Integrating cumulative binomial probability into artificial bee colony algorithm for global optimization in mechanical engineering design

Abstract

The artificial bee colony (ABC) algorithm has gained significant attention in engineering design optimization for its simplicity and robustness. However, it suffers from low convergence accuracy and imbalanced exploration and exploitation, particularly in non-convex and multi-modal problems. To alleviate these shortcomings, we present a novel ABC algorithm that combines cumulative binomial probability (CBABC), which contains two versions, single-dimensional evolution (CBACB_S) and multi-dimensional evolution (CBABC_M). According to the success and failure evolution experience, we first introduce a scaling factor based on cumulative binomial probability. Sequentially new movement equations with different characteristics are designed in the onlooker bee phase to balance local and global search capabilities. Then, a novel abandoned solution update mechanism is defined during scout bee phase to partly improve the solution accuracy. Finally, our approaches achieve a minimum winning rate of 68.19% and a maximum winning rate of 95.45% against its 9 outstanding ABC variants across the 22 functions. In addition, the proposed algorithms maintain a winning rate within [57.89%, 100%] compared to several state-of-the-art evolutionary algorithms for tackling 19 real-world mechanical engineering problems from Congress on Evolutionary Computation 2020 (CEC2020) suite.