The artificial electric field algorithm (AEFA) is a population-based metaheuristic optimization
algorithm. It is inspired by the electrostatic field theory and fundamental laws of physics. The …

Due to its low dependency on the control parameters and straightforward operations, the
Artificial Electric Field Algorithm (AEFA) has drawn much interest; yet, it still has slow …

Competitive and collaborative strategies and topologies are among the most essential
concepts and greatly influence the optimization ability of population-based optimization …

In this article, a series of learning strategies are proposed to enhance the optimization ability
of the artificial electric field algorithm. Orthogonal learning is an important mathematical tool …

The optimal parameter values in a feed-forward neural network model play an important role
in determining the efficiency and significance of the trained model. In this paper, we propose …

The multiple traveling salesman problem (MTSP) is an extension of the traveling salesman
problem (TSP). It is found that the MTSP problem on a three-dimensional sphere has more …

In the real world, many optimization problems are discrete and very complex to solve. Some
of them are in the class of NP-hard problems and their search spaces grow exponentially …

In this study, an improved version of artificial electric field (AEF) algorithm, named as
opposition-based AEF (ObAEF), has been proposed for the first time ever to tune a fractional …

The comprehensive learning strategy is a meticulous method for enhancing the optimization
ability of population‐based optimization algorithms. This article proposes an adaptive …

Optimization problems in real-world scenarios require algorithms that effectively balance
exploration and exploitation to avoid local optima and achieve global solutions. To address …