Novel quantum particle multi-objective optimization method

Abstract

The invention mainly belongs to the technical field of multi-objective optimization, and specifically relates to a novel quantum particle multi-objective optimization method. The novel quantum particle multi-objective optimization method is used to improve the accuracy, diversity and evenness of solutions for handling the problem of multi-objective optimization using a quantum particle swarm algorithm. The novel quantum particle multi-objective optimization method comprises the steps of establishing a quantum double-potential-well model based on double-delta potential well simplification, establishing a particle location updating model based on the double-potential-well model, and constructing a shared learning strategy of particles. The local optimization precision of the algorithm is improved, and solutions are distributed more uniformly. The shared learning mechanism is used to widen the search scope of particles and increase the diversity of solutions, and the tendency of the existing quantum particle swarm algorithm to easily converge to a boundary solution is avoided. Good convergence performance and distribution performance can still be maintained during handling of the problem of high-dimension target optimization. A novel practical method is provided for solving the problem of multi-objective optimization in engineering application.

Description

technical field [0001] The invention mainly belongs to the technical field of multi-objective optimization, and in particular relates to a novel quantum particle swarm multi-objective optimization method. Background technique [0002] Unlike single-objective optimization, which finally converges to a single solution, multi-objective optimization obtains an optimal solution set, so it is necessary to store the Pareto optimal solution for each generation of particles in an external file, and this external file follows the movement of particles Constantly updated and maintained, eventually reaching the Pareto front. Multi-objective optimization expects the final Pareto front to be as close as possible to the real Pareto front, that is, good convergence. Secondly, the Pareto front is required to be evenly distributed and the distribution range should be as wide as possible. [0003] The quantum particle swarm optimization algorithm shows good global search ability and fast conv...

AI Technical Summary

Problems solved by technology

First, a single local attractor P is used in the quantum particle swarm optimization algorithm i (t), causing particles to easily gather near a single solution in the late iteration; secondly, the characteristic length L of the particle aggregation state in the quantum particle swarm optimization algorithm i (t) adopts the average best position c(t), causing each particle to refer to the information of the same c(t), and in multi-objective optimization, each particle should find the Pareto optimal solution, and the particles use the same c (t) Limiting the search space and the diversity of groups, resulting in a decrease in the accuracy of the algorithm when dealing with high-dimensional target optimization problems in engineering applications

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