改良式帝國主義競爭演算法

Improved Imperialist Competitive Algorithm

李朝源(Chau-Yuan Lee)

指導教授 ： 劉俞志

元智大學/資訊學院/資訊管理學系/碩士(2011年)

https://doi.org/10.6838/YZU.2011.00089

摘要

近年來有許多種類之最佳化方法，其中較為知名的有基因演算法(Genetic Algorithm, GA)、粒子群最佳化(Particle Swarm Optimization, PSO)及蟻群最佳化演算法(Ant Colony Optimization, ACO)、差異進化演算法(Differential Evolution Algorithm, DEA)，這些演算法都是觀察自然界生物活動之習性，利用電腦模擬而成之最佳化演算法；而近年來有研究學者觀察人類歷史中帝國與殖民地之資源競爭現象，並加以實作而成帝國主義競爭演算法(Imperialist Competitive Algorithm, ICA)，其效能表現也相當優越；而本研究主要為改良帝國主義競爭演算法殖民地之移動方式之缺陷，經過實驗測試後，改良後之帝國主義競爭演算法能夠獲得進一步的效能提升。

關鍵字

最佳化 ； 基因演算法 ； 粒子群最佳化 ； 蟻群最佳化 ； 差異進化演算法 ； 帝國主義競爭演算法

並列摘要

Many nature-inspired optimization methods, such as Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization and Differential Evolution Algorithm have received much attention for the past few decades. These algorithms are based on computer simulation of biological activity. Recently, a new nature-inspired optimization method, called Imperialist Competition Algorithm (ICA), was proposed. ICA is based on the resource competition among colonial empires. This study modifies how colonies move in ICA to reduce the chance of falling into local optimum. Our experimental results show that the proposed method outperforms ICA.

並列關鍵字

Ant Colony Optimization ； Differential Evolution Algorithm ； Imperialist Competitive Algorithm ； Optimization ； Genetic Algorithm ； Particle Swarm Optimization

參考文獻

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