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| 1 | 含电动汽车的电力系统动态环境经济调度显示文摘为应对电动汽车规模化应用给电力调度带来的挑战,构建含电动汽车的动态环境经济调度模型。该模型将各调度时段电动汽车的'车-网'互动(V2G)功率以及常规机组的出力作为决策变量,以总燃料费用和污染排放量作为优化目标,在满足系统能量及用户出行需求的前提下,动态管理电动汽车的充放电行为。设计一种采用改进MOEA/D的优化调度求解方法,并提出基于罚函数的决策变量两步制动态约束处理策略。测试系统的仿真结果验证了所提调度模型及方法的合理性及有效性。 | 朱永胜 王杰 瞿博阳 李健 SUGANTHAN P N | 2016 | 电力自动化设备2016，36，10： | 18 |
| 2 | 双局部粒子群算法解决环境经济调度问题显示文摘提出一种基于双局部最优的多目标粒子群优化算法,与可行解为优的约束处理方法相结合,来求解决非线性带约束的多目标电力系统环境经济调度问题。该算法针对传统多目标粒子群算法多样性低的局限性,通过对搜索空间的分割归类来增加帕累托最优解的多样性;并采用一种新的双局部最优来引导粒子的搜索,从而增强了算法的全局搜索能力。算法加入了可行解为优的约束处理方法对IEEE30节点六发电机电力系统环境经济负荷分配模型分别在几个不同复杂性问题的情况进行仿真测试,并与文献中的其他算法进行了比较。结果表明,改进的算法能够在保持帕累托最优解多样性的同时具有良好的收敛性能,更有效地解决电力系统环境经济调度问题。 | 瞿博阳 梁静 Ponnuthurai Nagaratnam Suganthan | 2014 | 计算机工程与应用2014，50，11： | 4 |
| 3 | Dynamic economic emission dispatch based on multi-objective pigeon-inspired optimization with double disturbance显示文摘Dear editor,Recently, dynamic economic dispatch (DED) has attracted much attention in power system optimal operation [1, 2], because it considers the interplay between the different dispatching periods based on ramp rate limits of the generator units. | Li YAN Boyang QU Yongsheng ZHU Baihao QIAO Ponnuthurai Nagaratnam SUGANTHAN | 2019 | Science China(Information Sciences)2019，62，7： | 3 |
| 4 | Multi-objective differential evolution with diversity enhancement显示文摘Multi-objective differential evolution (MODE) is a powerful and efficient population-based stochastic search technique for solving multi-objective optimization problems in many scientific and engineering fields. However, premature convergence is the major drawback of MODE, especially when there are numerous local Pareto optimal solutions. To overcome this problem, we propose a MODE with a diversity enhancement (MODE-DE) mechanism to prevent the algorithm becoming trapped in a locally optimal Pareto front. The proposed algorithm combines the current population with a number of randomly generated parameter vectors to increase the diversity of the differential vectors and thereby the diversity of the newly generated offspring. The performance of the MODE-DE algorithm was evaluated on a set of 19 benchmark problem codes available from http://gffzzdcd0dfe20e794032hoc0f6kpqwc6f6bbu.ffgz.tsg.suse.edu.cn/home/epnsugan/. With the proposed method, the performances were either better than or equal to those of the MODE without the diversity enhancement. | Ponnuthurai-Nagaratnam SUGANTHAN | 2010 | Journal of Zhejiang University-Science C(Computers and Electronics)2010，11，7： | 2 |
| 5 | Niching particle swarm optimization with local search for multi-modal optimization显示文摘 | B.Y. Qu J.J. Liang P.N. Suganthan | 2012 | Information Sciences2012，，： | 2 |
| 6 | A discrete artificial bee colony algorithm for the lot- streaming flow shop scheduling problem显示文摘 | PAN Q FATIH TASGETIREN M SUGANTHAN P N etal | 2011 | Information Sciences2011，181，12： | 1 |
| 7 | Evolutionary extreme learning machine显示文摘 | Q Zhu A k Qin P Suganthan | 2005 | Pattern Recognition2005，38，： | 1 |
| 8 | Differential evolution:A survey of the state-of-the-art显示文摘 | Das S Suganthan P N | 2011 | IEEE Transactions on Evo- lutionary Computation2011，15，1： | 1 |
| 9 | A Dif- ferential Evolution Algorithm with Self-Adapting Strategy and Control Parameters 显示文摘 | PAN Q K SUGANTHAN P N WANG L | 2011 | Computers and Operations Research2011，38，1： | 1 |
| 10 | Robust adaptive beamforming based on covariance matrix reconstruction for look direction mismatch显示文摘 | Mallipeddi R Lie J P Suganthan P N | 2011 | Progress in Electromagnetics Research Letters2011，25，： | 1 |
| 11 | Ensemble of constraint handling techniques显示文摘 | Mallipeddi R Suganthan P N | 2010 | IEEE Trans on Evolutionary Computation2010，14，4： | 1 |
| 12 | A differential evolution algorithm with self-adapting strategy and control parameters显示文摘 | Pan Q Suganthan P Wang L | 2011 | Computers & Operations Research2011，38，1： | 1 |
| 13 | Dynamic multi-swarm particle swarm optimizer with harmony search显示文摘 | Zhao S Z Suganthan P N Pan Q K | 2011 | Expert Systems with Applications2011，38，4： | 1 |
| 14 | Ensemble of constraint handling techniques显示文摘 | Mallipeddi R Ponnuthurai P Suganthan N | 2010 | IEEE Transactions on Evolutionary Computation2010，14，4： | 1 |
| 15 | Differential evolution algorithm with strategy adaptation for global numerical optimization显示文摘 | Qin A K Huang V L Suganthan P N | 2009 | IEEE Transactions on Evolutionary Computation2009，13，2： | 1 |
| 16 | Evolutionary extreme learning machine显示文摘 | ZHU Qin-yu QIN A K Suganthan P N | 2005 | Pattern Recognition2005，38，2： | 1 |
| 17 | Comprehensivelearning particle swarm optimizer for global optimization of multimodalfunctions显示文摘 | LIANG J J QIN A K SUGANTHAN P N ei al | 2006 | IEEE Trans on Evolutionary Computation2006，10，3： | 1 |
| 18 | A Differential Evolution Algorithm with Self-adapting Strategy and Control Parameters显示文摘 | Pan Quanke Suganthan P N Wang Ling | 2011 | Computers&Operations Research2011，38，1： | 1 |
| 19 | Ensemble of niching algorithms显示文摘 | Yu E Suganthan P N | 2010 | Information Sciences2010，180，15： | 1 |
| 20 | A discrete artificial bee colony algorithm for the lot- streaming flow shop scheduling problem显示文摘 | Pan Q K Tasgetiren M F Suganthan P N | 2011 | Inform Sci2011，181，12： | 1 |
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