A novel modified random walk grey wolf optimisation approach for non-smooth and non-convex economic load dispatch. (3rd June 2022)

• Record Type: Journal Article
• Authors: Sahoo, Arun Kumar; Panigrahi, Tapas Kumar; Nayak, Gopal Krishna
• Is Part Of: International journal of innovative computing and applications. Volume 13:Number 2(2022)
• Page Start: 59
• Page End: 78
• Publication Date: 2022-06-03
• Subjects: economic load dispatch -- ELD -- constraints -- grey wolf optimisation -- GWO -- modified random walk grey wolf optimisation -- valve point loading; Evolutionary computation -- Periodicals; Neural networks (Computer science) -- Periodicals; Genetic programming (Computer science) -- Periodicals; Biologically-inspired computing -- Periodicals; Swarm intelligence -- Periodicals; Quantum computers -- Periodicals; 006.3
• Languages: English
• ISSNs: 1751-648X

Abstract

In practice, economic load dispatch (ELD) problems have non-smooth and non-convex cost functions which are subjected to numerous nonlinear equality and inequality constraints involving multiple decision variables. It makes the problem computationally labyrinthine to solve via any analytic method. This study proposes a novel and improved version of the conventional grey wolf optimisation (GWO) technique to solve the ELD problem. In the improved GWO, the leadership hierarchy of the grey wolf is ameliorated by taking the random walking behaviour of the grey wolfs into consideration. The algorithm aims to modify the existing leaders with the best leaders to overcome the drawbacks of the conventional GWO. The improved GWO guarantees better exploration and exploitation of the search space. The nonlinear constraints of generating units like ramp rate constraints, influence of valve-point loading and prohibited operating zones (POZs) are taken into account for both lines with and without losses. The obtained results are compared to that of other contemporary algorithms for demonstrating the superiority of the suggested one. This technique provides the optimum dispatch with a faster convergence rate in comparison to the conventional GWO and some other existing methods.