Design and development of a unified framework towards swarm intelligence

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• 作者：Shuzhu Zhang ; C. K. M. Lee ; K. M. Yu ; H. C. W. Lau
• 关键词：Swarm intelligence ; Optimization problem ; Meta ; heuristics ; Unified framework ; Intensification and diversification
• 刊名：Artificial Intelligence Review
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：47
• 期：2
• 页码：253-277
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence (incl. Robotics); Computer Science, general;
• 出版者：Springer Netherlands
• ISSN：1573-7462
• 卷排序：47

文摘

The application of swarm intelligence (SI) in the optimization field has been gaining much popularity, and various SI algorithms have been proposed in last decade. However, with the increased number of SI algorithms, most research focuses on the implementation of a specific choice of SI algorithms, and there has been rare research analyzing the common features among SI algorithms coherently. More importantly, no general principles for the implementation and improvement of SI algorithms exist for solving various optimization problems. In this research, aiming to cover such a research gap, a unified framework towards SI is proposed inspired by the in-depth analysis of SI algorithms. The unified framework consists of the most frequently used operations and strategies derived from typical examples of SI algorithms. Following the proposed unified framework, the intrinsic features of SI algorithms can be understood straightforwardly and the implementation and improvement of SI algorithms can be achieved effortlessly, which is of great importance in practice. The numerical experiments examine the effects of the possible strategies employed in the unified framework, and provide pilot attempts to validate the performance of different combinations of strategies, which can not only facilitate specific SI algorithm application, but also can motivate SI algorithm innovation.