以PSO為基之帝國主義競爭演算法

PSO-based Imperial Competitive Algorithm

余俊瑩(Chun-Ying Yu)

指導教授 ： 林志麟

元智大學/資訊學院/資訊管理學系/碩士(2012年)

https://doi.org/10.6838/YZU.2012.00092

摘要

本研究之目的在於改良帝國主義競爭演算法 (Imperialist Competitive Algorithm, ICA)。帝國主義競爭演算法不同於基因演算法 (Genetic Algorithm, GA)、粒子群最佳化(Particle Swarm Optimization, PSO)、蟻群最佳化演算法 (Ant Colony Optimization, ACO)都是觀察自然界生物演化的演算法，而帝國主義演算法是藉由觀察人類歷史帝國與殖民地互相資源競爭之行為模式而模擬出的演算法。帝國主義競爭演算法具有殖民地移動步伐大小不穩定以及初始帝國位置好壞之優劣等缺陷。本研究提出以PSO演算法為基礎結合ICA以及人造帝國等方法改良上述缺陷，經實驗測試後本研究方向，具有效提升ICA之效能。

關鍵字

帝國主義競爭演算法 ； 基因演算法 ； 粒子群最佳化 ； 蟻群最佳化演算法

並列摘要

The purpose is to improve the Imperialist Competitive Algorithm (ICA). Unlike Imperialist Competitive Algorithm, Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization are based on computer simulation of biological activity. Imperialist Competitive Algorithm is based on the resource competition among colonial empires. This study proposes PSO-based Imperialist Competitive Algorithm and Artificial Imperialist method. Our experimental results show that the proposed methods outperform ICA.

並列關鍵字

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

參考文獻

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