Tournament Selection Based Artificial Bee Colony Algorithm with Elitist Strategy

Abstract

Artificial bee colony (ABC) algorithm is a novel heuristic algorithm inspired from the intelligent behavior of honey bee swarm. ABC algorithm has a good performance on solving optimization problems of multivariable functions and has been applied in many fields. However, traditional ABC algorithm chooses solutions on the onlooker stage with roulette wheel selection (RWS) strategy which has several disadvantages. Firstly, RWS is suitable for maximization optimization problem. The fitness value has to be converted when solving minimization optimization problem. This makes RWS difficult to be generally used in real-world applications. Secondly, RWS has no any parameter that can control the selection pressure. Therefore, RWS is not easy to adapt to various optimization problems. This paper proposes a tournament selection based ABC (TSABC) algorithm to avoid these disadvantages of RWS based ABC. Moreover, this paper proposes an elitist strategy that can be applied to traditional ABC, TSABC, and any other ABC variants, so as to avoid the phenomenon that ABC algorithm may abandon the globally best solution in the scout stage. We compare the performance of traditional ABC and TSABC on a set of benchmark functions. The experiment results show that TSABC is more flexible and can be efficiently adapted to solve various optimization problems by controlling the selection pressure.