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Bacterial Foraging Optimization Algorithm with Dynamically Reduced Setting of Migration Probability

Published: 14 October 2021 Publication History

Abstract

In the application of bacterial foraging optimization algorithm, the parameter setting has a very important effect on the performance of the algorithm. In order to solve the problems of low convergence accuracy and complex parameter setting of bacterial foraging optimization algorithm, the bacterial foraging optimization algorithm with dynamically reduced setting of migration probability was proposed by analyzing the function of each parameter in bacterial foraging optimization algorithm and considering the better balance between global search and local search ability. In the initial stage of the algorithm, the migration probability is relatively large, and the global search ability is relatively strong. With the progress of the algorithm, the migration probability is reduced, and the local search ability is enhanced, so that the algorithm can obtain a more accurate solution. At the same time, in order to avoid the algorithm falling into the local solution, the parameter of migration probability is processed in sections in the process of dynamic parameter setting with the idea of simulated annealing algorithm. Classical single-peak and multi-peak reference functions are selected to test the effect of the improved algorithm, and the experimental results verify that the improved algorithm has higher convergence accuracy.

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  • (2023)Intelligent proportional-integral-derivate controller using metaheuristic approach via crow search algorithm for vibration suppression of flexible plate structureJournal of Low Frequency Noise, Vibration and Active Control10.1177/1461348423120394643:1(560-574)Online publication date: 5-Oct-2023

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ICCMS '21: Proceedings of the 13th International Conference on Computer Modeling and Simulation
June 2021
276 pages
ISBN:9781450389792
DOI:10.1145/3474963
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 October 2021

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  1. bacterial foraging optimization algorithm
  2. migration probability
  3. swarm intelligence algorithm

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  • (2023)Intelligent proportional-integral-derivate controller using metaheuristic approach via crow search algorithm for vibration suppression of flexible plate structureJournal of Low Frequency Noise, Vibration and Active Control10.1177/1461348423120394643:1(560-574)Online publication date: 5-Oct-2023

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