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Experimental Study of Swarm Migration Algorithms on Stochastic and Global Optimisation Problem

Experimental Study of Swarm Migration Algorithms on Stochastic and Global Optimisation Problem

Stephen Opoku Oppong, Benjamin Ghansah, Evans Baidoo, Wilson Osafo Apeanti, Daniel Danso Essel
Copyright: © 2022 |Volume: 14 |Issue: 1 |Pages: 26
ISSN: 2637-7888|EISSN: 2637-7896|EISBN13: 9781683183471|DOI: 10.4018/IJDAI.296389
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MLA

Oppong, Stephen Opoku, et al. "Experimental Study of Swarm Migration Algorithms on Stochastic and Global Optimisation Problem." IJDAI vol.14, no.1 2022: pp.1-26. http://doi.org/10.4018/IJDAI.296389

APA

Oppong, S. O., Ghansah, B., Baidoo, E., Apeanti, W. O., & Essel, D. D. (2022). Experimental Study of Swarm Migration Algorithms on Stochastic and Global Optimisation Problem. International Journal of Distributed Artificial Intelligence (IJDAI), 14(1), 1-26. http://doi.org/10.4018/IJDAI.296389

Chicago

Oppong, Stephen Opoku, et al. "Experimental Study of Swarm Migration Algorithms on Stochastic and Global Optimisation Problem," International Journal of Distributed Artificial Intelligence (IJDAI) 14, no.1: 1-26. http://doi.org/10.4018/IJDAI.296389

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Abstract

Complex computational problems are occurrences in our daily lives that needs to be analysed effectively in order to make meaningful and informed decision. This study performs empirical analysis into the performance of six optimisation algorithms based on swarm intelligence on nine well known stochastic and global optimisation problems, with the aim of identifying a technique that returns an optimum output on some selected benchmark techniques. Extensive experiments show that, Multi-Swarm and Pigeon inspired optimisation algorithm outperformed Particle Swarm, Firefly and Evolutionary optimizations in both convergence speed and global solution. The algorithms adopted in this paper gives an indication of which algorithmic solution presents optimal results for a problem in terms of quality of performance, precision and efficiency.

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