Self-adaptive search equation-based artificial bee colony algorithm with CMA-ES on the noiseless BBOB testbed

Abstract

Self-Adaptive Search Equation based Arti cial Bee Colony (SSEABC) is a recent variant of Arti cial Bee Colony (ABC) algorithm. SSEABC proposed three enhancements on the canonical ABC algorithm. ese are the self-adaptive search equation selection strategy, hybridization with a local search procedure and incremental population size strategy. e performance of SSEABC is tested on CEC 2015 benchmark suite and ranked third within all participants of competition. In this paper, we benchmarks SSEABC using the noisefree BBOB function testbed. We also compare SSEABC performance to PSO, ABC and GA algorithms. CCS CONCEPTS •Computing methodologies → Continuous space search;

Adaptive iir filter design using self-adaptive search equation based artificial beecolony algorithm

“…In order to overcome this problem, a self-adaptive search equation generation method which can find the appropriate search equation related to the nature of the problem is needed. In our previous work, the self-adaptive search-equation-based artificial bee colony (SSEABC) algorithm was designed for this purpose and achieved successful results in several types of benchmark continuous optimization functions [28,29]. In this study, the SSEABC algorithm is applied to IIR filter design problem.…”