Abstract
To overcome the shortcomings of the lightning attachment procedure optimization (LAPO) algorithm, such as premature convergence and slow convergence speed, an enhanced lightning attachment procedure optimization (ELAPO) algorithm was proposed in this paper. In the downward leader movement, the idea of differential evolution was introduced to speed up population convergence; in the upward leader movement, by superimposing vectors pointing to the average individual, the individual updating mode was modified to change the direction of individual evolution, avoid falling into local optimum, and carry out a more fine local information search; in the performance enhancement stage, opposition-based learning (OBL) was used to replace the worst individuals, improve the convergence rate of population, and increase the global exploration capability. Finally, 16 typical benchmark functions in CEC2005 are used to carry out simulation experiments with LAPO algorithm, four improved algorithms, and ELAPO. Experimental results showed that ELAPO obtained the better convergence velocity and optimization accuracy.
Highlights
The optimization problems in engineering field can be expressed by mathematical models and solved by mathematical or numerical methods
In order to overcome the shortcomings of slow computation speed and low reliability of traditional numerical methods in solving engineering problems, researchers have proposed a large number of meta-heuristic algorithms [1,2,3], which are widely used to solve complex problems in industries and services, from planning to production management and even engineering [4,5,6,7,8]
The results show that the lightning attachment procedure optimization (LAPO) algorithm has obvious advantages in convergence speed and accuracy, but similar to other swarm intelligence optimization algorithms, the LAPO algorithm has some shortcomings, such as slow convergence speed in the middle and later stages of the evolutionary search, and easy to fall into local optimum when solving high-dimensional and multi-peak problems
Summary
The optimization problems in engineering field can be expressed by mathematical models and solved by mathematical or numerical methods. Foroughi Nematollahi and others, inspired by the nature of lightning attachment process, proposed the lightning attachment procedure optimization (LAPO) [26] It is compared with nine algorithms, such as the particle swarm optimization (PSO), differential evolution (DE), gray wolf optimizer (GWO), and cuckoo search algorithms (CSA), on four sets of 29 standard test functions. In the process of performance improvement, the dynamic opposition-based learning (OBL) [27] method is used to replace the original update operation. Experiments show that this method can effectively improve the convergence speed of the algorithm.
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