Honey Bees Mating Optimization algorithm for large scale vehicle routing problems

  • Authors:

  • Yannis Marinakis;Magdalene Marinaki;Georgios Dounias

  • Affiliations:

  • Decision Support Systems Laboratory, Department of Production Engineering and Management, Technical University of Crete, Chania, Crete, Greece 73100;Industrial Systems Control Laboratory, Department of Production Engineering and Management, Technical University of Crete, Chania, Crete, Greece 73100;Department of Financial and Management Engineering, Management and Decision Engineering Laboratory, University of the Aegean, Chios, Greece 82100

  • Venue:
  • Natural Computing: an international journal
  • Year:
  • 2010

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Abstract

Honey Bees Mating Optimization algorithm is a relatively new nature inspired algorithm. In this paper, this nature inspired algorithm is used in a hybrid scheme with other metaheuristic algorithms for successfully solving the Vehicle Routing Problem. More precisely, the proposed algorithm for the solution of the Vehicle Routing Problem, the Honey Bees Mating Optimization (HBMOVRP), combines a Honey Bees Mating Optimization (HBMO) algorithm with the Multiple Phase Neighborhood Search---Greedy Randomized Adaptive Search Procedure (MPNS---GRASP) and the Expanding Neighborhood Search (ENS) algorithm. Besides these two procedures, the proposed algorithm has, also, two additional main innovative features compared to other Honey Bees Mating Optimization algorithms concerning the crossover operator and the workers. Two sets of benchmark instances are used in order to test the proposed algorithm. The results obtained for both sets are very satisfactory. More specifically, in the fourteen classic instances proposed by Christofides, the average quality is 0.029% and in the second set with the twenty large scale vehicle routing problems the average quality is 0.40%.