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4 articles for “hybrid evolutionary algorithm”
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Hybrid Evolutionary Algorithm for Automated Test Case Generation in Large-Scale Software Systems
Abstract: Automated test case generation is a crucial activity for ensuring the reliability, robustness, and quality of large-scale software systems. Conventional test generation techniques often face significant challenges in handling scalability, achieving high code coverage, and adapting to complex and heterogeneous software architectures. Evolutionary algorithms have emerged as effective solutions, capable of exploring large search spaces and generating test suites with improved coverage and fault detection capabilities. However, individual evolutionary strategies, …
Published in International Journal of Software Computing and Testing · Vol. 12, Issue 1, 2026 · pp. 15–21 Read article
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A Comprehensive Study on Evolvable Hardware
Abstract: AbstractEvolvable Hardware (EHW) is an innovative approach to hardware design that utilizes evolutionary algorithms, such as Genetic Algorithms (GAs) and Genetic Programming (GP), to enable electronic systems to autonomously adapt, optimize, and reconfigure themselves in response to changing environments or requirements. Inspired by natural evolution, EHW dynamically evolves hardware configurations through selection, mutation, and crossover, allowing systems to improve performance, enhance fault tolerance, and achieve greater energy efficiency. Central to …
Published in International Journal of Analog Integrated Circuits · Vol. 11, Issue 1, 2025 · pp. 16–22 Read article
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Deep Reinforcement Learning-Based MPPT for PV Systems Under Partial Shading: A Hybrid Metaheuristic-Optimized Control Framework.
Abstract: Partial shade and quickly changing environmental conditions make Maximum Power Point Tracking (MPPT) in photovoltaic (PV) arrays particularly difficult. In these situations, traditional algorithms frequently fail to find the global optimum or experience sluggish convergence and steady-state oscillations. To obtain reliable, real-time MPPT under non-uniform irradiance, this research suggests a hybrid control architecture that combines a deep recurrent reinforcement learning (DRL) agent with metaheuristic-based parameter optimization and a digital-twin training …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 1, 2026 · pp. 35–46 Read article
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Deep Reinforcement Learning-Based MPPT for PV Systems Under Partial Shading: A HybridMetaheuristic-Optimized Control Framework.
Abstract: Partial shade and quickly changing environmental conditions make Maximum Power Point Tracking (MPPT) in photovoltaic (PV) arrays particularly difficult. In these situations, traditional algorithms frequently fail to find the global optimum or experience sluggish convergence and steady-state oscillations. To obtain reliable, real-time MPPT under non-uniform irradiance, this research suggests a hybrid control architecture that combines a deep recurrent reinforcement learning (DRL) agent with metaheuristic-based parameter optimization and a digital-twin training …
Published in International Journal of Electrical Power System and Technology · Vol. 12, Issue 1, 2026 Read article