A novel hybrid meta-heuristic algorithm for optimization problems. Issue 3 (21st September 2018)

• Record Type: Journal Article
• Authors: Gai, Wendong; Qu, Chengzhi; Liu, Jie; Zhang, Jing
• Is Part Of: Systems science & control engineering. Volume 6:Issue 3(2018)
• Page Start: 64
• Page End: 73
• Publication Date: 2018-09-21
• Subjects: Hybrid meta-heuristic algorithm -- grey wolf optimization (GWO) -- symbiotic organisms search (SOS) -- genetic algorithm (GA) -- fractional-order controller; System theory -- Periodicals; Automatic control -- Periodicals; 003.05
• DOI: 10.1080/21642583.2018.1531359
• Languages: English
• ISSNs: 2164-2583

Abstract

ABSTRACT: This paper presents a novel hybrid meta-heuristic algorithm called HMGSG to solve the optimization problems. In the proposed HMGSG algorithm, a spiral-shaped path for grey wolf optimization (GWO) is used to ensure both the faster convergence rate and diversity. The mutualism phase of symbiotic organisms search (SOS) is introduced and modified with the adaptive benefit factors to optimize the ability of exploitation. The stud genetic algorithm (GA) is introduced into the HMGSG to promote convergence. The numerical experiment results show that the performance of HMGSG is superior to that of the GWO, SOS and GA. In addition, the HMGSG algorithm is used to optimize the fractional-order PID controller parameters for roll attitude control of UAV. And the simulation results show the effectiveness of this algorithm.