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Hybrid Gravitational Search Algorithm with Random-key Encoding Scheme Combined with Simulated Annealing


Huiqin Chen, Sheng Li, Zheng Tang


Vol. 11  No. 6  pp. 208-217


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.


Gravitational search, simulated annealing, local improvement, population-based algorithm, hybridization