AEFA: Artificial electric field algorithm for global optimization

被引:227
|
作者
Anita [1 ]
Yadav, Anupam [2 ]
机构
[1] Natl Inst Technol Uttarakhand, Dept Sci & Humanites, Srinagar 246174, Uttarakhand, India
[2] Dr BR Ambedkar Natl Inst Technol Jalandhar, Dept Math, Jalandhar 144011, Punjab, India
关键词
Optimization; Soft computing; Artificial intelligence; Electric force; DIFFERENTIAL EVOLUTION; SWARM OPTIMIZER; MUTATION;
D O I
10.1016/j.swevo.2019.03.013
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Electrostatic Force is one of the fundamental force of physical world. The concept of electric field and charged particles provide us a strong theory for the working force of attraction or repulsion between two charged particles. In the recent years many heuristic optimization algorithms are proposed based on natural phenomenon. The current article proposes a novel artificial electric field algorithm (AEFA) which inspired by the Coulomb's law of electrostatic force. The AEFA has been designed to work as a population based optimization algorithm, the concept of charge is extended to fitness value of the population in an innovative way. The proposed AEFA has been tested over a newly and challenging state-of-the-art optimization problems. The theoretical convergence of the proposed AEFA is also established along with statistical validation and comparison with recent state-of-the-art optimization algorithms. The presented study and findings suggests that the proposed AEFA as an outstanding optimization algorithms for non linear optimization.
引用
收藏
页码:93 / 108
页数:16
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