Toward the Energy-Saving Optimization of WLAN Deployment in Real 3-D Environment: A Hybrid Swarm Intelligent Method

Liu, Peng; Hu, Qian; Jin, Kezhong; Yu, Guanding; Tang, Zhenzhou

doi:10.1109/jsyst.2021.3065434

Cited by 21 publications

(16 citation statements)

References 36 publications

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“…Bio-inspired optimization algorithm represents a class of metaheuristic algorithms whose principles are inspired by biology and natural phenomenon and have been successfully applied to solve different problems [49]. This category of algorithms exploits the basic process of nature and then translates them into rules or procedures, which are then model computationally for solving complex real-life problems [50] [51] [52], [53], [54], [55], [56], [57]. They are mostly population-based algorithms, and examples of such are Satin Bowerbird Optimizer (SBO), Earthworm Optimisation Algorithm (EOA), Wildebeest Herd Optimization (WHO), Virus Colony Search (VCS), Slime Mould Algorithm (SMA), Invasive weed colonization optimization (IWO), Biogeography-based optimization (BBO), Coronavirus optimization algorithm (COA), emperor penguin and salp swarm algorithm (ESA).…”

Section: Metaheuristic Optimization Algorithms: Bioinspired-based Algorithmsmentioning

confidence: 99%

Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

et al. 2022

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Nature computing has evolved with exciting performance to solve complex real-world combinatorial optimization problems. These problems span across engineering, medical sciences, and sciences generally. The Ebola virus has a propagation strategy that allows individuals in a population to move among susceptible, infected, quarantined, hospitalized, recovered, and dead sub-population groups. Motivated by the effectiveness of this strategy of propagation of the disease, a new bio-inspired and population-based optimization algorithm is proposed. This study presents a novel metaheuristic algorithm named Ebola optimization search algorithm (EOSA) based on the propagation mechanism of the Ebola virus disease. First, we designed an improved SIR model of the disease, namely SEIR-HVQD: Susceptible (S), exposed (E), Infected (I), Hospitalized (H), recovered (R), Vaccinated (V), Quarantine (Q), and Death or death (D). Secondly, we represented the new model using a mathematical model based on a system of first-order differential equations. A combination of the propagation and mathematical models was adapted for developing the new metaheuristic algorithm. To evaluate the performance and capability of the proposed method in comparison with other optimization methods, two sets of benchmark functions consisting of forty-seven (47) classical and thirty (30) constrained IEEE-CEC benchmark functions were investigated. The results indicate that the performance of the proposed algorithm is competitive with other state-of-the-art optimization methods based on scalability, convergence, and sensitivity analyses. Extensive simulation results show that the EOSA outperforms popular metaheuristic algorithms such as the Particle Swarm Optimization Algorithm (PSO), Genetic Algorithm (GA), and Artificial Bee Colony Algorithm (ABC). Also, the algorithm was applied to address the complex problem of selecting the best combination of convolutional neural network (CNN) hyperparameters in the image classification of digital mammography. Results obtained showed the optimized CNN architecture successfully detected breast cancer from digital images at an accuracy of 96.0%.

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Section: Metaheuristic Optimization Algorithms: Bioinspired-based Algorithmsmentioning

confidence: 99%

Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

et al. 2022

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“…This algorithm incorporates the non-dominated sorting approach and the reference point strategy to outperform the NSGA-II. Moreover, a novel hybrid swarm intelligence optimization algorithm, named PSO-Lévy-DFOA is developed in [27] that integrates PSO search strategy and Lévy flight applied to WLAN planning.…”

Section: ) Resolution Approachesmentioning

confidence: 99%

Multi-Objective Optimization of Energy-Efficient Buffer Allocation Problem for Non-Homogeneous Unreliable Production Lines

2022

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The current context of rising ecological awareness and high competitiveness, reveals a strong necessity to integrate the sustainability paradigm into the design of production systems. The buffer allocation problem is of particular interest since buffers absorb disruptions in the production line. However, despite the rich literature addressing the BAP, there are no studies that use a multi-objective framework to deal with energetic considerations. In this study, the energy-efficient buffer allocation problem (EE-BAP) is studied through a multi-objective resolution approach. The multi-objective problem is solved to optimize two conflicting objectives: maximizing production throughput and minimizing its energy consumption, under a total storage capacity available. The weighted sum and epsilon-constraint methods as well as the elitist non-dominated sorting genetic algorithm (NSGA-II) are adapted and implemented to solve the EE-BAP. The obtained solutions are analyzed and compared using different performance metrics. Numerical experiments show that epsilon-constraint outperforms the NSGA-II when considering comparable computational time. The Pareto solutions obtained are trade-offs between the two objectives, enabling decision making that balances productivity maximization with energy economics in the design of production lines.

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“…The massive amount of data traffic generated by the many different types of mobile services has led to a rapid increase in the number of base stations (BSs) deployed within the same network region [1]- [3]. This gradually accelerates network densification [4]- [6]. In addition, cellular networks have tended to use a higher frequency (e.g., a frequency in the terahertz range), which decreases the cell radius because of the larger attenuation of transmit power and requires more BSs to be deployed in the same network area [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Optimal Resource Allocation Considering Non-Uniform Spatial Traffic Distribution in Ultra-Dense Networks: A Multi-Agent Reinforcement Learning Approach

et al. 2022

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Recently, the demand for small cell base stations (SBSs) is exploding to accommodate the explosive increase in mobile data traffic. In ultra-dense small cell networks (UDSCNs), because the spatial and temporal traffic distributions are significantly disproportionate, the efficient management of the energy consumption of SBSs is crucial. Therefore, we herein propose a multi-agent distributed Q-learning algorithm that maximizes energy efficiency (EE) while minimizing the number of outage users. Through intensive simulations, we demonstrate that the proposed algorithm outperforms conventional algorithms in terms of EE and the number of outage users. Furthermore, it is shown that the proposed reinforcement learning algorithm can converge to the optimal solution despite its significantly lower computational complexity than the centralized approach.INDEX TERMS Multi-agent Q-learning, spatial traffic distribution, energy efficiency, user outage, ultradense small cell network.

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Toward the Energy-Saving Optimization of WLAN Deployment in Real 3-D Environment: A Hybrid Swarm Intelligent Method

Cited by 21 publications

References 36 publications

Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

Ebola Optimization Search Algorithm: A New Nature-Inspired Metaheuristic Optimization Algorithm

Multi-Objective Optimization of Energy-Efficient Buffer Allocation Problem for Non-Homogeneous Unreliable Production Lines

Optimal Resource Allocation Considering Non-Uniform Spatial Traffic Distribution in Ultra-Dense Networks: A Multi-Agent Reinforcement Learning Approach

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