Document Type : Research Paper
Authors
Department of Mathematics, Jaypee Institute of Information Technology Noida, India
Abstract
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.
Keywords
Main Subjects
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