依據足球運動員的移動行為探討最佳化移動策略

An optimization strategy based on the movement behavior of soccer players

游漢杰(Hindriyanto Dwipurnomo)

指導教授 ： 黃惠民

中原大學/電機資訊學院/工業與系統工程研究所/博士(2013年)

https://doi.org/10.6840/cycu201300234

摘要

最佳化在科學、工程和經濟問題上是相當重要的方法。最佳化方法有很多種，因不斷產生的最佳化問題，其數量仍持續增加。基於足球運動員的動作行為，本研究提出一個新的最佳化方法；此新方法有兩個基本動作，用“離開”和“前進”控制廣域與集中搜尋；除了動作外，此演算法具有運動員替換程序，對附近的一組優良解進行搜尋；一般來說，本研究提出的方法闡述了進化演算法中的競爭和複製過程，具有群體智慧演算法之強大的資訊共享；實作的範例中提供連續和不連續的問題，實驗結果展現出本方法具有成為一個強大的最佳化方法之潛力。作為一種新方法，本演算法可用不同的方式來加強，例如：發展更多種類的球員動作，以及實施本方法來解決各種最佳化問題。

關鍵字

群體智慧演算法之 ； 離開 ； 前進 ； 足球運動員的動作行為 ； 資訊共享 ； 進化演算法中

並列摘要

Optimization is important in science, engineering and economic problems. There are many optimization methods and their numbers increase continuously due to the growing optimization problems. A new optimization method based on the movement behavior of soccer players is proposed in this study. The new method has two basic movements; the move off and the move forward; which are used to control the diversification and intensification. Beside the movements, the algorithm has player substitution procedure which will impose the search process into nearby a set of goodsolution. In general, the proposed method elaborates the competition and the reproduction process in evolutionary algorithm with the powerful information sharing of swarm intelligence algorithm. Examples of implementations are provided for continuous and discrete problems. The experiment results reveal that the proposed method has the potential to become a powerful optimization method. As a new method, the proposed algorithm can be enhanced in many different ways such as elaborating more aspects of the soccer players’ movement as well as implementing the proposed method to solve various optimization problems.

並列關鍵字

soccer player movements ； move off ； swarm intelligence ； information sharing ； move forward ； evolutionary algorithm

參考文獻

Yang, X. S. (2010). A New Metaheuristic bat-inspired algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010) (Eds. J. R. Gonzalez et al.), Studies in Computational Intelligence, Springer Berlin, 284, Springer, 65-74.

Akanle, O.M., Zhang, D.Z. (2008). Agent-based model for optimizing supply-chain configurations. International Journal Production Economic, 115, 445-460.

Akpınar, S., Bayhan, G. M. (2011).A hybrid genetic algorithm for mixed model assembly line balancing problem with parallel workstations and zoning constraints. Engineering Applications of Artificial Intelligence, 24 (3), 449–457.

Alatas, B. (2010).Chaotic harmony search algorithms.Applied Mathematics and computations, 216, 2687-2699.

Albayrak, M., Allahverdi, N. (2011).Development a new mutation operator to solve the travelling salesman problem by aid of genetic algorithms.Expert Systems with Applications, 38, 1313-1320.