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All Journal International Journal of Electrical and Computer Engineering Journal of Robotics and Control (JRC)
Essaadoui, Alami
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Mechanical Engineering

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Adaptive Particle Swarm and Ant Colony Optimization Path Planning for Autonomous Robot Navigation Essaadoui, Alami; Baba, Youssef; Hamed, Oussama; Hamlich, Mohamed; Guemimi, Chafik; EL Kebch, Ali
Journal of Robotics and Control (JRC) Vol. 6 No. 4 (2025)
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18196/jrc.v6i4.26853

Abstract

Path planning in cluttered and uncertain environments remains a significant challenge in robotics, autonomous navigation, and logistics optimization. This paper proposes a novel Adaptive Hybrid PSO-ACO Planner, which synergistically combines Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) to compute efficient paths in grid-based environments with static obstacles. Unlike traditional fixed-phase hybrids, our approach features a dynamic switching strategy between PSO and ACO based on real-time convergence behavior, allowing the algorithm to maintain progress and escape local minima. Additionally, adaptive parameter tuning is integrated to enhance the balance between global exploration and local exploitation throughout the search. The switching logic is governed by two criteria: a stagnation threshold that triggers phase transitions and a progress-dependent adaptation mechanism that adjusts search intensities over time. PSO dominates the early search phase, rapidly exploring the solution space, while ACO refines promising paths through pheromone-guided optimization in later stages. The proposed planner also includes a path reconstruction module to ensure solution completeness and robustness. Experimental evaluations on grid-based environments demonstrate that the proposed method consistently achieves higher path quality and faster convergence compared to standalone PSO and ACO approaches. Quantitative results demonstrate notable improvements in path efficiency and overall success rate across a range of obstacle densities. These advancements establish the Adaptive Hybrid PSOACO Planner as a robust and efficient tool for real-time and practical deployment in autonomous robot navigation systems.
AI-MG-LEACH: investigation of MG-LEACH in wireless sensor networks energy efficiency applied the advanced algorithm Ouldzira, Hicham; Essaadoui, Alami; Hanine, Mustapha EL; Mouhsen, Ahmed; Mes-Adi, Hassane
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5080-5090

Abstract

Wireless sensor networks (WSNs) play a crucial role in data collection across various fields like environmental monitoring and industrial automation. The energy efficiency of these networks, powered by limited-capacity batteries, is key to their performance. Clustering protocols such as low- energy adaptive clustering hierarchy (LEACH) are widely used to optimize energy consumption. To enhance LEACH’s performance, MG-LEACH was introduced, improving cluster head selection to extend network lifespan. This study compares MG-LEACH with AI-MG-LEACH, which incorporates artificial intelligence (AI) to further improve energy efficiency by selecting cluster heads based on factors like residual energy. Simulations show AI-MG-LEACH reduces energy consumption, extends network life, and enhances data reliability, outperforming MG-LEACH.
Co-Authors Baba, Youssef EL Kebch, Ali Guemimi, Chafik Hamed, Oussama Hamlich, Mohamed Hanine, Mustapha EL Mes-Adi, Hassane Mouhsen, Ahmed Ouldzira, Hicham