An Improved Electromagnetic Field Optimization for the Global Optimization Problems

Abstract

Electromagnetic field optimization (EFO) is a relatively new physics-inspired population-based metaheuristic algorithm, which simulates the behavior of electromagnets with different polarities and takes advantage of a nature-inspired ratio, known as the golden ratio. In EFO, the population consists of electromagnetic particles made of electromagnets corresponding to variables of an optimization problem and is divided into three fields: positive, negative, and neutral. In each iteration, a new electromagnetic particle is generated based on the attraction-repulsion forces among these electromagnetic fields, where the repulsion force helps particle to avoid the local optimal point, and the attraction force leads to find global optimal. This paper introduces an improved version of the EFO called improved electromagnetic field optimization (iEFO). Distinct from the EFO, the iEFO has two novel modifications: new solution generation function for the electromagnets and adaptive control of algorithmic parameters. In addition to these major improvements, the boundary control and randomization procedures for the newly generated electromagnets are modified. In the computational studies, the performance of the proposed iEFO is tested against original EFO, existing physics-inspired algorithms, and state-of-the-art meta-heuristic algorithms as artificial bee colony algorithm, particle swarm optimization, and differential evolution. Obtained results are verified with statistical testing, and results reveal that proposed iEFO outperforms the EFO and other considered competitor algorithms by providing better results.

Optimization Techniques for Directional Overcurrent Relay Coordination: A Comprehensive Review

“…EFO is one of the relatively new physics-inspired metaheuristic algorithms, which is first proposed by Abedinpourshotorban et al It simulates the behavior of electromagnets with different polarities and takes advantage of a nature-inspired ratio, known as the golden ratio [229]. A notable characteristic of EFO is the collaboration of multiple particles to create a novel electromagnetic particle, and it was demonstrated that EFO exhibits superior performance when compared to other optimization algorithms [230].…”

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

“…A considerable number of metaheuristic algorithms in the literature have taken inspiration from physical phenomena for handling optimization problems. For solving global optimization problems using Physics‐based algorithms, a considerable number of works have been proposed such as electromagnetic field optimization (EFO), SA, multiverse optimization (MVO), and flow regime algorithm (FRA) . Moreover, MVO has been used widely in many real‐world optimization applications such as geological engineering, which it was used to slope stability assessment, economic dispatch problem, which is used for solving a linear programming model for proper management of electricity production sources, handwritten documents, which is applied as an automatic clustering algorithm, image segmentation, which is adopted in color image segmentation problem, optimal power flow problem, which is implemented for solving fuel cost reduction, voltage deviation minimization, and voltage stability improvement …”

Quantum Based Particle Swarm Optimization for Equivalent Circuit Design of Terminal Antenna Impedance

“…This is typically one of the most difficult problems to solve since it involves a large number of parameters, complex constraints, and objective functions with more than one optimum (Mescia et al., 2017). Moreover, in many cases, the optimization problem is non–linear and more challenging issues occur, especially when many local optimal solutions exist (Fornarelli et al., 2009; Yurtkuran, 2019).…”

“…This is typically one of the most difficult problems to solve since it involves a large number of parameters, complex constraints, and objective functions with more than one optimum (Mescia et al, 2017). Moreover, in many cases, the optimization problem is non-linear and more challenging issues occur, especially when many local optimal solutions exist (Fornarelli et al, 2009;Yurtkuran, 2019).Since the objective function is generally a multimodal one and considering that it is very difficult for deterministic algorithms to find the global optimal solution, many metaheuristic algorithms have become increasingly popular because of their potential in solving large-scale problems efficiently in a way that is impossible by using deterministic approaches. Compared to other nature-inspired optimization algorithms the swarm-inspired ones are gaining popularity within the electromagnetic research community and among electromagnetic engineers as design tool and problem solvers.…”