Novel quantum particle multi-objective optimization method

A multi-objective optimization, quantum particle swarm technology, applied in the field of multi-objective optimization, can solve the problem of accuracy drop

Inactive Publication Date: 2016-08-24
方洋旺
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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 t

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  • Novel quantum particle multi-objective optimization method
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  • Novel quantum particle multi-objective optimization method

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Embodiment 1

[0059] The present invention selects ZDT1, ZDT2, ZDT3, ZDT4 in the ZDT function set, and DTLZ2 in the DTLZ function set as test functions, and the specific function forms are shown in Table 1. The ZDT series are two-objective optimization problems, in which ZDT1 has a convex Pareto front, ZDT2 has a non-convex Pareto front, ZDT3 has 5 non-continuous Pareto fronts, and ZDT4 has 21 9 A local Pareto optimum interferes with the search for the global optimum. The number of optimization objectives of the DTLZ2 function is set to 3, with 12 decision variables, and its true Pareto front is distributed on the unit sphere in the first quadrant. The above test functions include non-convexity, discontinuity, high dimensionality and deception, which can comprehensively and objectively reflect the pros and cons of the optimization algorithm.

[0060] Standard test functions in the experiment in Table 1

[0061]

[0062]

[0063] In order to comprehensively evaluate the accuracy, con...

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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...

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Application Information

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IPC IPC(8): G06N3/00G06F17/50
CPCG06F30/20G06N3/006
Inventor 方洋旺柴栋伍友利雍霄驹彭维仕杨鹏飞
Owner 方洋旺
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