원문정보
초록
영어
Aiming at solving the NP-hard workshop production scheduling problems, proposed one kind based on mind evolutionary algorithm. The algorithm in the traditional ant colony algorithm is established, and the combination of evolutionary thought and local optimization idea overcomes the basic ant colony algorithm is easy to fall into local optimal defects, the improved state transition rules, defining a pheromone range, improve the pheromone update strategy, and the increase of neighborhood search. Experimental results show that, for a typical production scheduling problems, based on mind evolutionary ant colony algorithm can obtain the optimal solution in theory, optimal solution, the solution and average three indicators are better than the basic ant colony algorithm, showed good performance.
목차
1. Introduction
2. Basic Principles of an Ant Colony Algorithm
3. Ant Colony Algorithm Based On Evolutionary Thinking
3.1. Mind Evolutionary Algorithm
3.2. Ant Colony Algorithm based on Evolutionary Thinking
4. Ant Colony Algorithm of Typical Production Scheduling Problem
4.1. Typical Job Shop Problem
4.2. Improved State Transition Rules
4.3. Defining the Scope of Pheromone
4.4. Pheromone Update Strategy
4.5 Increasing in Neighborhood Search
5. Simulation Testing and Analysis
6. Conclusions
Acknowledgements
References