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Volume 13 | Issue 10 | Year 2026 | Article Id. IJEEE-V13I10P104 | DOI : https://doi.org/10.14445/23488379/IJEEE-V13I10P104

Multi-Objective Optimization Approach for Power Distribution Network Reconfiguration using Hybrid Particle Swarm and Genetic Algorithm

Edward Affedzi, Stephen Eduku

Received Revised Accepted Published
31 Jul 2026 08 Sep 2026 16 Sep 2026 28 Oct 2026

Citation :

Edward Affedzi, Stephen Eduku, "Multi-Objective Optimization Approach for Power Distribution Network Reconfiguration using Hybrid Particle Swarm and Genetic Algorithm," International Journal of Electrical and Electronics Engineering, vol. 13, no. 10, pp. 37-48, 2026. Crossref, https://doi.org/10.14445/23488379/IJEEE-V13I10P104

Abstract

Power distribution networks are a critical component of the electric power distribution system. Hence, numerous research has focus on distribution network reconfiguration systems, intending to minimize power losses and interruptions, namely, Customer Average Interruption Index (CAIDI), System Average Interruption Frequency Index (SAIFI), Energy Not Supplied (ENS), and Voltage Profile (VP) improvement, either by Genetic Algorithm (GA) or Particle Swarm Optimization (PSO). Nonetheless, existing research has not simultaneously employed a hybrid of GA and PSO optimization methods to ascertain which optimization method is better in terms of improving the reliability indices, such as CAIDI, SAIFI, and ENS, while minimizing power losses and improving the VP, thus leaving a gap in the literature. Hence, the proposed research work seeks to utilize the synergy of GA and PSO optimization techniques on the IEEE 33-bus and IEEE 69-bus systems with the aforementioned reliability targets to unearth which of the optimization techniques is better than the other. Meanwhile, the obtained results indicate that the GA exhibits a higher voltage level enhancement of 2.75% with a corresponding SAIFI reduction of 33%. Nonetheless, the PSO unveils superior or enhanced power loss reduction of 61.75%, ENS reduction of 48%, and CAIDI reduction of 30% with a corresponding computational speed of 0.24 seconds. Hence, based on the aforementioned analysis of the results, it can be concluded that the selection of the optimization algorithm depends on the network's specific operational requirements, as no single algorithm performs optimally across all parameter measurements. Besides, while the PSO performs excellently in terms of loss, ENS, and CAIDI reduction, the GA performs well in terms of voltage enhancement and SAIDI reduction.

Keywords

Distribution network, IEEE 33-bus reconfiguration, IEEE 69-bus reconfiguration, Multi-Objective, GA optimization, PSO optimization, Reliability Indices.

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