A population-based search engine optimization algorithm inspired by the behavior of bats

Authors: Zueva A.A.
Published in issue: #8(73)/2022
DOI: 10.18698/2541-8009-2022-8-816
Category: Informatics, Computer Engineering and Control | Chapter: System Analysis, Control, and Information Processing, Statistics
Keywords: metaheuristic search, bat algorithm, global optimization, population algorithm, efficiency research, group intelligence, structural optimization, minimization
Published: 07.10.2022

Metaheuristic algorithms are the most effective for solving undifferentiated multimodal and ravine minimization problems with high dimensional search space. The paper presents a study of one of such methods – an algorithm inspired by the behavior of bats. The method is implemented using the object-oriented C++ programming language, with a class of bats and a container class of the population. Analysis was performed based on ravine and multi-extremal functions using data on algorithm convergence and the best found values at different dimensions of the vector of varying parameters. A comparison is made between the method under study and modifications of the evolutionary strategy algorithm in terms of the smallest solutions found and the convergence rates of the algorithms at different dimensions. It is concluded that the bat algorithm is highly efficient on both ravine and multiextremal functions of different dimensions.

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