Open Access
Research Article
Dynamic inertia weight control using fitness metrics in particle swarm optimization algorithm
*Suchita AroraCorresponding authorsuchita.arora1@poornima.edu.inDepartment of Computer Science and Engineering Amity University Rajasthan; Department of Computer Science and Engineering Poornima UniversityDepartment of Computer Science and Engineering Poornima UniversityJaipur, Rajasthan, 303905, IndiaView full profile → , Sunil Kumarsunilkumar.hqr@gov.inDepartment of Computer Science and Engineering Amity University RajasthanDefence Research and Development Organization Near Metcalfe HouseJaipur, New Delhi, 110054, IndiaView full profile → , Sandeep Kumarsandeepkumar.hqr@gov.inDepartment of AI and Data Science Engineering CHRIST (Deemed to be University)Defence Research and Development Organization Near Metcalfe HouseNew Delhi, New Delhi, 110054, India0009-0005-7107-3914View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 01 Apr 2025
- Published Online:
- 01 May 2026
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2302
- Pages:
- 2119–2127
Abstract
Nature solves complex problems in a simple manner. This inspired researchers to develop a mathematical model to solve real-world problems that are not solvable or require high-end resources. The particle swarm optimization (PSO) is a nature-inspired techniques that have solved numerous engineering optimization problems. Sometimes PSO suffers from slow and premature convergence due to a high rate of exploration and exploitation, respectively. To overcome this problem, this paper introduced a modified PSO algorithm with dynamic inertia weight control with the help of fitness to improve convergence. The proposed approach uses dynamic inertia weight control in PSO to improve balancing in exploration and exploitation. Due to a fitness-driven approach, the proposed variant is adaptable for the population and avoids premature convergence, which is usually a problem basic PSO faces. The proposed approach is named as dynamic inertia weight control using fitness metrics in the PSO Algorithm (DIW-PSO). The experimental results demonstrated that DIW-PSO is best suited for multimodal high-dimensional problems and in a dynamic environment.
Keywords
Subject Classifications
Primary 68T01Secondary 68Q32
References
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