## 1 Introduction  
Economic load dispatch (ELD) is considered the most elementary and computationally heavy optimization problem in electricity industry studies. ELD enables the power system to analyze the committed generating units to allocate the optimum level of active power output to achieve the total power demands at the cheapest operating cost while satisfying several physical and operational constraints [1]. Over the last few years, various mathematical programming techniques and optimization methods have been applied to solve ELD problems, such as the Newton method [2], the gradient method [3], the base point and participation factors method, and the Lambda-iteration method [2]. Nevertheless, none of the mentioned techniques perform well in addressing practical problems with nonlinear and discontinuous characteristics since these methods required the incremental cost of the generators to be increase in a monotonic or piecewise linear fashion [2]. Therefore, nonlinear programming [4], dynamic programming [5], and some of their modified applications have been employed to deal with ELD issues. Unfortunately, the drawback of these methods is their computational time, which when applied to modern power systems with a huge number of generating units is too long. To overcome the downside of traditional mathematical programming, researchers around the world have introduced metaheuristic optimization algorithms, such as simulated annealing (SA) [6], genetic algorithms (GAs) [7], tabu searches (TS) [8], and artificial neural networks (ANNs) [9]. [PERSON] [10] successfully addressed complex optimization issues. However, these probabilistic heuristic algorithms do not always guarantee the global search property. Recently, different kinds of global optimization algorithmshave been proposed, such as particle swarm optimization (PSO) [11], modified particle swarm optimization [12], biogeography-based optimization (BBO) [13], modified genetic algorithm [14], bacteria foraging optimization [15], differential evolution (DE) [16], and ant colony optimization [17]. The application of these techniques in ELD has delivered some promising solutions in terms of minimizing total generation cost and improving convergence rate. However, recent research has recognized a few deficiencies in stochastic algorithms like GA. This degradation in efficiency and limited search capability may be noticeable when applied to multimodal objective functions. Additionally, TS requires a suitable selection of control parameters to attain an optimal solution. Slow convergence can be considered a key disadvantage of GA, SA, TS, and ANN, making them unable to appropriately address real-time issues. Even though PSO has attracted research interest due to its rapid convergence characteristic and its flexibility, PSO is still restricted when applied to large-scale real-time ELD, since it is not always guaranteed that the total generation cost is the global best solution. The modification of the presented algorithm can be a solution for achieving the global optimal solution and upgrading the convergence rate. Existing modified metaheuristic methods include chaotic differential evolution and sequential quadratic programming (DEC_SQP) [18], improved coordinated aggregation-based PSO (ICA_PSO) [19], quantum-inspired particle swarm optimization (QPSO) [20], modified shuffled frog leaping algorithm with GA crossover (MSFLA and GA) [21], a different version of PSO [19], shuffled differential evolution (SDE) [21], hybrid biogeography-based optimization with differential evolution (DE/BBO) [22], and adaptive hybrid backtracking search optimization [23]. Oppositional invasive weed optimization (OIWO) [24] improves the convergence rate of invasive weed optimization by incorporating opposite-based learning (OBL) [25]. Nevertheless, this OIWO relies heavily on the initial selection of control parameters to obtain the global best solution. Moreover, the solution is not unique for every trial, and this method also has the problem of long computational time.