Barnacles Mating Optimizer: A new bio-inspired algorithm for solving engineering optimization problems

被引：258
|
作者：
Sulaiman, Mohd Herwan [1 ]
Mustaffa, Zuriani [2 ]
Saari, Mohd Mawardi [1 ]
Daniyal, Hamdan [1 ]
机构：
[1] UMP, Fac Elect & Elect Engn Technol, Pekan Pahang 26600, Malaysia
[2] UMP, Fac Comp, Gambang Pahang 26300, Malaysia
来源：
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE | 2020年 / 87卷
关键词：
Barnacles Optimization Algorithm; Benchmarked functions; Loss minimization; Meta-heuristic technique; Optimal reactive power dispatch; LEARNING-BASED OPTIMIZATION; LEAGUE COMPETITION ALGORITHM; META-HEURISTIC ALGORITHM; GLOBAL OPTIMIZATION; DESIGN; EVOLUTION; SYSTEMS;
D O I：
10.1016/j.engappai.2019.103330
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper presents a novel bio-inspired optimization algorithm namely the Barnacles Mating Optimizer (BMO) algorithm to solve optimization problems. The proposed algorithm mimics the mating behaviour of barnacles in nature for solving optimization problems. The BMO is first benchmarked on a set of 23 mathematical functions to test the characteristics of BMO in finding the optimal solutions. It is then applied to optimal reactive power dispatch (ORPD) problem to verify the reliability and efficiency of BMO. Extensive comparative studies with other algorithms are conducted and from the simulation results, it is observed that BMO generally provides better results and exhibits huge potential of BMO in solving real optimization problems.
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页数：13
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