A novel life choice-based optimizer

被引:36
作者
Khatri, Abhishek [1 ]
Gaba, Akash [1 ]
Rana, K. P. S. [1 ]
Kumar, Vineet [1 ]
机构
[1] Netaji Subhas Univ Technol, Dept Instrumentat & Control Engn, Sect 3, New Delhi 110078, India
关键词
Optimization; Optimization techniques; Metaheuristic algorithm; Metaheuristics-constrained optimization; Life choice-based optimizer; HEURISTIC OPTIMIZATION; SWARM OPTIMIZATION; DIFFERENTIAL EVOLUTION; ALGORITHM; SEARCH; DESIGN; MODEL;
D O I
10.1007/s00500-019-04443-z
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a novel metaheuristic algorithm named as life choice-based optimizer (LCBO) developed on the typical decision-making ability of humans to attain their goals while learning from fellow members. LCBO is investigated on 29 popular benchmark functions which included six CEC-2005 functions, and its performance has been benchmarked against seven optimization techniques including recent ones. Further, different abilities of LCBO optimization algorithm such as exploitation, exploration and local minima avoidance were also investigated and have been reported. In addition to this, scalability is tested for several benchmark functions where dimensions have been varied till 200. Furthermore, two engineering optimization benchmark problems, namely pressure vessel design and cantilever beam design, were also optimized using LCBO and the results have been compared with recently reported other algorithms. The obtained comparative results in all the above-mentioned experimentations revealed the clear superiority of LCBO over the other considered metaheuristic optimization algorithms. Therefore, based on the presented investigations, it is concluded that LCBO is a potential optimizer for engineering problems.
引用
收藏
页码:9121 / 9141
页数:21
相关论文
共 105 条
[1]  
Abbass H, 2002, I ELECT ELECT ENG, V1, P207
[2]   Electromagnetic field optimization: A physics-inspired metaheuristic optimization algorithm [J].
Abedinpourshotorban, Hosein ;
Shamsuddin, Siti Mariyam ;
Beheshti, Zahra ;
Jawawi, Dayang N. A. .
SWARM AND EVOLUTIONARY COMPUTATION, 2016, 26 :8-22
[3]   Grenade Explosion Method-A novel tool for optimization of multimodal functions [J].
Ahrari, Ali ;
Atai, Ali A. .
APPLIED SOFT COMPUTING, 2010, 10 (04) :1132-1140
[4]   ACROA: Artificial Chemical Reaction Optimization Algorithm for global optimization [J].
Alatas, Bilal .
EXPERT SYSTEMS WITH APPLICATIONS, 2011, 38 (10) :13170-13180
[5]  
[Anonymous], 2006, GENE EXPRESSION PROG
[6]   Bird mating optimizer: An optimization algorithm inspired by bird mating strategies [J].
Askarzadeh, Alireza .
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2014, 19 (04) :1213-1228
[7]  
Atashpaz-Gargari E, 2007, IEEE C EVOL COMPUTAT, P4661, DOI 10.1109/cec.2007.4425083
[8]  
Baluja S., 1994, Population-based incremental learning: a method for integrating genetic search based function optimization and competitive learning
[9]   Most Valuable Player Algorithm: a novel optimization algorithm inspired from sport [J].
Bouchekara, H. R. E. H. .
OPERATIONAL RESEARCH, 2020, 20 (01) :139-195
[10]   Symbiotic Organisms Search: A new metaheuristic optimization algorithm [J].
Cheng, Min-Yuan ;
Prayogo, Doddy .
COMPUTERS & STRUCTURES, 2014, 139 :98-112