Name
Abstract | ||
|---|---|---|
The bin packing process can be modeled on optimization problems and it is widely studied due to its various applications. However, most implementation of the problem lacks of coordination in a unified optimization framework. Therefore, based on a general framework of multi-objective optimization with metaheuristics, jMetal, a novel genetic algorithm for bin packing problems is proposed in this paper. First, it extends the base solution of jMetal to problems with dynamic variables. Then according to the flow chart of genetic algorithms in jMetal, the operators for the heuristic algorithm are devised, e.g., the crossover and mutation operators. Finally the bin packing problem is implemented with the designed operators in this framework. Experiments are carried out, to verify the performance of the algorithm, obtaining the results that are comparable with the well-known heuristics. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/IEEE.ICCC.2017.10 | 2017 IEEE International Conference on Cognitive Computing (ICCC) |
Keywords | DocType | ISBN |
bin packing,jMetal,genetic algorithm,optimization | Conference | 978-1-5386-2009-0 |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fei Luo | 1 | 8 | 4.53 |
| Isaac D. Scherson | 2 | 279 | 45.02 |
| Joel Fuentes | 3 | 1 | 3.11 |