An ameliorated particle swarm optimizer for solving numerical optimization problems

Although the particle swarm optimizer (PSO) has been widely used to address various complicated engineering problems, it is likely to suffer lack of diversity and ineffectiveness of balance between the global search ability and the local search ability in the search process. In this paper, we report an innovative and improved optimization method called ameliorated particle swarm optimizer (A-PSO), which is different from the original PSO algorithm and its variants in parameter update and the position generation of each particle. In A-PSO, the nonlinear dynamic acceleration coefficients, logistic map and a modified particle position update approach are introduced in PSO to improve the solution quality and accelerate the global convergence rate. Twenty well-known numerical optimization functions are adopted to evaluate the effectiveness of the proposed method and it is illustrated that, for most numerical optimization problems, the convergence performance and search accuracy of the A-PSO method are superior to the similar heuristic optimization algorithms and other well-known PSO variants. Namely, the proposed A-PSO technique has a faster convergence rate and is more stable than other PSO variants and similar population-based methods for almost all numerical optimization problems. Therefore, the A-PSO method is successfully used as a new optimization technique for solving numerical optimization problems. (C) 2018 Elsevier B.V. All rights reserved.