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2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： A H M Rafiul Awal Zheng Dou Md Al Shayokh Muhammad Irshad Zahoor College of Information and Communication Engineering Harbin Engineering University

ISBN: (纸本)9781509038237;9781509038220

Computational complexity considers as challenging issue in conventional Partial Transmit Sequence(PTS) scheme, even though it has good performance to reduce PAPR. In this paper, one of the recently developed bio-inspired algorithm named Chicken Swarm Optimization(CSO) is implemented with the combination of Partial Transmit Sequences to reduce PAPR, one of the most featured problems in OFDM system. Results show that CSO with PTS scheme reduces the computational complexity and improves the reduction of PAPR up to 2.25 d B at 0.0011 complementary cumulative distribution function(CCDF) for an unmodified OFDM signal.

关键词： bio-inspired algorithm CSO partial transmit sequence peak to average power ratio computational complexity

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Threat Hunting in Internet of Things Networks with bio-inspired Models

Threat Hunting in Internet of Things Networks with Bio-Inspi...

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2023 IEEE International Conference on Big Data, BigData 2023

作者： Lim, Jia Mei Grace Olayinka, Olakunle University of Sheffield Department of Computer Science Sheffield United Kingdom

ISBN: (纸本)9798350324457

Cyber threat hunting is time-consuming as large data quantities are analysed to hunt down attacks that have evaded existing security measures. With an increasing number of Internet of Things (IoT) devices, the subsequently increasing data quantities make finding attacks through traditional approaches impossible. Existing research has highlighted the use of signature-based, visualisation, analytical, and Artificial Intelligence (AI) approaches to threat detection and anomaly detection. This paper contributes to the research on threat hunting using AI and presents novel models for threat hunting in IoT networks. The proposed models effectively demonstrate the importance of preprocessing approaches and model adaptability while challenging the need for overly complicated models. © 2023 IEEE.

关键词： bio-inspired algorithm Internet of Things (IoT) Message Queuing Telemetry Transport (MQTT) Protocol multilayer perceptron reinforcement learning Threat hunting

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A biologically-inspired Power Control algorithm for Energy-Efficient Cellular Networks

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ENERGIES 2016年第3期9卷 161页

作者： Choi, Hyun-Ho Lee, Jung-Ryun Hankyong Natl Univ Inst Informat Technol Convergence Dept Elect Elect & Control Engn 327 Chungang Ro Anseong 17579 South Korea Chung Ang Univ Sch Elect Engn 84 Heukseok Ro Seoul 06974 South Korea

Most of the energy used to operate a cellular network is consumed by a base station (BS), and reducing the transmission power of a BS can therefore afford a substantial reduction in the amount of energy used in a network. In this paper, we propose a distributed transmit power control (TPC) algorithm inspired by bird flocking behavior as a means of improving the energy efficiency of a cellular network. Just as each bird in a flock attempts to match its velocity with the average velocity of adjacent birds, in the proposed algorithm, each mobile station (MS) in a cell matches its rate with the average rate of the co-channel MSs in adjacent cells by controlling the transmit power of its serving BS. We verify that this bio-inspired TPC algorithm using a local rate-average process achieves an exponential convergence and maximizes the minimum rate of the MSs concerned. Simulation results show that the proposed TPC algorithm follows the same convergence properties as the flocking algorithm and also effectively reduces the power consumption at the BSs while maintaining a low outage probability as the inter-cell interference increases;in so doing, it significantly improves the energy efficiency of a cellular network.

关键词： green base station flocking model energy efficiency power control algorithm bio-inspired algorithm energy-efficient cellular network

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bio-inspired Optimization-Based Path Planning algorithms in Unmanned Aerial Vehicles: A Survey

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SENSORS 2023年第6期23卷 3051-3051页

作者： Poudel, Sabitri Arafat, Muhammad Yeasir Moh, Sangman Chosun Univ Dept Comp Engn 309 Pilmun daero Gwangju 61452 South Korea

Advancements in electronics and software have enabled the rapid development of unmanned aerial vehicles (UAVs) and UAV-assisted applications. Although the mobility of UAVs allows for flexible deployment of networks, it introduces challenges regarding throughput, delay, cost, and energy. Therefore, path planning is an important aspect of UAV communications. bio-inspired algorithms rely on the inspiration and principles of the biological evolution of nature to achieve robust survival techniques. However, the issues have many nonlinear constraints, which pose a number of problems such as time restrictions and high dimensionality. Recent trends tend to employ bio-inspired optimization algorithms, which are a potential method for handling difficult optimization problems, to address the issues associated with standard optimization algorithms. Focusing on these points, we investigate various bio-inspired algorithms for UAV path planning over the past decade. To the best of our knowledge, no survey on existing bio-inspired algorithms for UAV path planning has been reported in the literature. In this study, we investigate the prevailing bio-inspired algorithms extensively from the perspective of key features, working principles, advantages, and limitations. Subsequently, path planning algorithms are compared with each other in terms of their major features, characteristics, and performance factors. Furthermore, the challenges and future research trends in UAV path planning are summarized and discussed.

关键词： bio-inspired algorithm optimization algorithm path planning unmanned aerial vehicle UAV communication UAV path planning

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Whale optimization-orchestrated Federated Learning-based resource allocation scheme for D2D communication

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AD HOC NETWORKS 2024年 163卷

作者： Jadav, Nilesh Kumar Tanwar, Sudeep Nirma Univ Inst Technol Dept Comp Sci & Engn Ahmadabad 382481 Gujarat India

Device -to -Device (D2D) communication plays a prominent role in mobile data offloading from the cellular infrastructure (e.g., base station). This paradigm empowers user equipment to communicate with each other directly, offering an efficient resort for data communication that eliminates the need for the base station. However, significant challenges, such as interference, resource allocation, and energy efficiency, impede the performance of D2D communication. In the context of resource allocation, most of the existing work primarily focuses on game and graph theoretical models, which raises the computational complexity as the number of D2D users increases. In this article, we formulated a sum rate maximization problem, which is solved using a combinatorial scheme comprised of Whale Optimization algorithm (WOA) and Federated Learning (FL). First, we discover the optimal CUs-D2D Groups (D2DGs) pairs by utilizing the social behavior of whales in the WOA. Only these optimal links are permitted to participate in the FL -based resource allocation, ensuring a physical layer access control. Next, we generated a dataset from the WOA-based optimal CU-D2DG links, which is employed by the Convolutional Neural Network (CNN) model for decentralized learning. FL offers a proactive decision for resource assignment, i.e., whose CU resources will be used by the D2DG. The proposed scheme is evaluated by considering different performance parameters, such as convergence rate, statistical measure (accuracy, loss), fairness (0.72), and overall sum rate ( approximate to 25 Mbps).

关键词： Resource allocation Physical layer access control D2D communication Federated Learning Whale Optimization algorithm bio-inspired algorithm

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A Discrete Adaptive Lion Optimization algorithm for QoS-Driven IoT Service Composition with Global Constraints

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JOURNAL OF NETWORK AND SYSTEMS MANAGEMENT 2024年第2期32卷 34-34页

作者： Ouhadda, Souhila Ait Hacene Sadouki, Samia Chibani Achroufene, Achour Tari, Abdelkamel Univ Bejaia Fac Exact Sci LIMED Lab Bejaia 06000 Algeria Univ Bejaia Fac Technol Bejaia 06000 Algeria Ecole Super Sci & Technol Informat & Numer Bejaia 06300 Algeria

In an Internet of Things (IoT) environment, multiple objects usually interact with one another to meet a complex user's request. This involves the composition of several atomic IoT services. Given a large number of functionally equivalent services with different Quality of Service (QoS) values, the service composition problem remains one of the main challenges in IoT environments. This paper presents a Discrete Adaptive Lion Optimization algorithm (DALOA) to select IoT services in a composition process while considering global user QoS constraints. DALOA is based on the Lion Optimization algorithm (LOA) and developed by combining several LOA operators, such as roaming, mating, and migration. First, DALOA divides the initial population into two sub-populations: pride and nomad, and each sub-population has its search strategies. Second, the roaming nomad process follows a random searching mode (strong exploration) to avoid being trapped in local optima. Third, the roaming pride searching mode represents strong local research, ensuring more efficient exploitation. Four, mating (mating pride, mating nomad) allows for information sharing between members of the same population. Finally, the migration operator is used to ensure population diversity by allowing information sharing between the pride and the nomad. The simulation results show that DALOA obtains the best compositional optimality and finds the near-optimal composition of the IoT services in a reasonable execution time compared to other approaches. Indeed, the combination of the previous operators provides a good trade-off between exploration and exploitation.

关键词： bio-inspired algorithm Service composition Internet of Things Lion Optimization algorithm (LOA) Discrete Adaptive LOA Quality of Service

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Modelling and optimizing secondary metabolites production in Spirodela polyrhiza using machine learning

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JOURNAL OF CLEANER PRODUCTION 2024年 478卷

作者： Tan, Win Hung Tong, C. Y. Chua, M. X. Derek, C. J. C. Univ Sains Malaysia Sch Chem Engn Engn Campus Nibong Tebal 14300 Pulau Pinang Malaysia

Spirodela. polyrhiza as one of the potential feedstocks for industrial natural dye and pharmaceutical products, efficient process to produce from it were scarce. Here, Artificial Neural Network (ANN) and Genetic algorithm (GA) were employed for modelling and optimization respectively. Current study focus on establishing useable models and anticipating the optimal concentration of nitrate (NO3-), phosphate (PO43-) and ammonium (NH4+) to give highest relative growth rate (RGR) and produce maximum amount of anthocyanins, chlorophylls, phenolic compounds and antioxidants. In short, 12 and 11 neurons in hidden layer were found to be most suitable neuron numbers used for the modelling of growth and secondary metabolites respectively. All the models exhibited a high degree of robustness and performance with a high r(2) value (higher than 0.75) and a low mean squared error (lower than 0.05). GA also anticipated the best concentration of macronutrients to achieve the maximum growth and metabolites production with significant low errors (below 7 %). The employment of machine learning was useful for such applications.

关键词： Antioxidants Plant Anthocyanins Software bio-inspired algorithm

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Solar photovoltaic Maximum Power Point Tracking controller optimization using Grey Wolf Optimizer: A performance comparison between bio-inspired and traditional algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2023年 211卷

作者： Aguila-Leon, Jesus Vargas-Salgado, Carlos Chinas-Palaciosa, Cristian Diaz-Bello, Dacil Univ Politecn Valencia Inst Univ Invest Ingn Energet Valencia Spain Univ Guadalajara Dept Estudios Agua & Energia Guadalajara Mexico Univ Politecn Valencia Dept Ingn Electr Valencia Spain

Solar photovoltaic systems are widely used;however, their performance is bound to weather conditions, depending on irradiation, temperature, and the effect of shadows. Maximum Power Point Tracking techniques have been developed to solve this issue. Standard methods use mainly-two algorithms: Perturb and Observe and Incremental Conductance. However, such algorithms perform differently when the Solar photovoltaic system works under sudden solar irradiation changes, temperature, and load changes. This research proposes an opti-mized Maximum Power Point Tracking controller based on the Grey Wolf Optimization algorithm using the MATLAB/Simulink software as an alternative to the traditional techniques. Global efficiency and Root Mean Square Error evaluate the controller's performance. The response time is analyzed using the Grey Wolf Optimizer algorithm, Wolf Optimizer algorithm, Simulated Annealing, and Particle Swarm Optimization. These four metaheuristic algorithms are compared to the Perturb and Observe, and Incremental Conductance algorithms. The models are analyzed for the transient state and full-day operation scenarios for constant and variable ir-radiations, temperatures, and loads. The comparative results show that the Maximum Power Point Tracking controller optimized by the Grey Wolf Optimizer algorithm has superior performance, giving an average 6% output power higher than the other controllers under the test scenarios evaluated. The efficiency of the proposed model was, on average, 3% higher than the Incremental Conductance and Perturb & Observe controllers. For the MPPT controller tunning stage, the Grey Wolf Optimizer algorithm had the best performance with an RMSE of 255.3549 with a compute time of 27.3 min;the worst performing was the Particle Swarm Optimization with an RMSE of 332.4075 and 27.8 min computation time. The proposed GWO optimized MPPT controller had the faster settling time for each irradiation level compared, with an average of 0.175 s. Als

关键词： Optimization Metaheuristic algorithms Grey Wolf Optimization Microgrid Photovoltaic Maximum Power Point Tracking bio-inspired algorithm

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From honeybees to robots and back: division of labour based on partitioning social inhibition

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bioINSPIRATION & bioMIMETICS 2015年第6期10卷 066005-066005页

作者： Zahadat, Payam Hahshold, Sibylle Thenius, Ronald Crailsheim, Karl Schmickl, Thomas Karl Franzens Univ Graz Dept Zool Artificial Life Lab A-8010 Graz Austria

In this paper, a distributed adaptive partitioning algorithm inspired by division of labor in honeybees is investigated for its applicability in a swarm of underwater robots in one hand and is qualitatively compared with the behavior of honeybee colonies on the other hand. The algorithm, partitioning social inhibition (PSI), is based on local interactions and uses a simple logic inspired from age-polyethism and task allocation in honeybee colonies. The algorithm is analyzed in simulation and is successfully applied here to partition a swarm of underwater robots into groups demonstrating its adaptivity to changes and applicability in real world systems. In a turn towards the inspiration origins of the algorithm, three honeybee colonies are then studied for age-polyethism behaviors and the results are contrasted with a simulated swarm running the PSI algorithm. Similar effects are detected in both the biological and simulated swarms suggesting biological plausibility of the mechanisms employed by the artificial system.

关键词： swarm intelligence bio-inspired algorithm division of labour partitioning algorithm swarm robotics honeybees

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PANDA: A physarum-inspired algorithm to solve the multi-objective discrete network design problem

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 246卷

作者： Heeroo, Kelvin Nigel Shinday Gukhool, Oomesh Univ Mauritius Fac Informat Commun & Digital Technol Dept Software & Informat Syst Reduit Mauritius Univ Mauritius Fac Informat Commun & Digital Technol Dept Software & Informat Syst Reduit Mauritius

The Multi -Objective Discrete Network Design Problem (MO-DNDP) describes the optimisation of a transport network across multiple metrics. Due to the impractical processing times of exact solutions, heuristics have been harnessed to solve this problem. However, the scale of the networks, the set of candidate links and the metrics assessed have been limited. This research proposes a MO-DNDP that encapsulates 8 metrics across three real networks used as benchmarks scaling from 17 km2 to 41,850 km2 to encapsulate the complexity of optimising transport networks at different scales, traffic demands and constraint densities. To solve this more comprehensive MO-DNDP, this paper proposes the Physarum-inspired algorithm PANDA that improves upon the classical Physarum Solver algorithm by harnessing the attractant sensing and merging capabilities of the biological Physarum Polycephalum slime mould. PANDA is a first attempt at solving the complex MO-DNDP using the Physarum foraging model. PANDA consistently scored better than the classical Solver in the land use rating by circumventing landforms, built-up areas, and environmentally sensitive regions. Additionally, PANDA constructed networks that were on average 30 % better than the existing benchmark networks and 32 % better than the Physarum Solver networks. PANDA improved upon the benchmarks by constructing lower capacity roads that were optimised for the actual traffic demand between each origin-destination pair instead of shared arterials.

关键词： Physarum Polycephalum bio-inspired algorithm Network design problem

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