A configurable generalized artificial bee colony algorithm with local search strategies

Abstract

In this paper, we apply a generalized artificial bee colony (ABC-X) algorithm to the learning-based real-parameter optimization competition at the 2015 Congress on Evolutionary Computation. The main idea underlying the ABC-X algorithm is to provide a flexible, freely configurable framework for artificial bee colony (ABC) algorithms. From this framework, one can not only instantiate known ABC algorithms but also configure new, previously unseen ABC algorithms that may perform even better than known ABC algorithms. One key advantage of a configurable algorithm framework is that it is adaptable to many different specific problems without requiring necessarily an algorithm re-design. This is relevant if in the application problem repeatedly instances of the problem need to be solved regularly. This situation arises in many practical settings e.g. in power control or other application areas: Routinely a sequence of specific instances of a more general continuous optimization problem arise and these instances have to be solved repeatedly (possibly for an infinite horizon) in the future: in this case the instances of the problem in the sequence will share similarities as they arise from a same source. This is also the situation that is targeted by the learning-based real-parameter optimization competition and which we have also described in our own earlier research.