Improved accelerated PSO algorithm for mechanical engineering optimization problems - Ben Guedria, Najeh (Applied soft computing, 2016)
Flowchart of the improved accelerated particle swarm optimization. •A new improved accelerated particle swarm optimization algorithm is proposed (IAPSO).•Individual particles memories are incorporated in order to increase swarm diversity.•Balance between exploration and exploitation is controlled through two selected functions.•IAPSO outperforms several recent meta-heuristic algorithms, in terms of accuracy and convergence speed.•New optimal solutions, for some benchmark engineering problems, are obtained. This paper introduces an improved accelerated particle swarm optimization algorithm (IAPSO) to solve constrained nonlinear optimization problems with various types of design variables. The main improvements of the original algorithm are the incorporation of the individual particles memories, in order to increase swarm diversity, and the introduction of two selected functions to control balance between exploration and exploitation, during search process. These modifications are used to update particles positions of the swarm. Performance of the proposed algorithm is illustrated through six benchmark mechanical engineering design optimization problems. Comparison of obtained computation results with those of several recent meta-heuristic algorithms shows the superiority of the IAPSO in terms of accuracy and convergence speed.