Earthworm optimisation algorithm: a bio-inspired metaheuristic algorithm for global optimisation problems

被引：364
|
作者：
Wang, Gai-Ge [1 ,2 ,3 ]
Deb, Suash [4 ]
Coelho, Leandro dos Santos [5 ,6 ]
机构：
[1] Ocean Univ China, Dept Comp Sci & Technol, Qingdao 266100, Peoples R China
[2] Northeast Normal Univ, Inst Algorithm & Big Data Anal, Changchun 130117, Jilin, Peoples R China
[3] Northeast Normal Univ, Sch Comp Sci & Informat Technol, Changchun 130117, Jilin, Peoples R China
[4] Cambridge Inst Technol, Ranchi 835103, Jharkhand, India
[5] Pontifical Catholic Univ Parana PUCPR, Ind & Syst Engn Grad Program PPGEPS, Curitiba, Parana, Brazil
[6] Fed Univ Parana UFPR, Polytech Ctr, Dept Elect Engn, Elect Engn Grad Program PPGEE, Curitiba, Parana, Brazil
来源：
INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION | 2018年 / 12卷 / 01期
基金：
中国国家自然科学基金;
关键词：
earthworm optimisation algorithm; EWA; evolutionary computation; benchmark functions; improved crossover operator; Cauchy mutation; CM; bio-inspired metaheuristic; global optimisation; swarm intelligence; evolutionary algorithms; KRILL HERD ALGORITHM; BIOGEOGRAPHY-BASED OPTIMIZATION; PARTICLE SWARM OPTIMIZATION; ARTIFICIAL BEE COLONY; MODEL;
D O I：
10.1504/IJBIC.2015.10004283
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Earthworms can aerate the soil with their burrowing action and enrich the soil with their waste nutrients. Inspired by the earthworm contribution in nature, a new kind of bio-inspired metaheuristic algorithm, called earthworm optimisation algorithm (EWA), is proposed in this paper. The EWA method is inspired by the two kinds of reproduction (Reproduction 1 and Reproduction 2) of the earthworms. Reproduction 1 generates only one offspring by itself. Reproduction 2 is to generate one or more than one offspring at one time, and this can successfully be done by nine improved crossover operators. In addition, Cauchy mutation (CM) is added to EWA method. Nine different EWA methods with one, two and three offsprings based on nine improved crossover operators are respectively proposed. The results show that EWA23 performs the best and it can find the better fitness on most benchmarks than others.
引用
收藏
页码：1 / 22
页数：22
相关论文
共 50 条
  • [1] Earthworm optimisation algorithm: A bio-inspired metaheuristic algorithm for global optimisation problems
    Wang G.-G.
    Deb S.
    Dos Santos Coelho L.
    Wang, Gai-Ge (gaigewang@163.com), 2018, Inderscience Enterprises Ltd. (12) : 1 - 22
  • [2] Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems
    Gai-Ge Wang
    Memetic Computing, 2018, 10 : 151 - 164
  • [3] Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems
    Wang, Gai-Ge
    MEMETIC COMPUTING, 2018, 10 (02) : 151 - 164
  • [4] A bio-inspired evolutionary algorithm: allostatic optimisation
    Osuna-Enciso, Valentin
    Cuevas, Erik
    Oliva, Diego
    Sossa, Humberto
    Perez-Cisneros, Marco
    INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION, 2016, 8 (03) : 154 - 169
  • [5] A new metaheuristic optimisation algorithm motivated by elephant herding behaviour
    Wang, Gai-Ge
    Deb, Suash
    Gao, Xiao-Zhi
    Coelho, Leandro dos Santos
    INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION, 2016, 8 (06) : 394 - 409
  • [6] A new bio-inspired optimisation algorithm: Bird Swarm Algorithm
    Meng, Xian-Bing
    Gao, X. Z.
    Lu, Lihua
    Liu, Yu
    Zhang, Hengzhen
    JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE, 2016, 28 (04) : 673 - 687
  • [7] Assessment of bio-inspired metaheuristic optimisation algorithms for estimating soil temperature
    Moazenzadeh, Roozbeh
    Mohammadi, Babak
    GEODERMA, 2019, 353 : 152 - 171
  • [8] Orca predation algorithm: A novel bio-inspired algorithm for global optimization problems
    Jiang, Yuxin
    Wu, Qing
    Zhu, Shenke
    Zhang, Luke
    EXPERT SYSTEMS WITH APPLICATIONS, 2022, 188
  • [9] Starfish optimization algorithm (SFOA): a bio-inspired metaheuristic algorithm for global optimization compared with 100 optimizers
    Changting Zhong
    Gang Li
    Zeng Meng
    Haijiang Li
    Ali Riza Yildiz
    Seyedali Mirjalili
    Neural Computing and Applications, 2025, 37 (5) : 3641 - 3683
  • [10] Biased Eavesdropping Particles: A Novel Bio-inspired Heterogeneous Particle Swarm Optimisation Algorithm
    Varna, Fevzi Tugrul
    Husbands, Phil
    2021 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI 2021), 2021,
12345