Multi-target flexible job shop scheduling method based on cooperative hybrid artificial fish swarm model

An artificial fish swarm algorithm and workshop scheduling technology, applied in computing models, biological models, instruments, etc., can solve the problems of slow convergence and poor local optimization ability in the later stage

Inactive Publication Date: 2015-08-26
DALIAN UNIV OF TECH
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Problems solved by technology

[0007] The technical problem to be solved in the present invention is how to use the artificial fish swarm algorithm to solve the multi-objective flexible job shop scheduling problem. The key point is to improve the artificial fish swarm algorithm to overcome the slow convergence and local optimization of the algorithm in the optimization process. The difficulty lies in how to ensure the quality and dispersion of the optimized non-inferior solution set

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  • Multi-target flexible job shop scheduling method based on cooperative hybrid artificial fish swarm model
  • Multi-target flexible job shop scheduling method based on cooperative hybrid artificial fish swarm model
  • Multi-target flexible job shop scheduling method based on cooperative hybrid artificial fish swarm model

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[0059] Embodiments of the present invention will be described in detail below in combination with technical solutions and accompanying drawings.

[0060] 1. Encoding and decoding. Artificial fish encoding is to transform the feasible solution of the research problem from the solution space to the search space that the artificial fish swarm algorithm can handle; artificial fish decoding is to transform the algorithm space to the problem space. In the flexible job shop scheduling problem, the machine selection sub-problem and the process sequencing sub-problem will be encoded and decoded respectively. For the examples given in Table 1, the artificial fish encoding process is as follows figure 2 shown. For regular scheduling performance indicators, the optimal solution exists in the active scheduling, so the feasible solution is decoded into the active scheduling, which not only ensures the existence of the optimal solution but also improves the search efficiency of the algori...

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Abstract

The invention belongs to the crossing field of a computer application technology and production manufacturing. A natural computing technology is used to optimize a multi-target flexible job shop scheduling problem. A problem that a cooperative hybrid artificial fish swarm algorithm is used to solve multi-target flexible job shop scheduling is provided. The method is characterized in that a foraging behavior with a distribution estimation attribute and an artificial fish attraction behavior are designed to improve an artificial fish swarm model; a cooperation idea is introduced into the model; through multiple population cooperation of the fish swarm, global searching is performed and is cooperated with a simulation annealing algorithm so as to enhance an algorithm local searching capability; aiming at a multi-target problem, an improved epsilon-Pareto dominant strategy is designed to evaluate an individual applicable degree value. The method in the invention has the following advantages that problems of slow later-period convergence, a poor local optimizing ability and the like, which exist in the artificial fish swarm algorithm during a searching process, can be overcome; through cooperative optimization, a pareto solution set with good quality and dispersibility is obtained.

Description

technical field [0001] The invention belongs to the intersection field of computer application technology and manufacturing, and optimizes the multi-objective flexible job shop scheduling problem through natural computing technology. A collaborative mixed artificial fish swarm algorithm is proposed to solve the flexible job shop scheduling problem with three objectives of maximum completion time, maximum machine load and total machine load. The main innovation is the design of foraging behavior and artificial The fish attracting behavior improves the artificial fish school model; the idea of ​​collaboration is introduced into the model, the global search is carried out through multi-group cooperation of the fish school, and the local search ability is enhanced by synergy with the simulated annealing algorithm; an improved ε is designed for multi-objective problems - Pareto dominance strategy evaluates the fitness value of the individual. Background technique [0002] In the...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/00G06Q10/06
Inventor 葛宏伟陈新孙亮谭国真
Owner DALIAN UNIV OF TECH
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