Title: An enhanced breeding swarms algorithm for high dimensional optimisations

Authors: Jon A. Hansen; Jørgen Sund; Dylan Tollemache; Ali Arefi; Ghavameddin Nourbakhsh

Addresses: School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia ' School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia ' School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia ' Discipline of Engineering and Energy, Murdoch University, Perth, Australia ' School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia

Abstract: This paper proposes a metaheuristic optimisation algorithm named enhanced breeding swarms (EBS), which combines the strengths of particle swarm optimisation (PSO) with those of genetic algorithm (GA). In addition, EBS introduces three modifications to the original breeding swarms to improve the performance and the accuracy of the optimisation algorithm. These modifications are applied on the acceptance criteria based on the improved glowworm swarm optimisation, velocity impact factor, and the mutation operator. The EBS algorithm is tested and compared against GA, PSO, and original BS algorithms, using unrotated and rotated six recognised optimisation benchmark functions. Results indicate that the EBS outperforms GA, PSO, and BS in most cases in terms of accuracy and speed of convergence, especially when the dimension of optimisation increases. As an application of the proposed EBS algorithm, a load flow analysis on a 6-bus network is performed, and the comparison results against another heuristic algorithm and the Newton-Raphson are reported.

Keywords: enhanced breeding swarms; EBS; particle swarm optimisation; PSO; generic algorithm; metaheuristic; improved glowworm swarm optimisation; IGSO; computational intelligence.

DOI: 10.1504/IJBIC.2020.107489

International Journal of Bio-Inspired Computation, 2020 Vol.15 No.3, pp.181 - 193

Accepted: 23 Dec 2019
Published online: 25 May 2020 *

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