Reducing Variable Trend Search algorithm for optimizing non linear multidimensional space search

A non linear multidimensional space search is a complex optimization problem. There are different biologically inspired algorithms which are used to optimize such a problem. A new algorithm termed as Reducing Variable Trend Search (RVTS) is proposed in this paper. RVTS emulates a modified decision making process called as Delphi process. RVTS is implemented on an IEEE 6 bus system and its performance is compared against PSO and dPSO.

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

[2]  A.H. Mantawy,et al.  A new reactive power optimization algorithm , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

[3]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[4]  Murray Turoff,et al.  The Delphi Method: Techniques and Applications , 1976 .

[5]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[6]  Donald E. Grierson,et al.  Comparison among five evolutionary-based optimization algorithms , 2005, Adv. Eng. Informatics.

[7]  Bhimrao S. Umre,et al.  Reactive power control using dynamic Particle Swarm Optimization for real power loss minimization , 2012 .

[8]  R. Eberhart,et al.  Comparing inertia weights and constriction factors in particle swarm optimization , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[9]  R. Storn,et al.  Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces , 2004 .

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