A Modified Differential Evolution Algorithm With Fitness Sharing for Power System Planning

The application of evolutionary computation methods in search and optimization has been growing over the past few decades. As a promising approach in metaheuristic optimization algorithms, differential evolution (DE) has been attracting increasing attention for wide engineering applications including power engineering. Different from conventional evolutionary algorithms using predefined probability distribution function for mutation process, differential evolution exploits the differences of randomly sampled pairs of objective vectors for its mutation process. Consequently the variation between vectors will outfit the objective functions topographical information toward the optimization process, and therefore provides efficient global optimization capability. However, although DE is shown to be precise, fast as well as robust, its search efficiency will be impaired during solution process with fast descending diversity of population. In this paper, detailed numerical studies are carried out to propose the characterization of the performance of several DE mutation methods with and without fitness sharing scheme. All the approaches using the proposed modified DE are presented on an example in power system planning.

[1]  W. Ashby,et al.  Principles of the self-organizing dynamic system. , 1947, The Journal of general psychology.

[2]  J. S. F. Barker,et al.  Simulation of Genetic Systems by Automatic Digital Computers , 1958 .

[3]  Lawrence J. Fogel,et al.  Artificial Intelligence through Simulated Evolution , 1966 .

[4]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[5]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[6]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[7]  D. Hill,et al.  Voltage stability indices for stressed power systems , 1993 .

[8]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[9]  Rainer Storn,et al.  Minimizing the real functions of the ICEC'96 contest by differential evolution , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[10]  Thomas Bäck,et al.  Evolutionary algorithms in theory and practice - evolution strategies, evolutionary programming, genetic algorithms , 1996 .

[11]  P. R. Bijwe,et al.  Look-ahead approach to power system loadability enhancement , 1997 .

[12]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[13]  R. Storn,et al.  Differential evolution a simple and efficient adaptive scheme for global optimization over continu , 1997 .

[14]  Bruno Sareni,et al.  Fitness sharing and niching methods revisited , 1998, IEEE Trans. Evol. Comput..

[15]  Luís Ferreira,et al.  Optimal distribution network expansion planning under uncertainty by evolutionary decision convergence , 1998 .

[16]  H. B. Quek,et al.  Pareto-optimal set based multiobjective tuning of fuzzy automatic train operation for mass transit system , 1999 .

[17]  Jong-Bae Park,et al.  Generation expansion planning based on an advanced evolutionary programming , 1999 .

[18]  D. Dasgupta Artificial Immune Systems and Their Applications , 1998, Springer Berlin Heidelberg.

[19]  Yun Li,et al.  Evolving trajectory controller networks from linear approximation model networks , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[20]  Ali M. S. Zalzala,et al.  Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons , 2000, IEEE Trans. Evol. Comput..

[21]  Libao Shi,et al.  Self-adaptive evolutionary programming and its application to multi-objective optimal operation of power systems , 2001 .

[22]  Vladimiro Miranda,et al.  EPSO-evolutionary particle swarm optimization, a new algorithm with applications in power systems , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[23]  Thai Doan Hoang Cau,et al.  A co-evolutionary approach to modelling the behaviour of participants in competitive electricity markets , 2002, IEEE Power Engineering Society Summer Meeting,.

[24]  Arthur C. Sanderson,et al.  Pareto-based multi-objective differential evolution , 2003, The 2003 Congress on Evolutionary Computation, 2003. CEC '03..

[25]  Kit Po Wong,et al.  Virtual Population and Acceleration Techniques for Evolutionary Power Flow Calculation in Power Systems , 2003 .

[26]  Dimitris K. Tasoulis,et al.  Parallel differential evolution , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[27]  Bernhard Sendhoff,et al.  Neural network regularization and ensembling using multi-objective evolutionary algorithms , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[28]  Ji-Pyng Chiou,et al.  Ant direction hybrid differential evolution for solving large capacitor placement problems , 2004 .

[29]  M. El-Bardini Hybrid evolutionary algorithm for identification and control of time varying system , 2004, International Conference on Electrical, Electronic and Computer Engineering, 2004. ICEEC '04..

[30]  Joong-Rin Shin,et al.  A particle swarm optimization for economic dispatch with nonsmooth cost functions , 2005, IEEE Transactions on Power Systems.

[31]  C. Su,et al.  Variable scaling hybrid differential evolution for solving network reconfiguration of distribution systems , 2005 .

[32]  Akbar A. Javadi,et al.  A hybrid intelligent genetic algorithm , 2005, Adv. Eng. Informatics.

[33]  Xiaodong Li,et al.  Efficient differential evolution using speciation for multimodal function optimization , 2005, GECCO '05.

[34]  Herbert Werner,et al.  A Hybrid Evolutionary-Algebraic Approach to Optimal and Robust Controller Design (Hybride evolutionär-algebraische Verfahren für den Entwurf optimaler und robuster Regler) , 2005, Autom..

[35]  S. Kannan,et al.  Application and comparison of metaheuristic techniques to generation expansion planning problem , 2005, IEEE Transactions on Power Systems.

[36]  J. Rowe,et al.  Particle SwarmOptimization andFitness Sharing tosolve Multi-Objective Optimization Problems , 2005 .

[37]  Kit Po Wong,et al.  Differential Evolution, an Alternative Approach to Evolutionary Algorithm , 2005 .

[38]  M. M. Ali,et al.  A numerical study of some modified differential evolution algorithms , 2006, Eur. J. Oper. Res..

[39]  Jason Teo,et al.  Exploring dynamic self-adaptive populations in differential evolution , 2006, Soft Comput..

[40]  Kit Po Wong,et al.  A hybrid planning method for transmission networks in a deregulated environment , 2006 .

[41]  A.J. Conejo,et al.  Optimal Network Placement of SVC Devices , 2007, IEEE Transactions on Power Systems.

[42]  Zhao Yang Dong,et al.  TCSC Allocation Based on Line Flow Based Equations Via Mixed-Integer Programming , 2007, IEEE Transactions on Power Systems.