| Summary: | 碩士 === 中原大學 === 資訊管理研究所 === 101 === Artificial Bee Colony algorithm (ABC) is a collective intelligence algorithm which refers to bee swarm`s division of labor mode. Because ABC has more excellent performance than other algorithms especially the function optimization field, scholars have pay attention to it continuously since the first published.
The distinctive features of ABC include simple calculation, easy to implement, and low demand parameter settings. However, there are still some defects in it, such as regional search which was unable to improve continuously, falling into local optimal solutions, and low effectiveness exploring of scout bee. In this study, we adopt the opposition of group center to lead search for strange area. This strategy is beneficial to adjust search resource of algorithm. It regulates exploring principle according to current trend of evolution, promoting convergence speed and increasing opportunity for departing from local optimal solutions.
In this research, we use 5 generally acknowledged benchmark functions to test our method. The outcome demonstrates that our algorithm has good effect regardless of high and low dimension. It owns speedy and better resolving ability at multimodal functions.
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