Related papers: Constrained Multi-Objective Genetic Algorithm Vari…

We present a genetic algorithm (GA)-based inverse design framework for synthesizing high-performance planar terahertz (THz) filters integrated with coplanar striplines (CPSs). The method efficiently explores high-dimensional design spaces…

In this paper, we propose a tri-domain reconfigurable multiuser multiple-input multiple-output (MIMO) communication system that integrates the electromagnetic (EM) reconfigurable antenna (EMRA) with the spatially movable antenna (SMA),…

This work presents a novel lattice-based methodology for incorporating multidimensional constraints into continuous decision variables within a genetic algorithm (GA) framework. The proposed approach consolidates established transcription…

In this paper, we introduce a novel method for merging the weights of multiple pre-trained neural networks using a genetic algorithm called MeGA. Traditional techniques, such as weight averaging and ensemble methods, often fail to fully…

Multi-objective optimization of analog circuits is hindered by high-dimensional parameter spaces, strong feedback couplings, and expensive transistor-level simulations. Evolutionary algorithms such as Non-dominated Sorting Genetic Algorithm…

To facilitate the antenna design with the aid of computer, one of the practices in consumer electronic industry is to model and optimize antenna performances with a simplified antenna geometric scheme. Traditional antenna modeling requires…

The evolution of gene regulatory networks in variable environments poses Multi-objective Optimization Problem (MOP), where the expression levels of genes must be tuned to meet the demands of each environment. When formalized in the context…

Adaptive filter in complex scenarios demands algorithms that integrate fast convergence, low complexity, and robust performance under diverse noise conditions. To address this challenge, we propose a online censoring robust total…

The MaxCut problem is a fundamental problem in Combinatorial Optimization, with significant implications across diverse domains such as logistics, network design, and statistical physics. The algorithm represents innovative approaches that…

Proton therapy is a modality in fast development. Characterized by a maximum dose deposition at the end of the proton trajectory followed by a sharp fall-off, proton beams can deliver a highly conformal dose to the tumor while sparing…

Stochastic Gradient Algorithms (SGAs) are ubiquitous in computational statistics, machine learning and optimisation. Recent years have brought an influx of interest in SGAs, and the non-asymptotic analysis of their bias is by now…

In this work, we present block diagonalization and power allocation algorithms for large-scale multiple-antenna systems with coarsely quantized signals. In particular, we develop Coarse Quantization-Aware Block Diagonalization…

In practical optimisation the dominant characteristics of the problem are often not known prior. Therefore, there is a need to develop general solvers as it is not always possible to tailor a specialised approach to each application. The…

Reconfigurable antenna multiple-input multiple-output (MIMO) is a foundational technology for the continuing evolution of cellular systems, including upcoming 6G communication systems. In this paper, we address the problem of…

Aiming at the shortcomings of the gazelle optimization algorithm, such as the imbalance between exploration and exploitation and the insufficient information exchange within the population, this paper proposes a multi-strategy improved…

The multi-gradient descent algorithm (MGDA) finds a common descent direction that can improve all objectives by identifying the minimum-norm point in the convex hull of the objective gradients. This method has become a foundational tool in…

In this paper GA based light weight faster version of Digital Signature Algorithm (GADSA) in wireless communication has been proposed. Various genetic operators like crossover and mutation are used to optimizing amount of modular…

Training Artificial Neural Networks (ANNs) with Stochastic Gradient Descent (SGD) frequently encounters difficulties, including substantial computing expense and the risk of converging to local optima, attributable to its dependence on…

NSGA-III is one of the most widely adopted algorithms for tackling many-objective optimization problems. However, its CPU-based design severely limits scalability and computational efficiency. To address the limitations, we propose…

This paper studies a multiuser mobile edge computing (MEC) system, in which one base station (BS) serves multiple users with intensive computation tasks. We exploit the multi-antenna non-orthogonal multiple access (NOMA) technique for…