Abstract
In order to improve the performance of Particle Swarm Optimization (PSO) algorithm in solving continuous function optimization problems, a chaotic particle optimization algorithm for complex functions is proposed. Firstly, the algorithm uses qubit Bloch spherical coordinate coding scheme to initialize the initial position of the population. This coding method can expand the ergodicity of the search space, increase the diversity of the population, and further accelerate the convergence speed of the algorithm. Secondly, Logistic chaos is used to search the elite individuals of the population, which effectively prevents the PSO algorithm from falling into local optimization, thus obtaining higher quality optimal solution. Finally, complex functions are used to improve chaotic particles to further improve the convergence speed and optimization accuracy of PSO algorithm. Through the optimization tests of four complex high-dimensional functions, the simulation results show that the improved algorithm is more competitive and its overall performance is better, especially suitable for the optimization of complex high-dimensional functions.
Highlights
Complex function optimization is an important research direction of optimization problems
According to the variation rule of objective function value, In appropriate steps along the direction that optimizes the value of the objective function, An approximate calculation method that approaches the optimal point of the objective function step by step, This method is good at solving continuous differentiated convex optimization problems, With the continuous expansion of engineering optimization problems, most of the objective functions are non-convex optimization problems
The emergence of group intelligent optimization algorithms provides a limited way for complex function optimization problems [2, 3]
Summary
Complex function optimization is an important research direction of optimization problems. The flow of the basic PSO algorithm is as follows: (1) Set the parameters of PSO algorithm, such as population size, problem dimension, inertia weight, maximum range and maximum speed, etc.
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