Signal lock optimization algorithm for engineering benchmark problems

2026-02-04T10:42:50+00:00

The increasing complexity of constrained engineering design problems has intensified the demand for metaheuristic optimization algorithms that are both computationally efficient and robust against premature convergence and search stagnation. This paper presents the Signal Lock Optimization Algorithm (SLOA), a novel population-based metaheuristic founded on the dual mechanisms of confidence reinforcement and noise suppression. The core principle of SLOA lies in identifying high-confidence solution components-referred to as signal locks-and reinforcing them during the search process while dynamically filtering stochastic perturbations that may mislead exploration in multimodal and highly constrained landscapes. The proposed algorithm incorporates adaptive parameter updating and an effective constraint-handling strategy to maintain a balanced exploration–exploitation trade-off. SLOA is extensively evaluated on a suite of widely adopted engineering benchmark problems, including Welded Beam Design, Pressure Vessel Design, Tension/Compression Spring Design, and Car Side-Impact Design. Comparative experimental results and statistical analyses demonstrate that SLOA consistently achieves superior or highly competitive solution quality, faster convergence rates, and high feasibility compared to several state-of-the-art metaheuristic algorithms. The findings confirm that the signal lock mechanism provides a reliable and scalable optimization framework for solving complex real-world engineering design problems.

Mathematical modeling and simulation of data-driven systems for efficient network performance optimization

2026-04-02T09:57:14+00:00

The high rate of heterogeneous networks growth and exponential increase in internet traffic has posed unprecedented challenges in ensuring optimum network performance. The problem of using traditional analytical models which are mostly on the basis of static assumptions is becoming insufficient to explain the dynamic, nonlinear behavior that is seen in modern data-driven communication systems. In this paper, it is described that stochastic queuing theory, Markov chain analysis, and machine learning-based digital twin (DT) technology are combined in a comprehensive mathematical modeling and simulation framework to optimize network performance metrics, such as latency, throughput, package delivery rate (PDR), and packet loss rate. The proposed hybrid model uses Deep Neural Network (DNN) architecture that is trained on high volume synthetic and real world traffic data that predicts congestion patterns and dynamically reconfigures routing decisions in real time. A digital twin layer is a version of the physical network topology that is simulated to allow experimental and what-if analysis to be done safely and offline (without affecting live traffic). Large-scale simulations with NS-3 and Python-based machine learning pipelines illustrate that the proposed model is able to achieve throughput efficiency 96.8, average end-to-end latency 3.2 ms, and a packet delivery ratio 97.5 in the case of heavy load. The statistical significance of improvements over baseline models, such as traditional M/M/1 queuing, Markov chain models, linear regression, and standalone DNN methods, are statistically significant in all key performance indicators. These findings confirm the feasibility of mathematical modeling based on data as a useful method of proactive, self-optimizing network management in practice. This paper will provide new knowledge about the combination of formal mathematical modeling with modern techniques of machine learning, providing a scalable and understandable solution to the optimization of network performance in the next generation.

Journal of Electronics, Computer Networking and Applied Mathematics

Signal lock optimization algorithm for engineering benchmark problems

Mathematical modeling and simulation of data-driven systems for efficient network performance optimization