Improved constrained multi-objective optimization problem solving method

A multi-objective optimization and problem-solving technology, applied in the field of improved constrained multi-objective optimization problem solving, which can solve problems such as inability to satisfy the preferences of decision makers, complex constraint processing, diversity of solution sets, and poor time efficiency.

Pending Publication Date: 2021-06-25
YANSHAN UNIV
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Problems solved by technology

[0008] The technical problem to be solved in the present invention is to provide an improved method for solving constrained multi-objective optimization problems, so as to solve the problems of complex constraint processing, algorithm convergence, solution set diversity and poor time efficiency, and failure to satisfy decision-maker preferences. Meet the needs of solving constrained multi-objective optimization problems

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  • Improved constrained multi-objective optimization problem solving method
  • Improved constrained multi-objective optimization problem solving method
  • Improved constrained multi-objective optimization problem solving method

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Embodiment Construction

[0090] Below in conjunction with embodiment the present invention is described in further detail:

[0091] Such as Figure 1 to Figure 4 As shown, an improved method for solving constrained multi-objective optimization problems includes the following steps:

[0092]Step 1: Use the constraint processing method based on the constraint violation index to deal with the equality and inequality constraints of the constrained multi-objective optimization problem. The specific process is as follows:

[0093] The standard form of a constrained multi-objective optimization problem is as follows:

[0094] min f(x)=(f 1 (x), f 2 (x),...,f M (x)),

[0095] s.t.x ∈ X,

[0096] h i (x)=0,i=1,2,...,m,

[0097] g j (x)≤0,j=1,2,...,n, (1)

[0098] In the formula: x=(x 1 ,x 2 ,...,x D ) is the decision vector; D is the dimension of the decision vector; X is the feasible region of the decision vector; f(x) is the target vector; f 1 , f 2 ,...,f M are M objective functions; m is th...

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Abstract

The invention discloses an improved constrained multi-objective optimization problem solving method. The method comprises the following steps: 1, processing equality constraint conditions and inequality constraint conditions of a constrained multi-objective optimization problem by adopting a constraint processing method based on a constraint violation index; 2, using an improved multi-target bacterial population taxis algorithm based on Pareto dominance to solve and obtain a Pareto optimal solution set and a corresponding Pareto front of a multi-target optimization problem; 3, selecting an optimal compromise solution for constraining the multi-objective optimization problem from the Pareto optimal solution set obtained by solving by adopting a multi-objective decision method based on the relationship between the objective satisfaction and the objective weight, According to the invention, the problems that constraint processing is complex, algorithm convergence, solution set diversity and time efficiency are poor, and preference of decision makers cannot be met are solved, and the requirement for solving the constraint multi-objective optimization problem is met.

Description

technical field [0001] The invention relates to the technical field of multi-objective optimization and multi-objective decision-making, in particular to an improved method for solving constrained multi-objective optimization problems. Background technique [0002] Multi-objective optimization problems widely exist in real life, and the solution of multi-objective optimization problems plays a very important role in practical problems such as people's daily life and engineering applications. The various sub-objectives of multi-objective optimization problems are usually contradictory, and the improvement of one sub-objective may cause the deterioration of another or several other sub-objectives, that is, it is impossible to optimize all sub-objectives at the same time. Coordinate and compromise between multiple sub-objectives to make each sub-objective as optimal as possible. [0003] In the process of solving, the essential difference between the multi-objective optimizati...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F30/27G06F111/04G06F111/06
CPCG06F30/27G06F2111/04G06F2111/06
Inventor 卢志刚乞胜静蔡瑶马雨薇
Owner YANSHAN UNIV
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