms have been proposed at home and abroad in recent years, such as butterfly optimization algorithm (BOA), moth-flame optimization (MFO), sine cosine algorithm (SCA), grasshopper optimization algorithm (GOA), Harris hawks optimization (HHO) and sparrow search algorithm (SSA). This paper describes the basic principle, algorithm steps, corresponding improvement strategies with advantages and disadvantages of each algorithm. To objectively compare the performance of each algorithm, this paper further evaluates the performance of each algorithm through 21 test functions of 3 types and 6 indicators. Finally, this paper summarizes the characteristics of the algorithm and prospects the development direction of intelligent optimization algorithm.

Key words: intelligent optimization algorithms (IOA), butterfly optimization algorithm (BOA), moth-flame optimi-zation (MFO), sine cosine algorithm (SCA), grasshopper optimization algorithm (GOA), Harris hawks optimization (HHO), sparrow search algorithm (SSA)

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