TY  - JOUR
AB  - Purpose– Meta-heuristic algorithms are efficient in achieving the optimal solution for engineering problems. Hybridization of different algorithms may enhance the quality of the solutions and improve the efficiency of the algorithms. The purpose of this paper is to propose a novel, robust hybrid meta-heuristic optimization approach by adding differential evolution (DE) mutation operator to the accelerated particle swarm optimization (APSO) algorithm to solve numerical optimization problems. Design/methodology/approach– The improvement includes the addition of DE mutation operator to the APSO updating equations so as to speed up convergence. Findings– A new optimization method is proposed by introducing DE-type mutation into APSO, and the hybrid algorithm is called differential evolution accelerated particle swarm optimization (DPSO). The difference between DPSO and APSO is that the mutation operator is employed to fine-tune the newly generated solution for each particle, rather than random walks used in APSO. Originality/value– A novel hybrid method is proposed and used to optimize 51 functions. It is compared with other methods to show its effectiveness. The effect of the DPSO parameters on convergence and performance is also studied and analyzed by detailed parameter sensitivity studies.
VL  - 31
IS  - 7
SN  - 0264-4401
DO  - 10.1108/EC-10-2012-0232
UR  - https://doi.org/10.1108/EC-10-2012-0232
AU  - Wang Gai-Ge
AU  - Hossein Gandomi Amir
AU  - Yang Xin-She
AU  - Hossein Alavi Amir
PY  - 2014
Y1  - 2014/01/01
TI  - A novel improved accelerated particle swarm optimization algorithm for global numerical optimization
T2  - Engineering Computations
PB  - Emerald Group Publishing Limited
SP  - 1198
EP  - 1220
Y2  - 2024/04/20
ER  -