Abstract
Lightning attachment procedure optimization (LAPO) is a new global optimization algorithm inspired by the attachment procedure of lightning in nature. However, similar to other metaheuristic algorithms, LAPO also has its own disadvantages. To obtain better global searching ability, an enhanced version of LAPO called ELAPO has been proposed in this paper. A quasi-opposition-based learning strategy is incorporated to improve both exploration and exploitation abilities by considering an estimate and its opposite simultaneously. Moreover, a dimensional search enhancement strategy is proposed to intensify the exploitation ability of the algorithm. 32 benchmark functions including unimodal, multimodal, and CEC 2014 functions are utilized to test the effectiveness of the proposed algorithm. Numerical results indicate that ELAPO can provide better or competitive performance compared with the basic LAPO and other five state-of-the-art optimization algorithms.
Highlights
Optimization problems can be found in many engineering application domains and scientific fields which have a complex and nonlinear nature
Evolutionary algorithms are generic population-based metaheuristics, which imitate the evolutionary behavior of biology in nature such as reproduction, mutation, recombination, and selection. e first generation starts with randomly initialized solutions and further evolves over successive generations. e best individual among the whole population in the final evolution is considered to be the optimization solution
The performance of ELPAO is evaluated by means of 32 different benchmark functions and the results
Summary
Optimization problems can be found in many engineering application domains and scientific fields which have a complex and nonlinear nature. Due to the limitations of classical approaches, many natural-inspired stochastic optimization algorithms have been proposed to conduct global optimization problems in the last two decades. Such optimization algorithms were commonly simple and easy to implement, and these features make the possibility to solve highly complex optimization problems. Ese metaheuristics can be roughly classified into three categories: evolutionary algorithms, swarm intelligence, and physical-based algorithms. Evolutionary algorithms are generic population-based metaheuristics, which imitate the evolutionary behavior of biology in nature such as reproduction, mutation, recombination, and selection. E best individual among the whole population in the final evolution is considered to be the optimization solution. Some of the popular evolutionary algorithms are genetic algorithm (GA) [1], genetic programming (GP) [2], evolution strategy (ES) [3], differential evolution (DE) algorithm [4], and biogeography-based optimizer (BBO) [5]
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