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Published in: Natural Computing 3/2021

05-01-2021

Improving convergence in swarm algorithms by controlling range of random movement

Authors: Reshu Chaudhary, Hema Banati

Published in: Natural Computing | Issue 3/2021

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Abstract

Swarm intelligence algorithms are stochastic algorithms, i.e. they perform some random movement. This random movement imparts the algorithms with exploration capabilities and allows them to escape local optima. Exploration at the start of execution helps with thorough inspection of the search/solution space. However, as the algorithm progresses, the focus should ideally shift from exploration to exploitation. This shift would help the algorithm to enhance existing solutions and improve its convergence capabilities. Hence if the range of random movement is not kept in check, it may limit an algorithm’s convergence capabilities and overall efficiency. To ensure that the convergence of an algorithm is not compromised, an improved search technique to reduce range of uniform random movement was recently proposed for bat algorithm. Uniform distribution and levy distribution are the most commonly used random distributions in swarm algorithms. In this paper, the applicability of the improved search technique over different swarm algorithms employing uniform and levy distributions, as well as Cauchy distribution has been studied. The selected algorithms are firefly algorithm, cuckoo search algorithm, moth search algorithm, whale optimization algorithm, earthworm optimization algorithm and elephant herding optimization algorithm. The resultant variants of each of these algorithms show improvement upon inclusion of the improved search technique. Hence results establish that the improved search technique has positive influence over swarm algorithms employing different random distributions.

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Metadata
Title
Improving convergence in swarm algorithms by controlling range of random movement
Authors
Reshu Chaudhary
Hema Banati
Publication date
05-01-2021
Publisher
Springer Netherlands
Published in
Natural Computing / Issue 3/2021
Print ISSN: 1567-7818
Electronic ISSN: 1572-9796
DOI
https://doi.org/10.1007/s11047-020-09826-y

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