EABC-AS: Elite-driven artificial bee colony algorithm with adaptive population scaling

Abstract

The Artificial Bee Colony Algorithm (ABC) is a widely recognized optimization algorithm known for its effectiveness. However, many variants of the ABC algorithm fail to fully leverage the potential of each population, and their inherent random search strategies often limit the algorithm’s convergence capabilities, leading to diminished performance. To address these issues, we introduce an enhanced version of the ABC algorithm, which incorporates two essential features: adaptive population scaling and an elite-driven evolutionary strategy. The adaptive population scaling mechanism dynamically adjusts the population size of each bee colony based on their respective function, and the elite-driven evolutionary strategy with external archive makes bees evolve by utilizing information from elite individuals while ensuring diversity is maintained. These two features enhance the algorithm’s convergence ability. We employ the CEC 2017 and CEC 2022 benchmarks to assess the optimization capabilities of the proposed algorithm. The experimental results indicate that the EABC-AS algorithm displays significant competitiveness relative to CEC excellent algorithms and other state-of-the-art (SOTA) algorithms.