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Optimization is a topic that has always been discussed in all different fields of science. One of the most effective techniques for solving such problems is optimization algorithms. In this paper, a new optimizer called Multi-Leader optimizer (MLO) is developed in which multiple leaders guide members of the population towards the optimal answer. MLO is mathematically modelled based on the process of advancing members of the population and following the leaders. MLO performance in optimization is examined on twenty-three standard objective functions. The results of this optimization are compared with the results of the other eight existing optimization algorithms including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Teaching-Learning-Based Optimization (TLBO), Gray Wolf Optimizer (GWO), Grasshopper Optimization Algorithm (GOA), Emperor Penguin Optimizer (EPO), Shell Game Optimization (SGO), and Hide Objects Game Optimization (HOGO). Based on the analysis of the simulation results on unimodal test functions to evaluate exploitation ability and multimodal test functions in order to evaluate exploration ability, it has been determined that MLO has a higher ability to solve optimization problems than existing optimization algorithms.

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A novel optimization methodology, Momentum Search Algorithm (MSA) is presented based on Newton's laws: the law of conservation of momentum. It includes a set of masses in a closed system considering the conservation of momentum and kinetic energy of bodies. The possible solutions are presented by system bodies' positions in an n-dimensional space. The mass of bodies is proportional to their fitness function. Larger masses represent the better solutions. At each iteration, an external body collides separately with all solution bodies and moves them toward the optimum solution. The direction of the collision depends on the position of solution bodies and the position of the body with the best fitness function. As the better solutions have heavier bodies, the external body has less effect on their positions. On the other hand, the worse solutions are lighter and moved easily by the external body toward the better positions. The best position is achieved by allowing the external body to move the solution bodies toward better positions. The numerical results obtained from several standard benchmark test functions indicate the superiority of the proposed method over many other optimization techniques such as Genetic Algorithm,

Summary Electric arc furnace (EAF) is a nonlinear and time‐varying load that causes power quality problems such as voltage flicker and harmonics. Field measurement of voltage and current from EAFs in Mobarakeh Steel Company in Isfahan‐Iran are used to introduce a comprehensive model for EAF. A new dynamic, nonlinear and time‐varying model is presented here. In other words, the function between EAF voltage and current is nonlinear, differential and with time‐varying parameters. By using the recorded data, the order of the dynamic part is determined by a new approach. The nonlinear characteristic is modeled with proper polynomial functions. All parameters related to the dynamic and nonlinear functions are considered time varying as time series. Autoregressive moving average (ARMA) models are implemented and the suitable ARMA order is attained for all EAF time‐varying parameters. Copyright © 2014 John Wiley & Sons, Ltd.