Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Experimental Analysis of Time Complexity and Solution Quality of Swarm Intelligence Algorithm

Version 1 : Received: 23 July 2020 / Approved: 24 July 2020 / Online: 24 July 2020 (14:16:10 CEST)

How to cite: Ajuji, M.; Abubakar, A.; Emmanuel, D.U. Experimental Analysis of Time Complexity and Solution Quality of Swarm Intelligence Algorithm. Preprints 2020, 2020070593 (doi: 10.20944/preprints202007.0593.v1). Ajuji, M.; Abubakar, A.; Emmanuel, D.U. Experimental Analysis of Time Complexity and Solution Quality of Swarm Intelligence Algorithm. Preprints 2020, 2020070593 (doi: 10.20944/preprints202007.0593.v1).

Abstract

Nature-inspired algorithms are very popular tools for solving optimization problems inspired by nature. However, there is no guarantee that optimal solution can be obtained using a randomly selected algorithm. As such, the problem can be addressed using trial and error via the use of different optimization algorithms. Therefore, the proposed study in this paper analyzes the time-complexity and efficacy of some nature-inspired algorithms which includes Artificial Bee Colony, Bat Algorithm and Particle Swarm Optimization. For each algorithm used, experiments were conducted several times with iterations and comparative analysis was made. The result obtained shows that Artificial Bee Colony outperformed other algorithms in terms of the quality of the solution, Particle Swarm Optimization is time efficient while Artificial Bee Colony yield a worst case scenario in terms of time complexity.

Subject Areas

artificial bee colony; bat; particle swarm; optimization and Opytimizer

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