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Top1. Introduction
Due to increased power demand by commercial and residential users, the cost of power generation becomes a huge concern in power system operation and control. If there is a slight decrease in the cost of power generation, it will create a major effect on the economics of the power system. Researchers and engineers have introduced Economic Load Dispatch (ELD) term to run power generating units at minimum cost with satisfying power demand. The economics of power systems encourage researchers to invent techniques that reduce the cost of power generation significantly. The traditional numerical techniques like lambda iteration method (Zhan et al., 2014), gradient method (Ray, 2014), linear programming method and quadratic programming method (dos Santos Coelho & Mariani, 2006) used to solve ELD problem. These methods are based on linear cost approximation. The practical ELD problem becomes highly nonlinear after considering various operating constraints like generating operating constraints and valve point loading effect (VPLE). Many metaheuristic techniques like Genetic Algorithm (GA) (Bakirtzis, 1994), Differential Evaluation (DE) (Roy et al., 2014), Particle Swarm Optimization (PSO) (Gaing, 2003), Evolutionary Programming (EP) (Dao et al., 2015), Hybrid Evolutionary Programming (HEP) (Sinha et al., 2003), Civilized Swarm Optimization (CSO) (Narang et al., 2017), Modified PSO (MPSO) (Kamboj et al., 2016), Adaptive Real Coded GA (ARCGA) (Ni et al., 2017), Bacteria Foraging Optimization (BFO) (Ali & Abd-Elazim, 2ss011), Search Group Optimization (SGO) (Bhattacharjee & Patel, 2019), Seeker Optimization Algorithm (SOA) (Shaw et al., 2012) used due to capable of finding high dimensional ELD problem. Sometimes, these methods converge to local optima and do not guarantee the global best solution.