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Genetic algorithms (GAs) are the most important evolutionary computation technique that is used to solve various complex problems that involve a large search space. To have a performance improvement over GA, the concept of Hybrid genetic algorithms that were inspired from the biological behaviour of different living beings was put to use to solve the Non-deterministic Polynomial (NP)complete problems. Hybrid GA can be derived by amalgamating with efficient nature inspired heuristic algorithms like Particle Swarm optimization (PSO), Ant Colony Optimization (ACO), firefly algorithm, cuckoo search, etc.. The grey wolf optimization algorithm has been the recently proposed bio-inspired optimization algorithm that proved as the most recent and best in solving complex problems. In this perspective, Group Mosquito Host Seeking Algorithm based Self-organizing (GMHSA) technique for genetic algorithm has been proposed. The proposed GMHSA model is embedded at the stage prior to genetic operations in order to achieve better exploration and exploitation. The well-known combinatorial optimization problem, Travelling Salesman Problem (TSP), is used as the testbed and the test instances retrieved from the standard TSP library. Various recent and best working hybrid GA models are used to justify the significances of the proposed model. The experimental results show that the proposed algorithm yields better outcome with respect to computational time and also achieve improvement in average convergence rate, irrespective of the size of the test instance, compared to other existing models.

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