Hybrid Gravitational Search Algorithm with Random-key Encoding Scheme Combined with Simulated Annealing
Huiqin Chen, Sheng Li, Zheng Sophia Tang · 2011
Summary 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.