计算机科学
库仑定律
趋同(经济学)
领域(数学)
元启发式
数学优化
库仑
启发式
算法
最优化问题
国家(计算机科学)
人口
人工智能
数学
物理
量子力学
社会学
电子
人口学
经济
经济增长
纯数学
作者
Anita Anita,Anupam Yadav
标识
DOI:10.1016/j.swevo.2019.03.013
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.
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