Abstract
Traditional genetic algorithms often meet the occurrence of slow convergence and enclosure competition. We present a parallel algorithm for the traveling salesman problem (TSP) which incorporates several greedy heuristics based on genetic algorithms. We call this algorithm the greedy genetic algorithm (GGA) here. The ideas we incorporate in our algorithm include: (i) generating the initial population using the gene bank; (ii) double-directional greedy crossover and the local searches of greedy mutation; (Hi) special-purpose immigrations to promote diversity of population and an open competition; (iv) stepwise parallel optimization of each individual of the population; (v) developing an overall design that attempts to strike a right balance between diversification and a bias towards fitter individuals. We test all these ideas to assess their impacts on the GGA performance, and also compare our final algorithm to the simple genetic algorithm on the benchmark instances in TSPLIB, a well-known library of TSP instances. We find the greedy genetic algorithm to be a very effective and robust algorithm for TSPs.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
