# Self-adaptive Artificial Bee Colony with a Candidate Strategy Pool  
[PERSON]  
1 School of Computer Science, Central China Normal University, Wuhan 430079, China; [EMAIL_ADDRESS] (Y.H.); [EMAIL_ADDRESS] (Y.Y.); [EMAIL_ADDRESS] (J.G.)  
[PERSON]  
1 School of Computer Science, Central China Normal University, Wuhan 430079, China; [EMAIL_ADDRESS] (Y.H.); [EMAIL_ADDRESS] (Y.Y.); [EMAIL_ADDRESS] (J.G.)  
[PERSON]  
1 School of Computer Science, Central China Normal University, Wuhan 430079, China; [EMAIL_ADDRESS] (Y.H.); [EMAIL_ADDRESS] (Y.Y.); [EMAIL_ADDRESS] (J.G.)  
[PERSON]  
2 School of Automation, Wuhan University of Technology, Wuhan 430070, China; [EMAIL_ADDRESS] 2*2  
* In comparison to other SAs, ABC has a sluggish convergence, due to the 1-D update in the search equation. It is crucial to figure out how to increase the convergence speed to improve ABC's performance. One difficulty that should be addressed is how to increase the algorithm's convergence speed, while maintaining high performance, by enhancing the method;
* The problems that can be solved using the artificial bee colony algorithm are limited in variety, due to the simplicity and singularity of the ABC algorithm's updating method;
* According to certain pertinent literature studies [13], ABC has a significant capacity for exploration because of a single search equation in the evolution process. As a result, a popular area of research is how to improve exploitation, while maintaining exploration.  
Many ABC variants have been designed to address these deficiencies, and the modified techniques can be divided into three groups: modifying search equations [14], assembling a multi-strategy [15], and hybridizing other metaheuristic search frameworks [16].  
In order to resolve these difficult optimization issues, this study suggests a novel variation of ABC called SAABC-CS, which stands for self-adaptive ABC with a candidate strategy pool. Its main characteristics can be summed up as follows:  
* Five alternative search methods are combined to generate a candidate strategy pool that improves ABC's exploitation capability, without sacrificing exploration capability. In addition, we include multi-dimensional updates in each strategy, which considerably increases the frequency of individual updates and boosts the convergence speed. A self-adaptive method is also suggested for choosing the right search technique. The knowledge from the previous information is used to adaptively update the selection probability of each strategy;
* Our approach, in contrast to other algorithms, performs quite well, without adding additional control parameters when applying each strategy, which is aligned to the artificial bee colony program's original intention--simplicity and effectiveness;
* By improving the method, we make it more useful for solving actual, practical issues in the real world.  
The remaining part of this paper is divided into the following sections: The works pertaining to the fundamental ABC and its variations are detailed in Section 2. In Section 3, the suggested algorithm SAABC-CS is described. The effectiveness of our suggested approach and the analysis of the results of our algorithm in comparison to other algorithms are provided in Section 4. The last Section provides a summary of our work.