本文旨在提出廣義性最佳化演算法探討與改良，其中廣義的最佳化演算法為最受重視，重要原因是適用範圍層面的問題，基因最佳化演算法、粒子群最佳化演算法、模擬退火法…等最常被使用，在規律的適應函式找極值上，每個方法各有優缺點，而優缺點取決於是否適應值符合標準和達到目標花費時間上的多寡，在不同的適應函式也各有不同的取向，基於以上的需求，本文也試著尋找更優於一般廣義性最佳化演算法的演算方式。
本論文嘗試以高度指向性配合強烈具跳出局部能力之方式，以此來達到搜索更迅速之目的，加上精簡演算法流程與使執行者使用上更方便設計的目標來完成此演算法，文末，吾人亦提出模擬以驗證本文改良法之可行性。

This study aims to explore the problems of strengthen-reverse and reduction for particle swarm optimization. Genetic algorithm, particle swarm optimization and simulated annealing are widely used to search for the global optimal solutions of fitness functions. The present work tries to make some improvements and to reduce the consuming time of the generalized optimization algorithms. Whether the generalized optimization algorithms are good or bad usually depends on the fitness function value.
This paper tried to use high pointing-behavior to make the speed of seeking out the global optimum being higher. But we need to increase the chance of escaping from the local optimal solutions. Final the new algorithms are as simplified as possible and that the user will apply these algorithms more easy than others. In addition, the simulation is given to verify the feasibility of the present method.

〔1〕 J Kennedy and R Eberhart, “Particle Swarm Optimization”, Proceedings of IEEE International Conference on Neural Networks, Vol. 4, pp. 1942-1948, 1995.
〔2〕 Shi Y and Eberhart R C, “A modified particle swarm optimizer”, IEEE World Congress on Computational Intelligence, pp. 69-73, 1998.
〔3〕 D. E. Goldberg, “Genetic Algorithm in Search, Optimization and Machine Learning”, Addison-Wesley Publishing Company, 1989.
〔4〕 S. Kirkpatrick, C. Gelatt Jr and M. Vecchi, “Optimization by simulated annealing”, Sci., Vol. 220, Issue. 4598, pp. 671–680, 1983.
〔5〕 Sachin Gupta and Swapna Devi, “Modified pso algorithm with high exploration and exploitation ability”, International journal of software engineering research & practices Vol. 1, Issue. 1, Jan 2011.
〔6〕 X. Cai, Y. Cui and Y. Tan, “Predicted modified pso with time-varying accelerator coefficients”, Int. J. Bio-Inspired computation, Vol. 1, No. 1/2, pp. 50–60,2009.
〔7〕 Sheng-Ta Hsieh, Tsung-Ying Sun, Chan-Cheng Liu and Shang-Jeng Tsai, “Efficient Population Utilization Strategy for Particle Swarm Optimizer”. IEEE Transactions on Systems, Man and Cybernetics, Vol. 39, Issue. 2, pp. 444–456, April 2009.
〔8〕 Zhi-Hui Zhan, Jun Zhang, Yun Li and Yu-Hui Shi, “Efficient Orthogonal Learning Particle Swarm Optimization”. IEEE Transactions on Evolutionary Computation, Vol. 15, Issue. 6, pp. 832–847, December 2011.
〔9〕 S. H. Ling, H. H. C. Iu, K. Y. Chan, H. K. Lam, Benny C. W. Yeung and Feank H. Leung, “Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications”. IEEE Transactions on Systems, Man and Cybernetics, Vol. 38, Issue. 3, pp. 743–763, June 2009.
〔10〕 Jang-Ho Seo, Chang-Hwan Im, Chang-Geun Heo, Jae-Kwang Kim, Hyun-Kyo Jung and Cheol-Gyun Lee, “Multimodal Function Optimization Based on Particle Swarm Optimization”. IEEE Transactions on Magnetics, Vol. 42, Issue. 4, April 2006.
〔11〕 Wei-Neng Chen, Jun Zhang, Ying Lin, Ni Chen, Zhi-Hui Zhan, Henry Shu-Hung Chung, Yun Li and Yu-Hui Shi, “Particle Swarm Optimization with an Aging Leader and Challengers”. IEEE Transactions on Evolutionary Computation, Vol. 17, Issue. 2, pp. 241–258, April 2013.
〔12〕 Chia-Feng Juang, “A Hybrid of Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design”. IEEE Transactions on Systems, Man and Cybernetics, Vol. 34, Issue. 2, April 2004.
〔13〕 Zhi-Hui Zhan, Jun Zhang, Yun Li and Henry Shu-Hung Chung, “Adaptive Particle Swarm Optimization”. IEEE Transactions on Systems, Man and Cybernetics, Vol. 39, Issue. 6, pp. 1362–1381, December 2009.
〔14〕 Wu-Chang Wu and Men-Shen Tsai, “Application of Enhanced Integer Coded Particle Swarm Optimization for Distribution System Feeder Reconfiguration”. IEEE Transactions on Power Systems, Vol. 26, Issue. 3, pp. 1591–1599, August 2011.
〔15〕 Changhe Li, Shengxiang Yang and Trung Thanh Nguyen, “A Self-Learning Particle Swarm Optimizer for Global Optimization Problems”. IEEE Transactions on Systems, Man and Cybernetics, Vol. 42, Issue. 3, pp. 627–646, June 2012.
〔16〕 Zhi-Hui Zhan, Jun Zhang, Yun Li and Yu-Hui Shi, “Orthogonal Learning Particle Swarm Optimization”. IEEE Transactions on Evolutionary Computation, Vol. 15, Issue. 6, pp. 832–847, December 2011.
〔17〕 Irina Ciornei and Elias Kyriakides, “Hybrid Ant Colony-Genetic Algorithm for Global Continuous Optimization”. IEEE Transactions on Systems, Man and Cybernetics, Vol. 42, Issue. 1, pp. 997–1006, February 2012.
〔18〕 Guangming Lv, Xiaomeng Sun and Jian Wang, “A Simulated Annealing-New Genetic Algorithm and its Application”. Interational Conference on Electronics and Optoelectronics, pp. 246–249, 2011.
〔19〕 Adham Atyabi and David M W Powers, “The Use of Area Extended Particle Swarm Optimization (AEPSO) in Swarm Robotics”. Int. Conf. Control, Automation, Robotice and Vision, pp. 591–596, 2010.
〔20〕 Yau-Tarng Juang, Shen-Lung Tung, and Hung-Chih Chiu, “An Improved Particle Swarm Optimization Algorithm with Adaptive Fuzzy Strategy”. The 11th Cross-Strait Information Technology Conference, pp. 128-133, December 2009.
〔21〕 Shen-Lung Tung, Yau-Tarng Juang, Wei-Hsun Lee, Wern-Yarng Shieh, and Wei-Ying Wu, “Optimization of the exponential stabilization problem in active suspension system using PSO”. Expert Systems with Applications, Vol. 38, Issue. 11, pp. 14044-14051, October 2011.
〔22〕 Yangguang Fu, Mingyue Ding, and Chengping Zhou, “Phase Angle-Encoded and Quantum-Behaved Particle Swarm Optimization Applied to Three-Dimensional Route Planning for UAV”. IEEE Transactions on Systems, Man and Cybernetics, Vol. 42, Issue. 2, pp. 511–526, March 2012.
〔23〕 網路資料：維基百科-基因演算法
https://zh.wikipedia.org/wiki/%E9%81%97%E4%BC%A0%E7%AE%97%E6%B3%95.
〔24〕 網路資料：維基百科-粒子群優化
http://zh.wikipedia.org/wiki/%E7%B2%92%E5%AD%90%E7%BE%A4%E4%BC%98%E5%8C%96.
〔25〕 網路資料：模擬退火法(simulated annealing)
http://jjcommons.csie.isu.edu.tw/research/download/SA.pdf‎