Meta-heuristic Hybrid Algorithmic Approach for Solving Combinatorial Optimization Problem (TSP)

Solving and optimizing combinatorial problems require high computational power because of their exponential growth and requirement of high processing power. During this study, a hybrid algorithm (Genetic Ant Colony Optimization Algorithm) is proposed in comparison with standard algorithm (Ant Colony Optimization Algorithm). Further the parameters for Ant Colony Optimization Algorithm are instinctively tuned to different levels of all heuristics to obtain suboptimal level, then multiple crossovers and mutation operators are used alongside those selected parameters while generating results with hybrid algorithm. The main emphasis of the proposed algorithm is the selection and tuning of parameters, which is extremely influential in this case. The algorithm has been tested on six benchmarks of TSPLIB. The results were compared with standard ACO algorithm, the hybrid algorithm outperformed the standard ACO algorithm.