Abstract
ABSTRACTThis 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.
Highlights
Optimization is an important research field, and solving optimization problems is a challenging issue
The results show that the fractional PID controller (FOPID) controller has the better dynamic performance and anti-disturbance ability than the traditional proportional integral derivative (PID) controller
This paper presents a novel hybrid method named HMGSG algorithm, which provides an interesting combination of modified grey wolf optimization (GWO), symbiotic organisms search (SOS) and genetic algorithm (GA)
Summary
Optimization is an important research field, and solving optimization problems is a challenging issue. Jiang, Ji, and Shen (2014) established a novel optimization algorithm HPSO–GSA to solve the economic emission load dispatch (EELD) problems They achieve an efficient balance between the social essence of PSO and the Newtonian gravity concept of GSA. Mahi, Baykan, and Kodaz (2015) presented a hybrid algorithm with PSO, ACO and 3-Opt heuristic method to solve the Travelling Salesman Problem (TSP) This combination reduces the probability of falling in local minimization and premature convergence. Soleimani and Kannan (2015) proposed a new hybrid meta-heuristic algorithm that is made up of the GA and PSO They analyse this two algorithm’s superiority and weaknesses and attempt to modify the traditional genetic algorithm by the particle swarm optimization.
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