The search equations and the local search procedures used in Artificial Bee Colony (ABC) algorithm are two key components that affect the performance of the algorithm. However, there is no search equation or local search that provides good results for all problem types. In this article, an ABC algorithm called “Self-adaptive Search Equation-based Artificial Bee Colony” (SSEABC) is proposed which can determine the appropriate local search procedure and the search equation internally during execution. SSEABC integrates three strategies into the canonical ABC algorithm. The first strategy is a self-adaptive strategy that determines the appropriate search equations for a particular problem by eliminating improper ones from a pool consisting of randomly generated search equations. The second strategy is a competitive local search selection. It decides the most effective local search procedure by comparing the performances of SSEABC, Mtsls1 and IPOP-CMA-ES. The third strategy 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 SSEABC algorithm is tested on benchmark functions proposed in the CEC'14 abd CEC'17 competition on single objective bound constrained real-parameter numerical optimization. SSEABC is compared with several ABC variants, competitor algorithms of CEC'14 and CEC'17, and several state-of-the-art algorithms. Finally, we applied SSEABC to the infinite impulse response (IIR) system identification problem as an engineering application. The results showed the superiority of the SSEABC algorithm.

人工蜂群（ABC）算法中使用的搜索方程式和局部搜索程序是影响算法性能的两个关键组成部分。但是，没有可以为所有问题类型提供良好结果的搜索方程式或局部搜索。在本文中，提出了一种称为“基于自适应搜索方程的人工蜂群”的ABC算法（SSEABC），该算法可以在执行过程中内部确定合适的局部搜索过程和搜索方程。SSEABC将三种策略集成到规范的ABC算法中。第一种策略是自适应策略，它通过从由随机生成的搜索方程式组成的池中消除不适当的搜索方程式来确定特定问题的适当搜索方程式。第二种策略是竞争性的本地搜索选择。它通过比较SSEABC，Mtsls1和IPOP-CMA-ES的性能来决定最有效的本地搜索过程。第三个策略是增量人口规模策略，该策略基于在预定次数的迭代之后添加位于迄今为止最好的食物来源位置附近的新食物来源。这有助于提高收敛速度。SSEABC算法在CEC'14和CEC'17竞赛中提出的基准函数上进行了测试，该基准函数是对单个目标范围约束的实参数数值优化的。将SSEABC与几种ABC变体，CEC'14和CEC'17的竞争对手算法以及几种最新算法进行了比较。最后，我们将SSEABC应用于工程上的无限冲激响应（IIR）系统识别问题。