Self-adaptive Search Equation-based Artificial Bee Colony Algorithm on the CEC 2014 Benchmark Functions

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.