Adaptive binary artificial bee colony algorithm - Durgut, Rafet; Aydin, Mehmet Emin (Applied soft computing, 2021)
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. Artificial 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 sufficiently for combinatorial and binary problems. This paper proposes an adaptive hybrid approach to devise ABC algorithms with multiple and complementary binary operators for higher efficiency in solving binary problems. Three prominent operator selection schemes have been comparatively investigated for the best configuration 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. •This paper proposes ensemble of different binary neighbourhood operators for ABC algorithms.•Three operator selection mechanisms have been tested.•Different reward types are used for credit assignment problem.•Instantaneous and sliding windows based reward values are used.•The best adaptive configuration has successfully solved uncapacitated facility location problems to optimum.