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Parallel cooperative evolution-based high-dimensional multi-objective optimization algorithm

A multi-objective optimization and co-evolution technology, applied in the field of intelligent optimization algorithms, can solve problems such as the reduction of optimization effects, and achieve the effects of improving performance, balancing convergence and diversity

Inactive Publication Date: 2018-08-21
SUN YAT SEN UNIV +1
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Problems solved by technology

But in real life, the number of objectives of many problems exceeds 3, that is, high-dimensional multi-objective optimization problem (MaOP), the optimization effect of these traditional multi-objective evolutionary algorithms is reduced

Method used

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  • Parallel cooperative evolution-based high-dimensional multi-objective optimization algorithm
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  • Parallel cooperative evolution-based high-dimensional multi-objective optimization algorithm

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Embodiment 1

[0033] Such as Figure 1-2 As shown, a high-dimensional multi-objective optimization algorithm based on parallel co-evolution includes the following process:

[0034] S1: Set the number of targets M, the maximum number of evaluations MFE, and initialize the initial population P to search for a set of solutions that take into account both convergence and diversity 1 And the population size is N 1 , initialize the initial population P for finding extreme points 2 And the population size is N 2 ;

[0035] S2: Generate a set of direction vectors W={w 1 ,w 2 ,...,w 2M} to guide the population to find extreme points;

[0036] S3: From P 1 Randomly select individual x from 1 , then from P 1 , P 2 Two populations randomly select a population, and then randomly select an individual x from this population 2 , and finally for individual x 1 and x 2 Crossover produces two offspring individuals o 1 and o 2 ,repeat Secondary offspring population Q 1 ;

[0037] S4: From P...

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Abstract

The invention provides a parallel cooperative evolution-based high-dimensional multi-objective optimization algorithm. The algorithm maintains two populations: one population is in charge of searchingfor an extreme point, and the other population is in charge of searching for a group of solutions taking convergence and diversity into account in a whole decision space. The two populations are cooperatively evolved. In a whole evolutionary process, the two populations have own evolution modes, and information communication and information sharing exist between the two populations. In an algorithm framework, any Pareto domination-based multi-objective evolutionary algorithm can be applied to the population in charge of searching for the group of the solutions taking the convergence and the diversity into account in the whole decision space. The framework improves the performance of the Pareto domination-based multi-objective evolutionary algorithm in solving a high-dimensional multi-objective optimization problem, overcomes the shortcoming of rapid deterioration of the performance of a conventional Pareto domination-based evolutionary algorithm in solving the high-dimensional multi-objective problem, and balances the convergence and diversity of high-dimensional multi-objective optimization problem solving.

Description

technical field [0001] The invention relates to the field of intelligent optimization algorithms, more specifically, to a high-dimensional multi-objective optimization algorithm based on parallel cooperative evolution. Background technique [0002] In the real world, many problems can be expressed as multi-objective optimization problems (MOPs), that is, problems with two or more optimization objectives. Multi-objective optimization problems are fundamentally different from single-objective optimization problems. There is only one optimal solution in single-objective optimization problems, but for multi-objective optimization problems, there is a set of optimal solutions due to conflicts between objectives. Evolutionary Algorithm (EA) is very suitable for solving multi-objective problems, because it can get a set of better approximate solutions by running it once. In the past ten years, many Pareto dominance-based evolutions have been proposed and achieved good results in ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/00
CPCG06Q10/04G06N3/006
Inventor 王甲海岑彬忠印鉴潘文杰
Owner SUN YAT SEN UNIV
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