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研究生:許富淵
研究生(外文):Fu-Yuan Hsu
論文名稱:應用進化規劃多目標演算法於配電系統饋線重構之研究
論文名稱(外文):A Study on the Applications of Multi-Objective Evolutionary Programming on Distribution System Feeder Reconfiguration Problems
指導教授:蔡孟伸蔡孟伸引用關係
指導教授(外文):Men-Shen Tsai
口試委員:林惠民盧展南楊宏澤洪穎怡吳啟瑞陳在相
口試日期:2012-07-16
學位類別:博士
校院名稱:國立臺北科技大學
系所名稱:機電科技研究所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:125
中文關鍵詞:輻射狀配電系統配電系統饋線重構多目標最佳化問題軟性計算進化規劃法交配/突變有條件突變灰關聯分析法極差方案與極優方案非支配排序
外文關鍵詞:Radial Power Distribution SystemMulti-Objective Optimization ProblemSoft ComputingEvolutionary Programming (EP)Crossover/MutationConditional MutationGrey Relational Analysis (GRA)Inferior SolutionSuperior SolutionPareto Non-dominated SortingNSGA-IIEP-GCRANSEP
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配電系統饋線重構問題是一種藉由尋求改變開關狀態的策略來達成改善配電系統運轉性能的最佳化問題,因此已有許多研究提出以軟性計算 (Soft Computing) 的技巧來處理這樣的問題。在這些研究當中,基因演算法 (Genetic Algorithm, GA)或者進化規劃法(Evolutionary Programming, EP)是最常應用於此類最佳化問題的軟性計算方法。這些方法是仿效生物演化方式來搜尋最佳解,在演化過程中是利用交配/突變 (Crossover/Mutation) 等基因運算 (Gene Operations) 來產生新的個體。但是針對輻射狀架構之配電系統,因為具有由拓樸所衍生之等式限制條件 (Equality Constrains in the Topology),所以利用 GA 或 EP 來解決配電系統饋線重構問題時,常因基因運算而產生不符合拓樸限制條件的解。因此針對染色體,在演算法當中必須加入一個檢查與處理的機制。但不合法染色體的處理機制會耗費大量的演算法執行時間 (Processing Time),而導致演算法執行效率降低。另一方面,以往這些有關於配電系統饋線重構的研究,以單一目標的問題作為考量居多,這樣可迅速求得最佳或次佳解。但此方式往往忽略系統運作的實際情資,進而使該解成為不符合現實之無效解。通常配電系統調度人員期望在允許的情況下,重構策略可多方面改善配電系統的運轉能力,換言之,配電系統饋線重構問題應該以多目標 (Multi-Objective)為考量。因此本論文依照配電調度人員對於核心技術的期望,發展適合輻射狀配電系統之多目標饋線重構演算法,演算法核心方法係利用仿效生物演化方式的演算法來求解饋線重構之最佳解。為了要避免基因運算所導致之不合法解的產生,本論文發展一種以“有條件式突變 (Conditional Mutation)”基礎的 EP來達成;另一方面,針對多目標而言,配電系統調度人員可能對於饋線重構解的需求有二:1)多目標整合解;2)最佳化解群。對於此兩個需求,本論文分別發展以 “灰關聯度 (Grey Relational Analysis, GRA)” 配合極差方案 (Inferior Solution) 與極優方案 (Superior Solution) 之方法,以及引用 “非支配排序基因演算法-II (Non-dominated Sorting Genetic Algorithm-II, NSGA-II)” 之適應值計算方法來結合本論文所提出的 EP 發展出 EP-GCRA 與 NSEP 來達成。

The feeder reconfiguration problem of power distribution system is an optimization problem which can achieve the improvement of the operational performance on the power distribution system by searching a strategy of changing the switches states. Many studies were proposed by utilizing the soft computing techniques to solve this problem. In these studies, Genetic Algorithm (GA) and Evolutionary Programming (EP) are two popular methods that have been applied for this optimization problem. The operators used in GA or EP emulate the biological evaluation to solve the optimization problems. In the traditional evolutionary process, the new individual can be generated by the gene operations, such as, crossover and mutation. However, for the problems that deal with radial power distribution systems, cares must be taken due to its topological constraint. Thus, when the GA or EP was applied to solve the feeder reconfiguration problem of power distribution system, the solutions that do not satisfy the topological constraints of the problem may be generated by genetic operations. A mechanism of chromosome validation process must be used in these algorithms before calculating the fitness values. However, the validation process takes the largest portion of the processing time. As a result, the performance of these algorithms is reduced. On the other hand, most of previous studies only considered single objective when deal with the feeder reconfiguration problems of power distribution systems. Nevertheless, this consideration ignores the some information in the real system. In reality, the power distribution system dispatchers expect that the different proposed strategies can be chosen based on their experience and conditions. Therefore, the feeder reconfiguration problem of power distribution system must take the multi-objective into consideration. A multi-objective feeder reconfiguration algorithm for redial power distribution system is developed in this dissertation. The emulation of biological evaluation algorithm is applied to solve the multi-objective feeder reconfiguration problems. In order to avoid the illegals be generated by the genetic operations, an Evolutionary Programming based on the “Conditional Mutation” is proposed in this dissertation. When solving the multi-objective problems, two approaches can be applied: 1) Integrating multi-objective into single-objective. 2) Identifying all solutions by considering all objectives. In this dissertation, the EP-GCRA and the NSEP are proposed respectively. The EP-GCRA algorithm applies the grey relational analysis by proposing the concept of inferior and superior solutions that integrate all the objectives for the multi-objective problems. For the second approach, the NSEP algorithm that utilizes the fitness value calculation used in the NSGA-II and the special “Conditional Mutation” operator proposed in this dissertation are developed to identify the optimal solution set. The EP approach is applied for these two algorithms. The results show that the application of EP performs better than traditional GA approaches.

摘要.................................................... i
Abstract............................................... iii
目錄.................................................... ix
表目錄................................................... xi
圖目錄................................................... xiii
第一章 緒論............................................ 1
1-1 研究動機......................................... 1
1-2 研究背景與目的.................................... 4
1-3 文獻回顧......................................... 5
1-4 研究方法......................................... 7
1-5 內容概要......................................... 9
第二章 配電系統饋線重構問題之探討.......................... 11
2-1 前言............................................ 11
2-2 配電系統饋線重構.................................. 14
2-2-1 配電系統饋線重構之目的.............................. 14
2-2-2 配電系統饋線重構之達成.............................. 17
2-3 配電系統饋線重構之目標.............................. 18
2-3-1 配電系統通過電流估算模型之描述....................... 19
2-3-2 配電系統之損失估算................................. 21
2-3-3 配電系統之最大末端壓降估算.......................... 21
2-3-4 配電系統之平衡度估算............................... 22
2-3-5 配電系統之饋線重構的開關操作次數..................... 23
第三章 求解多目標配電系統饋線重構之進化規劃演算法............. 24
3-1 前言............................................ 24
3-2 多目標配電系統饋線重構問題.......................... 26
3-2-1 多目標最佳化問題.................................. 27
3-2-2 多目標配電系統饋線重構.............................. 29
3-3 應用於配電系統饋線重構的染色體編/解碼之探討............ 29
3-3-1 以開關狀態之編碼.................................. 30
3-3-2 以常開開關編號之編碼............................... 32
3-3-3 “以開關狀態之編碼”與“以常開開關編號之編碼”對於基因運算的影響之探討.................................................... 33
3-3-4 以供電來源區域之編碼............................... 34
3-4 以進化規劃演算法為基礎之配電系統饋線重構演算法.......... 36
3-4-1 進化規劃演算法之概述............................... 37
3-4-2 進化規劃演算法之流程............................... 38
3-4-3 應用於配電系統饋線重構之染色體突變程序................. 42
3-5 灰關聯度分析為基礎之整合式多目標配電系統饋線重構進化規劃演算法......................................................48
3-5-1 整合式多目標最佳化問題.............................. 49
3-5-2 整合式多目標最佳化問題之適應函數值.................... 50
3-5-3 一般性多目標整合之方法的探討......................... 52
3-5-4 灰關聯度分析...................................... 55
3-5-5 針對配電系統饋線重構之灰關聯尺度的定義................. 57
3-5-6 以灰關聯度分析為基礎之整合式多目標配電系統饋線重構進化規劃演算法的流程................................................... 60
3-6 柏拉圖前緣為基礎搜尋多目標配電系統饋線重構之解群的進化規劃演算法 NSEP................................................... 63
3-6-1 以目標為向量之最佳化問題............................ 64
3-6-2 柏拉圖前緣與柏拉圖最佳解群.......................... 66
3-6-3 非支配排序的基因演算法之概述......................... 67
3-6-4 針對配電系統饋線重構之非支配排序的進化規劃演算法........ 71
第四章 模擬與結果....................................... 73
4-1 前言............................................ 73
4-2 模擬情境一----整合式多目標配電系統饋線重構問題......... 73
4-2-1 效能比較......................................... 76
4-2-2 利用台電之饋線來評估效能............................ 77
4-2-3 以一個大型的配電系統來評估效能....................... 87
4-3 模擬情境二----多目標配電系統饋線重構問題之最佳解群...... 92
4-3-1 演算法針對一個小型配電系統之效能比較.................. 93
4-3-2 演算法針對一個大型配電系統之效能比較.................. 97
4-3-3 NSEP與最好的參數設定下之 NSGA-II 針對一個大型配電系統之效能比較...................................................... 101
第五章 結論............................................ 109
參考文獻................................................. 113
附錄 中英文專有名詞對照表................................... 123

[1] S. Toune, H. Fudo, T. Genji, Y. Fukuyama and Y. Nakanishi, “Comparative study of modern heuristic algorithms to service restoration in distribution systems,” IEEE Transactions on Power Delivery, Vol. 17, No. 1, January 2002, pp. 173-181.
[2] M. Paar and P. Toman, “Utilization of particle swarm optimization algorithm for optimization of MV network compensation,” Power Tech, 2007 IEEE Lausanne, Switzerland, July 1-5 2007, pp. 1991-1995.
[3] Th. Back, G. Rudolph and H. P. Schwefel, “Evolutionary programming and evolution strategies: Similarities and differences,” In Proceedings of the Second Annual Conference on Evolutionary Programming, La Jolla, CA, 1993, pp. 11-22.
[4] W. H. Chen, M. S. Tsai and J. A. Jiang, “Preference ranking for restoration plans in distribution systems based on grey relational analysis,” Journals on Grey Systems, Vol. 16, No. 4, 2004, pp. 29-34.
[5] W. H. Chen and M. S. Tsai, “A novel approach to multi-objective network reconfiguration,” in Proc. Int. Conf. Advanced Power System Automation and Protection, Jeju, Korea, October 25-28 2004, pp. 503-506.
[6] Y. T. Hsiao, “Multiobjective evolution programming method for feeder reconfiguration,” IEEE Transactions on Power Systems, Vol. 19, No. 1, February 2004, pp. 594-599.
[7] Y. T. Hsiao and C. Y. Chien, “Enhancement of restoration service in distribution system using a combination fuzzy-GA method,” IEEE Transactions on Power Systems, Vol. 15, No. 4, November 2000, pp. 1394-1400.
[8] M. S. Tsai and F. Y. Hsu, “Application of grey correlation analysis in evolutionary programming for distribution system feeder reconfiguration,” IEEE Transactions on Power Systems, Vol. 25, No. 2, May 2010, pp. 1126-1133.
[9] P. Ngatchou, Z. Anahita and M. A. EI-Sharkawi, “Pareto Multi Objective Optimization,” 13th International Conference on Intelligent System Application to Power Systems (ISAP 2005), November 6-10 2005, pp. 84-91.
[10] C. A. C. Coello, “A Comprehensive survey of Evolutionary-Based Multi-objective Optimization Techniques,” Knowledge and Information System, Vol. 1, No. 3, August 1999, pp. 269-308.
[11] C. Fonseca and P. J. Fleming, “Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion, Generalization,” Proceedings of the fifth International Conference on Genetic Algorithms, San Mateo, CA, 1993, pp. 416-423.
[12] N. Srinivas and K. Deb, “Multiobjective Function Optimization Using Nondominated Sorting Genetic Algorithms,” Evolutionary Computation, Vol. 2, No. 3, 1994, pp. 221-248.
[13] J. Hom, N. Nafpliotis and D. E. Goldberg, “A Niched Pareto Genetic Algorithm for Multi-objective Optimization,” Proceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, Piscataway, NJ., 1994, pp. 82-87.
[14] E. Zitzler and L. Thiele, “An Evolutionary Algorithm for Multi-objective Optimization: The Strength Pareto Approach,” TIK Tech. Report No. 43, Swiss Federal Institute of Technology (ETH), 1998.
[15] J. Kennedy and R. C. Eberhart, Swarm Intelligence: Elsevier/Morgan Kauffman, 2001.
[16] M. P. Song and G. C. Gu, “Research on Particle Swarm Optimization: A Review,” Proceedings of the Third International Conference on Machine Learning and Cybernetics, 2004, pp. 2216-2241.
[17] C. A. C. Coello, G. T. Pulido and M. S. Leehuga, “Handling multiple objectives with particle swarm optimization,” IEEE Transactions on Evolutionary Computation, Vol. 8, No. 3, 2004, pp. 256-279.
[18] 許富淵,改良式基因演算應用於配電系統重構問題之探討,碩士論文,中原大學,中華民國九十三年。
[19] K. Nara, Y. Mishma and T. Satoh, “Network Reconfiguration for Loss Minimization and Load Balancing,” IEEE Power Engineering Society Winter Meeting, July 2003, pp. 2413-2418.
[20] S. Toune, H. Fudo, T. Genji and Y. Fukuyama, “Comparative Study of Modern Heuristic Algorithms to Service Restoration in Distribution Systems,” IEEE Transactions on Power Delivery, Vol. 17, No. 1, January 2002, pp. 173-181.
[21] H. Iba, T. Sato and H. de Grapis, “System Identification Approach to Genetic Programming,” IEEE World Congress on Computational Intelligence, June 1994, pp. 401-406.
[22] P. Thomson and J. F. Miller, “Comparison of AND-XOR Logic synthesis using a Genetic Algorithm against MISII for Implementation on FPGAs,” IEE Genetic Algorithms in Engineering System, September 1997, pp. 278-282.
[23] R. Zebulum, A. Stoica and D. Keymeulen,“Experiments on the Evolution of Digital to Analog Converters,” Aerospace Conference, 2001, IEEE Proceedings, March 2001, pp. 2321-2331.
[24] P. Chalermwat and T. El-Ghazawi, “Multi-resolution Image Registration Using Genetics,” 1999 International Conference on Image Processing (ICIP 99), October 1999, pp. 452-456.
[25] Z. Sum, G. Bebis, X. Yuan and S. J. Louis, “Genetic Feature Subset Selection for Gender Classification: A Comparison Study,” Sixth IEEE Workshop Proceedings on Applications of Computer Vision (WACV 2002), December 2002, pp. 165-170.
[26] C. F. Juang, J. Y. Lin and C. T. Lin, “Genetic reinforcement learning through symbiotic evolution for fuzzy controller design,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 30, April 2000, pp. 290-302.
[27] C. T. Lin and C. P. Jou, “GA-based fuzzy reinforcement learning for control of a magnetic bearing system,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 30, April 2000, pp. 276-189.
[28] V. Petridis, S. Kazarlis and A. Bakirtzis, “Varying fitness functions in genetic algorithm constrained optimization: the cutting stock and unit commitment problems,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 28, October 1998, pp. 629-640.
[29] A. Rudolf and R. Bayrleithner, “A genetic algorithm for solving the unit commitment problem of a hydro-thermal power system,” IEEE Transactions on Power Systems, Vol. 14, November 1999, pp. 1460-1468.
[30] H. T. Yang, P. C. Yang and C. L. Huang, “A parallel genetic algorithm approach to solving the unit commitment problem: implementation on the transputer networks” IEEE Trans on Power Systems, Vol. 12 , No. 2 , May 1997, pp. 661-668.
[31] Y. Y. Hong and C. Y. Li, “Genetic algorithms based economic dispatch for cogeneration units considering multiplant multibuyer wheeling,” IEEE Transactions on Power Systems, Vol. 17, February 2002, pp. 134-140.
[32] T. Yalcinoz and H. Altun, “Power economic dispatch using a hybrid genetic algorithm,” IEEE Transactions on Power Engineering Review, Vol. 21, March 2001, pp. 59-60.
[33] Z. L. Gaing, “Particle swarm optimization to solving the economic dispatch considering the generator constraints,” IEEE Transactions on Power Systems, Vol. 18, August 2003, pp.1187-1195.
[34] M. A. Abido and Y. L. Abdel-Magid, “Hybridizing rule-based power system stabilizers with genetic algorithms,” IEEE Transactions on Power Systems, Vol. 14, May 1999, pp. 600-607.
[35] M. A. Abido and Y. L. Abdel-Magid, “Tuning of a fuzzy logic power system stabilizer using genetic algorithms,” IEEE International Conference on Evolutionary Computation, April 1997, pp. 595-599.
[36] K. Nara, “Genetic algorithm for power systems planning,” Fourth International Conference on Advances in Power System Control, Operation and Management (APSCOM-97), November 1997, pp. 60-65.
[37] N. Samaan and C. Singh, “A new method for composite system annualized reliability indices based on genetic algorithms,” 2002 IEEE on Power Engineering Society Summer Meeting, July 2002, pp. 850-855.
[38] M. Bettayeb and Uvais Qidwai, “A Hybrid Least Squares-GA-Based Algorithm for Harmonic Estimation,” IEEE Transactions on Power Delivery, Vol. 18, No. 2, April 2003, pp. 377-382.
[39] C. F. Juang and C. F. Lu, “Power system load frequency control with fuzzy gain scheduling designed by new genetic algorithms,” IEEE International Conference on Fuzzy Systems (FUZZ-IEEE''02), May 2002, pp. 64-68.
[40] Y. Fukuyama, H. Endo, and Y. Nakanishi, “A hybrid system for service restoration using expert system and genetic algorithm,” International Conference on Intelligent Systems Applications to Power Systems, 1996, pp. 394-398.
[41] Y. Fukuyama and H. D. Chiang, “A parallel genetic algorithm for service restoration in electric power distribution systems,” International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and The Second International Fuzzy Engineering Symposium, Proceedings of 1995 , Vol. 1, pp. 275-282.
[42] 劉志文,陳裕達,“應用交談式基因演算法於配電系統多目標復電問題”,中華民國第二十一屆電力研討會,中華民國八十九年十一月,第151-154頁。
[43] K. N. Miu, H. D. Chiang and G. Darling, “Capacitor Placement, Replacement and Control in Large-Scale Distribution Systems by a GA-Based Tow-Stage Algorithm,” IEEE Transactions on Power Systems, Vol. 12, No. 3, August 1997, pp. 1160-1166.
[44] B. Milosevic and M. Begovic, “Nondominated Sorting Genetic Algorithm for Optimal Phasor Measurement Placement,” IEEE Transactions on Power systems, Vol. 18, No. 1, February 2003, pp. 69-75.
[45] 柯裕隆,應用派翠網路於配電系統開關操作策略制定之研究,博士論文,國立中山大學,中華民國九十年。
[46] A. L. Morelato and A. Monticelli, “Heuristic Search Approach to Distribution System Restoration,” IEEE Transactions on Power Delivery, Vol. 4, No. 4, October 1989, pp. 2235-2241.
[47] 洪坤玉,隨機搜尋與對局理論解析配電系統多目標復電計劃,碩士論文,中原大學,中華民國八十七年。
[48] M. S. Tsai, “Development of an object-oriented service restoration expert system with load variations,” IEEE Transactions on Power Systems, Vol. 23, No. 1, February 2008, pp. 219-225.
[49] H. Fudou, T. Genji, Y. Fukuyama and Y. Nakanishi, “A genetic algorithm for network reconfiguration using three phase unbalanced load flow,” in Proc. Intelligent Systems Application to Power Systems, July 1997, pp. 458-462.
[50] S. K. Goswami and S. K. Basti, “A new algorithm for reconfiguration of distribution feeders for loss minimization,” IEEE Transactions on Power Delivery, Vol. 7, No. 3, July 1992, pp. 1484-1490.
[51] W. M. Lin and H. C. Chin, “A new approach for distribution feeder reconfiguration for loss reduction and service restoration,” IEEE Transactions on Power Delivery, Vol. 13, No. 3, July 1998, pp. 870-875.
[52] M. E. Baran and F. F. Wu, “Network reconfiguration in distribution systems for loss reduction and load balancing,” IEEE Transactions on Power Delivery, Vol. 4, No. 2, April 1989, pp. 1401-1407.
[53] D. Shirmohanunadi and E. I. W. Hong, “Reconfiguration of electrical distribution network for resistive line losses reduction,” IEEE Transactions on Power Delivery, Vol. 4, No. 3, July 1989, pp. 1492-1498.
[54] R. F. Chang and C. N. Lu, “Feeder reconfiguration for load factor improvement,” IEEE Power Engineering Society Winter Meeting, 2002, pp. 980-984.
[55] T. Murata and H. Ishibuchi, “MOGA: multi-objective genetic algorithms,” Proceedings of IEEE International Conference on Evolutionary Computation, 1995, pp. 289.
[56] K. Nara, A. Shiose, M. Kitagawa and T. Ishihara, “Implementation of genetic algorithm for distribution systems loss minimum re-configuration,” IEEE Transactions on Power Systems, Vol. 7, No. 3, August 1992, pp. 1044-1051.
[57] M. S. Tsai and F. Y. Hsu, “Comparison of genetic algorithm reproduction methods for distribution system loss minimization,” in Proceedings 2004 International Conferences on Machine Learning and Cybernetics, August 26-29 2004, pp. 4113-4118.
[58] F. Y. Hsu and M. S. Tsai, “A multi-objective evolution programming method for feeder reconfiguration of power distribution system,” in Proceedings 13th International Conferences on Intelligent Systems Application to Power Systems, November 6-10 2005, pp. 55-60.
[59] T. Q. D. Khoa and P. T. T. Binh, “A hybrid ant colony search based reconfiguration of distribution network for loss reduction,” Transmission & Distribution Conference and Exposition: Latin America (TDC ''06. IEEE/PES), August 15-18 2006, pp. 1-7.
[60] F. S. Pereira, K. Vittori and G. R. M. da Costa, “Distribution system reconfiguration for loss reduction based on ant colony behavior,” Transmission & Distribution Conference and Exposition: Latin America (TDC ''06. IEEE/PES), August 15-18 2006, pp. 1-5.
[61] I. Roytelman, V. Melnik, S. S. H. Lee and R. L. Lugtu, “Multi-objective feeder reconfiguration by distribution management system,” IEEE Transactions on Power System, Vol. 11, No. 2, May1996, pp. 661-667.
[62] R. S. Chen, C. C. Chiu and Y. S. Yeh, “A genetic algorithm for the reliability optimization of a distributed system,” Ninth International Workshop on Database and Expert Systems Applications Proceedings, 1998, pp. 484-489.
[63] I. J. Ramirez-Rosado and J. L. Bernal-Agustin, “Genetic algorithms applied to the design of large power distribution systems,” IEEE Transactions on Power Systems, Vol. 13, No. 2, May 1998, pp. 696-703.
[64] J. L. Deng, “Control problems of grey systems,” Systems Control Letters, Vol. 1, No. 5, 1982, pp. 288-294.
[65] J. L. Deng, “Introduction to gray system theory,” Journals on Grey Systems, Vol. 1, 1989, pp. 1-24.
[66] J. H. Wu, M. L. You and K. L. Wen, “A modified grey relational grade,” Journals on Grey Systems, Vol. 11, No. 3, 1999, pp. 283-288.
[67] J. H. Wu and C. B. Chen, “An alternative form for grey relational grade,” Journals on Grey Systems, Vol. 11, No. 1, 1999, pp. 7-11.
[68] K. L. Wen, T. C. Chang and J. H. Wu, “Data preprocessing on grey relational analysis,” Journals on Grey Systems, Vol. 11, No. 1, 1999, pp. 139-141.
[69] N. Srinivas and K. Deb, “Multiobjective function optimization using nondominated sorting genetic algorithms,” Evolutionary Computation., Vol. 2, No. 3, 1995, pp. 221-248.
[70] K. Deb, A. Pratap, S. Agarwal and T. Meyarivan, “A fast and elitist multiobjective genetic algorithm: NSGA-II,” IEEE Transactions on Evolutionary Computation, Vol. 6, No. 2, April 2002.
[71] K. Deb, Multi-Objective Optimization using Evolutionary Algorithms, New York: John Wiley & Sons, Inc., 2001.
[72] J. S. Arora, Introduction to Optimum Design, 2nd edn, San Diego, California, USA: Elsevier Academic Press, 2004.
[73] S. Y. Ho, L. S. Shu and J. H. Chen, “Intelligent evolutionary algorithms for large parameter optimization problems,” IEEE Transactions on Evolutionary Computation, Vol. 8, No. 6, December 2004, pp. 522-541.
[74] F. Y. Hsu and M. S. Tsai, “A non-dominated sorting evolutionary programming algorithm for multi-objectives power distribution system feeder reconfiguration problems,” European Transactions on Electrical Power, Vol. 2012, November 20 2011.
[75] G. Colombo and C. L. Mumford, “Comparing algorithms, representations and operators for the multi-objective knapsack problem,” The 2005 IEEE Congress on Evolutionary Computation, September 2-5 2005, pp. 1268-1275.
[76] D. A. Van Veldhuizen and G. B. Lamont, “Evolutionary computation and convergence to a Pareto front,” Late Breaking Papers at the Genetic Programming 1998 Conference, July 1998, pp. 22-25.
[77] T. Murata and H. Ishibuchi, “MOGA: multi-objective genetic algorithms,” Proceedings of IEEE International Conference on Evolutionary Computation, 1995, pp. 289.
[78] M. A. Abido, “Multiobjective evolutionary algorithms for electric power dispatch problem,” IEEE Transactions on Evolutionary Computation, Vol. 10, No. 3, June 2006, pp. 315-329.
[79] M. A. Abido, “Multiobjective optimal power flow using strength Pareto evolutionary algorithm,” The 39th International Universities Power Engineering Conference (UPEC 2004), September 6-8 2004, pp. 457-461.
[80] A. Gomes, C. Henggeler Antunes and A. Gomes Martins, “Improving the responsiveness of NSGA-II using an adaptive mutation operator: a case study,” International Journal of Advanced Intelligence Paradigms archive, Vol. 2, No. 1, November 2010, pp. 4-18.
[81] I. Marouani, T. Guesmi, H. Hadj Abdallah and A. Ouali, “Optimal Reactive Power Dispatch with SSSC Device Using NSGAII Approach,” International Journal of Computer Science and Network Security (IJCSNS), Vol. 10, No. 7, July 10 2010, pp. 58-68.
[82] A. Gomes, C. Henggeler Antunes and A. Gomes Martins, “Improving the responsiveness of NSGA-II in dynamic environments using an adaptive mutation operator--a case study,” Lecture Notes in Computer Science 2010. Vol. 5177, 2010, pp. 90-97.


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