Abstract
Particle swarm optimization (PSO) has been proven to show good performance for solving various optimization problems. However, it tends to suffer from premature stagnation and loses exploration ability in the later evolution period when solving complex problems. This paper presents a sequential hybrid particle swarm optimization and gravitational search algorithm with dependent random coefficients called HPSO-GSA, which first incorporates the gravitational search algorithm (GSA) with the PSO by means of a sequential operating mode and then adopts three learning strategies in the hybridization process to overcome the aforementioned problem. Specifically, the particles in the HPSO-GSA enter into the PSO stage and update their velocities by adopting the dependent random coefficients strategy to enhance the exploration ability. Then, the GSA is incorporated into the PSO by using fixed iteration interval cycle or adaptive evolution stagnation cycle strategies when the swarm drops into local optimum and fails to improve their fitness. To evaluate the effectiveness and feasibility of the proposed HPSO-GSA, the simulations were conducted on benchmark test functions. The results reveal that the HPSO-GSA exhibits superior performance in terms of accuracy, reliability, and efficiency compared to PSO, GSA, and other recently developed hybrid variants.
Highlights
As many real-world optimization problems become increasingly complex, traditional optimization algorithms cannot sufficiently satisfy the problem requirements and better effective optimization algorithms are needed
To assess the performance of the hybrid particle swarm optimization (HPSO)-gravitational search algorithm (GSA) compared to seven other optimization algorithms, the simulation experiments based on different measures are presented. ese measures provide the ability to evaluate algorithms from different points
Based on the aforementioned performance evaluation and statistical results, we conclude that the proposed hybrid algorithm performs better overall in the involved test functions compared to particle swarm optimization (PSO), GSA, DEPSO, GAPSO, DEGSA, Genetic algorithm (GA)-GSA, and PSOGSA
Summary
As many real-world optimization problems become increasingly complex, traditional optimization algorithms cannot sufficiently satisfy the problem requirements and better effective optimization algorithms are needed. There is a lot of room for improvement in finding the better optimization algorithm Another issue is how to balance the exploration/exploitation search ability for a single metaheuristic algorithm including PSO or GSA. To further improve the respective drawbacks of PSO and GSA, a novel combination strategy that integrates PSO with GSA, sequential hybrid particle swarm optimization and gravitational search algorithm with dependent random coefficients called HPSOGSA, is proposed in this paper based on a sequential hybrid pattern. The performance of the proposed algorithm is evaluated against PSO, GSA, and state-of-the-art hybrid variants by using a set of benchmark test functions.
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