Abstract
As an influential technology of swarm evolutionary computing (SEC), the particle swarm optimization (PSO) algorithm has attracted extensive attention from all walks of life. However, how to rationally and effectively utilize the population resources to equilibrate the exploration and utilization is still a key dispute to be resolved. In this paper, we propose a novel PSO algorithm called Chaos Adaptive Particle Swarm Optimization (CAPSO), which adaptively adjust the inertia weight parameter <inline-formula> <tex-math notation="LaTeX">$w$ </tex-math></inline-formula> and acceleration coefficients <inline-formula> <tex-math notation="LaTeX">$c_{1},c_{2}$ </tex-math></inline-formula>, and introduces a controlling factor <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> based on chaos theory to adaptively adjust the range of chaotic search. This makes the algorithm have favorable adaptability, and then the particles cannot only effectively prevent missing the global optimal solution, but also have a high probability of jumping out of the local optimal solution. To verify the stability, convergence speed, and accuracy of CAPSO, we conduct ample experiments on 6 test functions. In addition, to further verify the effectiveness and scalability of CAPSO, comparative experiments are carried out on the CEC2013 test suite. Finally, the results prove that CAPSO outperforms other peer algorithms to achieve satisfactory performance.
Highlights
M OST engineering optimization problems can be abstracted into the mathematical representation of multimodal functions with multiple minimum values [1]
Based on the in-depth research and analysis of traditional particle swarm optimization algorithms, this paper aims to deal with complex function optimization problems and practical applications that are prone to poor convergence accuracy and the inability to effectively obtain global optimization
On the basis of chaos theory, we propose a chaotic adaptive particle swarm optimization (CAPSO) algorithm
Summary
M OST engineering optimization problems can be abstracted into the mathematical representation of multimodal functions with multiple minimum (maximum) values [1]. The Particle Swarm Optimization (PSO) algorithm [5], which is researched and developed by Kennedy and Eberhart [6], is an important technology with uniqueness and effectiveness in optimization problems. Once it was published, it triggered a wave of research. In each iteration, the direction and distance of the particle search will be adjusted in time under the joint influence of its local optimal position and the global optimal position of all particles. The main contributions of CAPSO can be summarized as follows: 1) We combine linear and nonlinear inertia weight to adaptively adjust the local and global search ability of particles.
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