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Conference Jul 2026

Toward Intelligent Routing in Sdn: a Comparative Review of Bio-Inspired Optimization Techniques

Bio-inspired routing algorithms have gained significant attention as effective approaches for solving complex optimization problems in modern communication networks. Drawing inspiration from collective behaviors in nature, these methods have been widely applied in decentralized environments such as wireless sensor and mobile ad hoc networks. However, their adoption within Software-Defined Networks (SDN) remains relatively limited. This paper provides a systematic and comparative review of bio-inspired routing algorithms with an emphasis on their applicability in SDN. A novel taxonomy is proposed to classify these algorithms according to their behavioral characteristics and their suitability for centralized control. In addition, a unified evaluation framework is introduced to enable consistent comparison among key approaches, including Ant Colony Optimization, Particle Swarm Optimization, Bee Colony Optimization, and Grey Wolf Optimization, based on performance criteria such as convergence, scalability, and quality of service. The study also includes an SDN-oriented analysis, examining the impact of centralized control on the behavior and performance of these algorithms. Finally, the paper outlines key research challenges, highlighting the importance of real-time optimization, hybrid methodologies, and integration with intelligent control mechanisms.

Spasimir Varshilov, K. Nikolova · 0 citations
Open access Jul 2026

Optimization of Resource Allocation in 5G MIMO Networks Using Linear Assignment Algorithms

Comparison shows that, under the evaluated DeepMIMO-based scenarios, the examined assignment-based methods exhibit different trade-offs in throughput, energy-consumption-related performance, bandwidth utilization, and adaptability to varying user demands, offering useful insights for 5G MIMO resource allocation studies.

Nikolaos Prodromos, Damianos Diasakos, V. Kokkinos et al. · 0 citations
Aug 2026

Optimized task offloading and resource allocation framework for edge-assisted IoT applications

This work aims to design an efficient framework by incorporating a novel hybrid metaheuristic algorithm that combines Draco Lizard Optimization (DLO) and Sand Cat Optimization (SCO) for optimal task offloading and resource allocation for IoT applications.

Mukesh Kumar Jha, Mohit Kumar · 0 citations
Open access Aug 2026

An Improved Coati Optimization Algorithm with Urban-Traffic-Inspired Strategies for Global Optimization and Low-Carbon Microgrid Scheduling

The economic scheduling of grid-connected microgrids requires the coordinated dispatch of renewable energy sources, controllable distributed generators, battery energy storage systems, and power exchange with the utility grid while satisfying various operational constraints. Owing to the time-varying nature of renewable generation and load demand, this problem often exhibits strong nonlinearity, temporal coupling, and complex constraint characteristics. To enhance the optimization capability of the original Coati Optimization Algorithm (COA) for such constrained scheduling tasks, this paper proposes an Improved Coati Optimization Algorithm, termed ICOA. Different from the original COA, which mainly depends on random initialization, single-best individual guidance, and simple local perturbation, the proposed ICOA redesigns the search process through several urban-traffic-inspired mechanisms. First, a road-network stratified initialization strategy is employed to improve the spatial coverage and diversity of the initial population. Second, a traffic-signal-guided exploration strategy adaptively adjusts the search direction by considering population congestion and elite information. Third, a lane-changing local exploitation operator is introduced to refine promising solutions with the aid of neighborhood information. Finally, a traffic-rule-based repair mechanism is incorporated to enhance the feasibility of candidate scheduling solutions under operational constraints. The performance of ICOA is first assessed on the CEC2017 benchmark suite with 10-, 30-, 50-, and 100-dimensional test settings. The results obtained from convergence curves, boxplots, Wilcoxon signed-rank tests, and Friedman mean rank tests demonstrate that ICOA achieves competitive performance in terms of convergence accuracy, robustness, and scalability when compared with 11 advanced algorithms. In addition, ICOA is applied to a 24 h grid-connected microgrid economic scheduling case. The simulation results show that ICOA obtains the lowest mean operating cost of 1393.58, which is 13.10% lower than that of the best competing algorithm in terms of mean cost. These results suggest that ICOA is an effective and reliable optimization method for both benchmark function optimization and constrained microgrid scheduling problems.

Wenjie Zhao, Chengpeng Li · 0 citations