Meta-heuristic Search Algorithms for Solving the Economic Load Dispatch Problem - Sheta, Alaa; Ali, Alaa; Baareh, Abdelkarim; Aljahdali, Sultan (2022 3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC), 2022)
Economic load dispatch (ELD) is a challenge optimization problem to minimize the total cost of the thermally generated power that satisfies a set of equality and inequality constraints. We need to maximize the power network load under several operational constraints to solve this problem. Meanwhile, we need to minimize the cost of power generation and minimize the loss in the network transmission. Traditional optimization methods were used to solve such problems as linear programming. Meta-heuristic search algorithms have shown encouraging performance in solving various real-life engineering problems. This paper attempts to provide a comprehensive comparison between nine meta-heuristic search algorithms, including Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Crow Search Algorithm (CSA), Differential Evolution (DE), Salp Swarm Algorithm (SSA), Harmony Search (HS), Sine Cosine Algorithm (SCA), Multi-Verse Optimizer (MVO), and Moth-Flame Optimization Algorithm (MFO) for solving the economic load dispatch problem. Our developed results demonstrated that meta-heuristics search algorithms (i.e., CSA and DE) offer the optimal power set for each power station. These computed power fulfill the supply needs and maintain both minimum power costs and power losses in power transmission.
Keywords: Costs; Economic Load Dispatch; Meta-heursitics Search algorithms; Optimization; Particle swarm optimization; Power distribution; Power transmission; Propagation losses; Search problems; Thermal loading; dept.csc

Enhanced cuckoo search algorithm for industrial winding process modeling - Braik, M.; Sheta, A.; Al-Hiary, H.; Aljahdali, S. (Journal of Intelligent Manufacturing, 2022)
Modeling of nonlinear industrial systems embraces two key stages: selection of a model structure with a compact parameter list, and selection of an algorithm to estimate the parameter list values. Thus, there is a need to develop a sufficiently adequate model to characterize the behavior of industrial systems to represent experimental data sets. The data collected for many industrial systems may be subject to the existence of high non-linearity and multiple constraints. Meanwhile, creating a thoroughgoing model for an industrial process is essential for model-based control systems. In this work, we explore the use of a proposed Enhanced version of the Cuckoo Search (ECS) algorithm to address a parameter estimation problem for both linear and nonlinear model structures of a real winding process. The performance of the developed models was compared with other mainstream meta-heuristics when they were targeted to model the same process. Moreover, these models were compared with other models developed based on some conventional modeling methods. Several evaluation tests were performed to judge the efficiency of the developed models based on ECS, which showed superior performance in both training and testing cases over that achieved by other modeling methods. © 2022, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.
Keywords: Cuckoo search algorithms; Developed model; Industrial systems; Industrial winding process; Learning algorithms; Linear modeling; Model method; Model structures; Non-linear modelling; Nonlinear systems; Optimization; Parameter estimation; Performance; Process-models; Winding; Winding process; dept.csc; peer-reviewed