An Adaptive Individual Inertia Weight Based on Best, Worst and Individual Particle Performances for the PSO Algorithm

Due to the growing need for metaheuristics with features that allow their implementation for real-time problems, this paper proposes an adaptive individual inertia weight in each iteration considering global and individual analysis, i.e., the best, worst and individual particles’ performance. As a result, the proposed adaptive individual inertia weight presents faster convergence for the Particle Swarm Optimization (PSO) algorithm when compared to other inertia mechanisms. The proposed algorithm is also suitable for real-time problems when the actual optimum is difficult to be attained, since a feasible and optimized solution is found in comparison to an initial solution. In this sense, the PSO with the proposed adaptive individual inertia weight was tested using eight benchmark functions in the continuous domain. The proposed PSO was compared to other three algorithms, reaching better optimized results in six benchmark functions at the end of 2000 iterations. Moreover, it is noteworthy to mention that the proposed adaptive individual inertia weight features rapid convergence for the PSO algorithm in the first 1000 iterations.