Grey Wolf Optimizer for solving economic dispatch problems

This work proposes a new meta-heuristic called Grey Wolf Optimizer (GWO) which inspired by grey wolves (Canis lupus). The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling prey, and attacking prey, are implemented. The algorithm is then benchmarked on 20 generating units in economic dispatch, and the results are verified by a comparative study with Biogeography-based optimization (BBO), Lambda Iteration method (LI), Hopfield model based approach (HM), Cuckoo Search (CS), Firefly, Artificial Bee Colony (ABC), Neural Networks training by Artificial Bee Colony (ABCNN), Quadratic Programming (QP) and General Algebraic Modeling System (GAMS). The results show that the GWO algorithm is able to provide very competitive results compared to these well-known meta-heuristics.

[1]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[2]  S. Granville.  Optimal reactive dispatch through interior point methods , 1994 .

[3]  G. L. Viviani,et al.  Hierarchical Economic Dispatch for Piecewise Quadratic Cost Functions , 1984, IEEE Transactions on Power Apparatus and Systems.

[4]  Xin-She Yang,et al.  Engineering Optimization: An Introduction with Metaheuristic Applications , 2010 .

[5]  Amita Mahor,et al.  Particle Swarm Optimization Approach For Economic Load Dispatch : A Review , 2012 .

[6]  Derviş Karaboğa,et al.  NEURAL NETWORKS TRAINING BY ARTIFICIAL BEE COLONY ALGORITHM ON PATTERN CLASSIFICATION , 2009 .

[7]  Ching-Tzong Su,et al.  New approach with a Hopfield modeling framework to economic dispatch , 2000 .

[8]  L. L. Lai,et al.  A fuzzy dissolved gas analysis method for the diagnosis of multiple incipient faults in a transformer , 2000 .

[9]  P. K. Chattopadhyay,et al.  Biogeography-Based Optimization for Different Economic Load Dispatch Problems , 2010, IEEE Transactions on Power Systems.

[10]  Xin-She Yang,et al.  Engineering optimisation by cuckoo search , 2010, Int. J. Math. Model. Numer. Optimisation.

[11]  Leandro dos Santos Coelho,et al.  Solving economic load dispatch problems in power systems using chaotic and Gaussian particle swarm optimization approaches , 2008 .

[12]  Xin-She Yang,et al.  Engineering optimisation by cuckoo search , 2010 .

[13]  H. T. Yang,et al.  Incorporating a Multi-Criteria Decision Procedure into the Combined Dynamic Programming/Production Simulation Algorithm for Generation Expansion Planning , 1989, IEEE Power Engineering Review.

[14]  Chern-Lin Chen,et al.  Branch-and-bound scheduling for thermal generating units , 1993 .

[15]  Zwe-Lee Gaing,et al.  Particle swarm optimization to solving the economic dispatch considering the generator constraints , 2003 .

[16]  Bijay Ketan Panigrahi,et al.  Solution of Large Scale Economic Load Dispatch Problem using Quadratic Programming and GAMS: A Comparative Analysis , 2012 .

[17]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[18]  Z.-X. Liang,et al.  A zoom feature for a dynamic programming solution to economic dispatch including transmission losses , 1992 .

[19]  R. Coppinger,et al.  Wolf-pack (Canis lupus) hunting strategies emerge from simple rules in computational simulations , 2011, Behavioural Processes.

[20]  V. Quintana,et al.  An efficient predictor-corrector interior point algorithm for security-constrained economic dispatch , 1997 .

[21]  Amita Mahor,et al.  ECONOMIC LOAD DISPATCH USING PARTICLE SWARM OPTIMIZATION , 2012 .

[22]  Chin E. Lin,et al.  A direct Newton-Raphson economic dispatch , 1992 .