University of Sistan and Baluchestan Iranian Journal of Fuzzy Systems 1735-0654 23 4 2026 08 01 A Competitive Framework for Structured Particle Swarm Optimization using Fuzzy Logic 145 170 10031 10.22111/ijfs.2026.54604.9673 EN Shipra Vatsa Department of Mathematics, Jaypee Institute of Information Technology Noida, India Neha Singhal Department of Mathematics, Jaypee Institute of Information Technology Noida, India Alka Tripathi Department of Mathematics, Jaypee Institute of Information Technology Noida, India Journal Article 2026 02 06 Particle Swarm Optimization (PSO) is one of the most popular metaheuristic algorithm used to solve complex real-world optimization problems to date. The major drawback of PSO is its weak exploration ability, which leads the algorithm prematurely to a local optimum. To address this issue, we propose an improved particle swarm optimization with an intelligent multi-swarm strategy (PSO-IMS), inspired by powerful PSO variants FHPSO, HPSO-ALS, and ALPSO. To ensure uniform dispersion of particles during initialization, low-discrepancy Sobol sequence has been used in this article. PSO-IMS uses the worst performing particle in the swarm, called gworst to enhance the algorithm’s exploration ability in the initial stages of the search. The method integrates a fuzzy-logic based parameter adaptation mechanism to manage uncertainty and dynamically adjust key control parameters, thereby reflecting a more realistic hierarchical decision process. To validate the performance of PSO-IMS, it has been tested on seventeen benchmark functions along with CEC 2013 test suite, and compared against four powerful PSO variants, in which our proposed algorithm showed satisfactory results. As an application of the proposed algorithm, it has been applied to the traveling salesman problem with thirty-one cities as well as an Unmanned Aerial Vehicles (UAV) path planning problem.

https://ijfs.usb.ac.ir/article_10031_e6861bf8e6786893ee8a1b30eb929635.pdf