Abstract
For the first time , the Holonic Particle Swarm Optimization (HPSO ) algorithm applies multiagent theory about the improvement in the PSO algorithm and achieved good results. In order to further improve the performance of the algorithm, this paper proposes an improved Adaptive Holonic Particle Swarm Optimization (AHPSO) algorithm. Firstly, a brief review of the HPSO algorithm is carried out, and the HPSO algorithm can be further studied in three aspects: grouping strategy, iteration number setting, and state switching discrimination. The HPSO algorithm uses an approximately uniform grouping strategy that is the simplest but does not consider the connections between particles. And if the particles with larger or smaller differences are grouped together in different search stages, the search efficiency will be improved. Therefore, this paper proposes a grouping strategy based on information entropy and system clustering and combines two grouping strategies with corresponding search methods. The performance of the HPSO algorithm depends on the setting of the number of iterations. If it is too small, it is difficult to search for the optimal and it wastes so many computing resources. Therefore, this paper constructs an adaptive termination condition that causes the particles to terminate spontaneously after convergence. The HPSO algorithm only performs a conversion from extensive search to exact search and still has the potential to fall into local optimum. This paper proposes a state switching condition to improve the probability that the algorithm jumps out of the local optimum. Finally, AHPSO and HPSO are compared by using 22 groups of standard test functions. AHPSO is faster in convergence than HPSO, and the number of iterations of AHPSO convergence is employed in HPSO. At this point, there exists a large gap between HPSO and the optimal solution, i.e., AHPSO can have better algorithm efficiency without setting the number of iterations.
Highlights
Particle swarm optimization (PSO) has been successfully applied to many optimization problems
The amount of Adaptive Holonic Particle Swarm Optimization (AHPSO)’s discriminant part is slightly higher than that of Holonic Particle Swarm Optimization (HPSO), the magnitude of the amount of extra computation has little impact on single iteration, i.e., the amount of calculation of two algorithms is similar in single iteration. e above figure suggests that AHPSO has a faster convergence speed than HPSO, nearly 78% of the number of HPSO iterations, saving 20% or more of the calculation time, which is of great significance to the optimization of multiple complex functions
E three points of setting the number of iterations and switching time of search strategy can be further studied, so an improved AHPSO algorithm is proposed. e main work is as follows: (1) e average grouping strategy is adopted in the HPSO algorithm, which is fast and easy to implement but has little effect on the performance improvement of the algorithm. erefore, this study employs the grouping strategy of information entropy method and system clustering method for reference to improve the grouping mode of particles and arrange and combine the grouping mode and search strategy. e advantages of grouping strategy are verified through theoretical analysis and simulation
Summary
Particle swarm optimization (PSO) has been successfully applied to many optimization problems. Is method stratifies the population into two groups to perform PSO and GA operations, respectively In each iteration, it improves the best position by comparing the optimal results of the PSO algorithm and GA. If we do not build a holonic structure, 11 basic agents’ information are needed to perform real-time information interaction, which requires each agent to have strong information transmission, storage, and processing capabilities In this case, each node stores global information. E holonic structure is controlled by layer management, and the high-level virtual agent controls the area information, and the number of optimizations is small. E literature [1] is to improve the topology of PSO particles by using holonic structure in multiagent systems. Since the literature [1] is the first to use the holonic structure to improve the PSO algorithm, there is still room for further improvement in some aspects. erefore, this paper carries out subsequent research
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