Multi-parameter multi-objective chaotic particle swarm optimization method

Abstract

The present invention discloses a multi-parameter multi-object chaotic particle swarm parameter optimization method. The method comprises the steps of (1) determining a target function and a parameter to be optimized, (2) initializing an algorithm, (3) calculating the target function corresponding to each individual in a population, (4) updating an individual history optimal solution, (5) updating a particle velocity and position, (6) updating a global optimal solution set, (7) updating a global optimal solution, and (8) carrying out result judgment. Compared with a common random initialization method and an existing chaotic logistic mapping particle swarm initialization method, according to the method of the invention, the performance of global optimization is improved and the stability is good, compared with a common multi-object weighted optimization method, the Pareto optimal solution technology is employed by the method, and the problem of difficult weight selection in the multi-object method is solved.

Description

technical field [0001] The invention belongs to the technical field of optimization control, and relates to a multi-parameter and multi-objective chaotic particle swarm parameter optimization method. Background technique [0002] In recent years, intelligent optimization algorithms such as genetic algorithm, ant colony algorithm, immune algorithm, and particle swarm optimization algorithm have been widely used in various fields. Among them, the particle swarm optimization algorithm is a random search algorithm based on group cooperation developed by simulating the foraging behavior of birds. It has the characteristics of simplicity, fast convergence, high optimization efficiency, and good robustness. Good results have been achieved in the problem, but it is still a challenging subject for multi-parameter and multi-objective comprehensive optimization problems. [0003] At present, there are two main problems that restrict the application of existing particle swarm optimizat...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a multi-parameter and multi-objective chaotic particle swarm parameter optimization method. Aiming at the problem that the distribution of initial particles in the solution space affects the convergence of the particle swarm algorithm, the initial particles are initialized by using the one-way coupling map lattice space-time chaotic map. Initial position and velocity, to better realize the initial uniform distribution of particles; secondly, by combining the particle swarm algorithm with the Pareto optimal solution theory, the solution based on the optimal trade-off of multi-objective functions is obtained, which solves the problem of weight loss in the weighted multi-objective method. Difficult value selection problem

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