Artificial bee colony algorithm: A component-wise analysis using diversity measurement

A swarm-based metaheuristic algorithm, like artificial bee colony (ABC), embraces four key elements of collective intelligence: positive feedback, negative feedback, multiple interactions, and fluctuation. Fluctuation refers to population diversity which can be measured using dimension-wise diversity. This paper performed component-wise analysis of ABC algorithm using diversity measurement. The analysis revealed scout bees component as counterproductive and onlooker bees component with poor global search ability. Subsequently, an ABC algorithm without scout bees component and modified onlooker bees component is proposed in this paper. The effectiveness and efficiency of the proposed ScoutlessABC is validated on test suite of a dozen of benchmark functions. To further evaluate the performance, ScoutlessABC is employed on the parameter training problem of fuzzy neural network for solving eight classification problems. The experimental results show that ScoutlessABC maintains strong convergence ability than the original ABC algorithm. Overall, this study has two major contributions: (a) an effective component-wise analysis approach using diversity measurement and (b) a simplified and modified ABC variant with enhanced search efficiency.