A Cauchy-based evolution strategy for solving the reactive power dispatch problem

Abstract In this work is presented a new proposal for solving the reactive power dispatch. The approach is based on the (μ+λ)-ES paradigm improved by the control of mutations and by using of Cauchy-based mutation rather than the classical Gaussian Mutations (GMs). Others variants are also implemented and a comparative study are performed. Good and reliable performance have been achieved and validation tests using the standard IEEE118 system are reported.

[1]  G. S. Fishman,et al.  Improving Monte Carlo Efficiency by Increasing Variance , 1992 .

[2]  D. B. Fogel Evolutionary optimization , 1992, [1992] Conference Record of the Twenty-Sixth Asilomar Conference on Signals, Systems & Computers.

[3]  Günter Rudolph,et al.  Contemporary Evolution Strategies , 1995, ECAL.

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

[5]  Loi Lei Lai,et al.  Application of evolutionary programming to reactive power planning-comparison with nonlinear programming approach , 1997 .

[6]  A. Hoffman Arguments on evolution : a paleontologist's perspective , 1989 .

[7]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.

[8]  Günter Rudolph,et al.  Global Optimization by Means of Distributed Evolution Strategies , 1990, PPSN.

[9]  S. S. Venkata,et al.  Improved distribution system planning using computational evolution , 1995 .

[10]  David B. Fogel,et al.  Meta-evolutionary programming , 1991, [1991] Conference Record of the Twenty-Fifth Asilomar Conference on Signals, Systems & Computers.

[11]  Loi Lei Lai,et al.  Intelligent System Applications in Power Engineering: Evolutionary Programming and Neural Networks , 1998 .

[12]  Xin Yao,et al.  Fast Evolution Strategies , 1997, Evolutionary Programming.

[13]  Kwang Y. Lee,et al.  Optimal reactive power planning using evolutionary algorithms: a comparative study for evolutionary programming, evolutionary strategy, genetic algorithm, and linear programming , 1998 .

[14]  Osvaldo R. Saavedra,et al.  Optimal reactive power dispatch using evolutionary computation: extended algorithms , 1999 .

[15]  David B. Fogel,et al.  An introduction to simulated evolutionary optimization , 1994, IEEE Trans. Neural Networks.

[16]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[17]  Xin Yao,et al.  Evolutionary programming made faster , 1999, IEEE Trans. Evol. Comput..

[18]  Q. H. Wu,et al.  Power system optimal reactive power dispatch using evolutionary programming , 1995 .