Adaptive Binary Artificial Bee Colony Algorithm

EasyChair Preprint 4687

Abstract

 Metaheuristics and swarm intelligence algorithms are bio-inspired algorithms, which have long standing track record of success in problem solving. Due to the nature and the complexity of the problems, problem solving approaches may not achieve the same success level in every type of problems. Articial bee colony (ABC) algorithm is a swarm intelligence algorithm and has originally been developed to solve numerical optimisation problems. It has a sound track record in numerical problems, but has not yet been tested suciently for combinatorial and binary problems. This paper proposes an adaptive hybrid approach to devise ABC algorithms with multiple and complementary binary operators for higher eciency in solving binary problems. Three prominent operator selection schemes have been comparatively investigated for the best conguration in this regard. The proposed approach has been applied to uncapacitated facility location problems, a renown NP-Hard combinatorial problem type modelled with 0-1 programming, and successfully solved the well-known benchmarks outperforming state-of-art algorithms.

Keyphrases: 0-1 programming, Adaptive Operator Selection, Articial Bee Colony, Uncapacitated facility location problems

@booklet{EasyChair:4687,
  author    = {Rafet Durgut and Mehmet Emin Aydin},
  title     = {Adaptive Binary Artificial Bee Colony Algorithm},
  howpublished = {EasyChair Preprint 4687},
  year      = {EasyChair, 2020}}