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Title |
Hybrid Gravitational Search Algorithm with Random-key Encoding Scheme Combined with Simulated Annealing |
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Author |
Huiqin Chen, Sheng Li, Zheng Tang |
| Citation |
Vol. 11 No. 6 pp. 208-217 |
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Abstract |
This paper is devoted to the presentation of a novel hybrid method by combining gravitational search algorithm (GSA) with simulated annealing (SA) method. In GSA, the representation of the problem on hand is based on the random-key encoding scheme. While GSA is employed as a global search algorithm, a multi-type local improvement scheme is incorporated into it, performing as a local search operator. Furthermore, SA is utilized to manipulate the iteration progress algorithmically. The resultant proposed hybrid random-key gravitational search algorithm (Hr-GSA) is tested on the famous traveling salesman problem. The experimental results show that Hr-GSA is more robust and efficient than other seven traditional population based algorithms, such as genetic algorithm, particle swarm optimization, artificial immune system, and so on. |
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Keywords |
Gravitational search, simulated annealing, local improvement, population-based algorithm, hybridization |
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