ABC-X : A Configurable Generalized Artificial Bee Colony Algorithm
The artificial bee colony (ABC) algorithm is a popular metaheuristic that was originally conceived for tackling continuous function optimization tasks. Over the last decade, a large number of variants of ABC have been proposed, making it by now a well-studied swarm intelligence algorithm. Typically, in a paper on algorithmic variants of ABC algorithms, one or at most two of its algorithmic components are modified. Possible changes include variations on the search equations, the selection of candidate solutions to be explored, or the adoption of features from other algorithmic techniques. In this article, we propose to follow a different direction and to build a generalized ABC algorithm, which we call ABC-X. ABC-X collects algorithmic components available from known ABC algorithms into a common algorithm framework that allows not only to instantiate known ABC variants but, more importantly, also many ABC algorithm variants that have never been explored before in the literature. Automatic algorithm configuration techniques can generate from this template new ABC variants that perform better than known ABC algorithms, even when their numerical parameters are fine-tuned using the same automatic configuration process.
SSEABC : Self-adaptive Search Equation-based Artificial Bee Colony Algorithm
This paper presents a new variant of the Artificial Bee Colony (ABC) algorithm, which is called “Self-adaptive Search Equation-based Artificial Bee Colony” (SSEABC) algorithm. SSEABC integrates three strategies into the canonical ABC algorithm. The first strategy is a self-adaptive strategy that determines appropriate search equations for a given problem instance by discarding dominated ones from a pool comprising a large number of randomly generated search equations. The second is an incremental population size strategy, which is based on adding new food sources located around the best-so-far food source position after a predefined number of iterations. This helps to increase convergence speed. The third strategy is competitive local search selection; it decides on which is the most effective local search procedure by comparing the performance of Mtsls1 and IPOP-CMA-ES in a competition phase and applying the winner local search to the best food source position for the rest of the iterations. The SSEABC algorithm is tested on the CEC 2014 numerical optimization problems and very competitive results are obtained.