Abstract—In this paper, a new PSO algorithm with adaptive inertia weight is introduced for global optimization. The objective of the study is to balance local search and global search abilities and alternate them through the algorithm progress. For this, an adaptive inertia weight is introduced using a feedback on particles' best positions. The inertia weight keeps varying to alternate exploration and exploitation. Tests are carried on a set of thirty test functions (the CEC 2014 benchmark functions) and compared with other settings of inertia weight. Results show that the new algorithm is very competitive mainly when increasing the dimension of the search space.
Index Terms—Algorithms, exploration and exploitation, inertia weight, particle swarm optimization.
S. Kessentini is with the Department of Mathematics, Faculty of Science of Sfax, University of Sfax, Route de Soukra km 4-BP 802, 3038 Sfax, Tunisia (e-mail: samehkessentini@gmail.com).
D. Barchiesi is with the Project Group for Automatic Mesh Generation and Advanced Methods - Gamma3 Project (UTT-INRIA), University of Technology of Troyes, 12 rue Marie Curie - BP 2060, 10010, Troyes Cedex, France (e-mail: dominique.barchiesi@utt.fr).
Cite: Sameh Kessentini and Dominique Barchiesi, "Particle Swarm Optimization with Adaptive Inertia Weight," International Journal of Machine Learning and Computing vol.5, no. 5, pp. 368-373, 2015.