Abstract
Meta-heuristic algorithms have been widely used in solving scheduling problems; previous studies focused on enhancing existing algorithmic mechanisms. This study advocates a new perspective-developing new chromosome (solution) representation schemes may improve the performance of existing meta-heuristic algorithms. In the context of a scheduling problem, known as permutation manufacturing-cell flow shop (PMFS), we compare the effectiveness of two chromosome representation schemes (Sold and Snew) while they are embedded in a meta-heuristic algorithm to solve the PMFS scheduling problem. Two existing meta-heuristic algorithms, genetic algorithm (GA) and ant colony optimization (ACO), are tested. Denote a tested meta-heuristic algorithm by X-Y, where X represents an algorithmic mechanism and Y represents a chromosome representation. Experiment results indicate that GA- Snew outperforms GA-Sold, and ACO-Snew also outperforms ACO-Sold. These findings reveal the importance of developing new chromosome representations in the application of meta-heuristic algorithms.
Original language | English |
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Pages (from-to) | 21-30 |
Number of pages | 10 |
Journal | Robotics and Computer-Integrated Manufacturing |
Volume | 29 |
Issue number | 3 |
DOIs | |
State | Published - 1 Jan 2013 |
Keywords
- Ant Colony optimization
- Chromosome representation
- Genetic algorithm
- Scheduling