Evolving novel algorithm based on intellectual behavior of Wild dog group as optimizer

Numerous algorithms have been invented for optimizations which are nature inspired and based on real life behaviour of species. In this paper, intelligent chasing and hunting methods adopted by the dogs to chase and hunt their prey in groups are used to develop the novel methodology named as “Dog Group Wild Chase and Hunt Drive (DGWCHD) Algorithm”. The proposed algorithm has been implemented on some TSP benchmark problems. These benchmark problems have been solved by different researchers for optimization as test bed for performance analysis of their proposed novel intelligent algorithms like Ant Colony System (ACS), Genetic Algorithms (GA), Simulated Annealing (SA), Evolutionary Programming (EP), The Multi-Agent Optimization System (MAOS), Particle Swarm Optimization (PSO) and Neural Networks (NN). The performance analysis of the novel proposed DGWCHD algorithm has been done and results are compared with other nature inspired techniques. The results obtained are very optimistic and encouraging.