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

Conference paper
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Part of the Communications in Computer and Information Science book series (CCIS, volume 1159)

Abstract

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.

Keywords

Ant Colony Algorithm (ACO) Genetic Algorithm (GA) Traveling Salesman Problem (TSP) Heuristics Meta-heuristics Optimization 

Notes

Acknowledgement

This work is supported by the National Natural Science Foundation of China (61922072, 61876169, 61673404, 61976237).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Electrical EngineeringZhengzhou UniversityZhengzhouChina
  2. 2.School of Computer Science and EngineeringJiangsu University of Science and TechnologyJiangsuChina
  3. 3.School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina

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