Genetic algorithm by employing guided local search for multi-objective optimization problem

A multi-objective optimization and local search technology, applied in genetic rules, gene models, etc., can solve problems such as insufficient population diversity, high computational cost of neighborhood solutions, and inability to solve multi-objective problems well, so as to achieve effective optimization of optimization problems , avoid movement, and reduce the effect of computing costs

Inactive Publication Date: 2017-05-03
SICHUAN YONGLIAN INFORMATION TECH CO LTD
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Problems solved by technology

[0005] The goals of this algorithm are: first, to solve the problem of excessive convergence and insufficient population diversity caused by the continuous cross-breeding of close relatives in the process of genetic algorithm; second, for a given scheme of the flexible job shop problem, its neighborhood

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  • Genetic algorithm by employing guided local search for multi-objective optimization problem
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  • Genetic algorithm by employing guided local search for multi-objective optimization problem

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Embodiment Construction

[0028] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention and not to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0029] The present invention aims at the problem of too fast convergence caused by the continuous cross-breeding of close relatives in the genetic operation process of the existing genetic algorithm, and the problem of insufficient population diversity is designed to calculate the cross index and mutation rate before the crossover. The cost of the domain solution is too high. Local search is only performe...

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Abstract

The invention provides a genetic algorithm by employing guided local search for a multi-objective optimization problem. The algorithm is used for the field of flexible job-shop scheduling. A flexible job-shop scheduling problem belongs to an NP-Hard problem, optimization of multiple objectives often needs to be faced in real production, and the objectives are in interaction and conflict. The genetic algorithm aims at solving the problems of too fast convergence, insufficient population diversity and over-high calculation cost of enumerating all neighborhood solutions caused by continuous cross breeding of close relatives in a genetic operation in the genetic algorithm in the prior art. According to the algorithm, a procedure of calculating a crossover rate and a mutation rate before genetic crossover and mutation and a procedure of searching a movable process and a feasible position by using the guided local search are designed for these problems; and through introduction of the two procedures, the calculation cost is reduced while algorithm premature is avoided. The algorithm is high in practicability and can be well used for actual job-shop scheduling.

Description

[0001] Technical field [0002] The invention relates to the field of job shops, in particular to using an algorithm to solve a multi-objective flexible job shop scheduling problem. Background technique [0003] The flexible job shop scheduling problem is an extension of the classic job shop scheduling problem. Each process is allowed to be processed on a given set of equipment. Therefore, in the flexible job shop scheduling problem, in addition to determining the processing sequence of each process on each device, it is also necessary to allocate a suitable device for each process. The flexible job shop scheduling problem is an NP-Hard problem. In real production, it is often necessary to face multiple optimization goals, and each goal affects and conflicts with each other. Therefore, there is generally no single optimal solution for multi-objective problems that is best for all objectives. At present, many techniques have been used to solve the flexible job shop schedulin...

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Application Information

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IPC IPC(8): G06N3/12
CPCG06N3/126
Inventor 龚晓慧胡成华
Owner SICHUAN YONGLIAN INFORMATION TECH CO LTD
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