This project presents the source codes of a novel meta-heuristic optimization algorithm named Guided Adaptive Search-based Particle Swarm Optimization (GuASPSO) algorithm. In this algorithm, the personal best particles are all divided into a linearly decreasing number of clusters. Then, the unique global best guide of a given particle located at a cluster is obtained as the weighted average calculated over other clusters’ best particles. Since the clustered particles are being well distributed over the whole search space in the clustering process, two desirable goals are fulfilled during the optimization process: (1) there would be a moderate distance between each particle and its unique global best guide, contributing the particles neither to be trapped in local optima nor engaged in a drift leading to lose diversity in the search space; and (2) all regions in the search space are recognized, whether these regions are more-densely populated regions or less-densely populated regions. Therefore, all regions in the search space are taken into account to guide the particles as a result of defining and designating a representative particle for each region to better maintain the diversity of the particles in the search space. This ability is endowed to GuASPSO to cover the major shortcoming of the PSO or other meta-heuristics to truly and toughly keep diversity among the search agents, especially when solving the large-scale optimization problems. In this approach, the number of clusters is high at the early iterations and is gradually decreased by lapse of iterations to less stress the diversity factor and further stress the fitness role to cause the particles to better converge to the optimal point. Holding this balance between global and personal bests’ role to attract the particles, on the one hand and between convergence and diversity, on the other hand, can hold a better exploration–exploitation balance in the proposed algorithm.
For further information about this algorithm, please refer to the following reference:
Rezaei, F., Safavi, H.R. GuASPSO: a new approach to hold a better exploration–exploitation balance in PSO algorithm. Soft Comput 24, 4855–4875 (2020). https://doi.org/10.1007/s00500-019-04240-8
Please cite this article upon using the source code.