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Nature-inspired algorithms for Wireless Sensor Networks: A comprehensive survey

COMPUTER SCIENCE REVIEW 2021年 39卷

作者： Singh, Abhilash Sharma, Sandeep Singh, Jitendra Indian Inst Sci Educ & Res Bhopal Fluvial Geomorphol & Remote Sensing Lab Bhopal India Gautam Buddha Univ Sch ICT Greater Noida India Indian Inst Technol Kanpur Dept Elect Engn Kanpur Uttar Pradesh India

In order to solve the critical issues in Wireless Sensor Networks (WSNs), with concern for limited sensor lifetime, nature-inspired algorithms are emerging as a suitable method. Getting optimal network coverage is one of those challenging issues that need to be examined critically before any network setup. Optimal network coverage not only minimizes the consumption of limited energy of battery-driven sensors but also reduce the sensing of redundant information. In this paper, we focus on nature-inspired optimization algorithms concerning the optimal coverage in WSNs. In the first half of the paper, we have briefly discussed the taxonomy of the optimization algorithms along with the problem domains in WSNs. In the second half of the paper, we have compared the performance of two nature-inspired algorithms for getting optimal coverage in WSNs. The first one is a combined Improved Genetic algorithm and Binary Ant Colony algorithm (IGA-BACA), and the second one is Lion Optimization (LO). The simulation results confirm that LO gives better network coverage, and the convergence rate of LO is faster than that of IGA-BACA. Further, we observed that the optimal coverage is achieved at a lesser number of generations in LO as compared to IGA-BACA. This review will help researchers to explore the applications in this field as well as beyond this area. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Optimal coverage bio-inspired algorithm Lion Optimization WSNs

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Application of bio-inspired algorithms in maximum power point tracking for PV systems under partial shading conditions - A review

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2018年 81卷 840-873页

作者： Li, Guiqiang Jin, Yi Akram, M. W. Chen, Xiao Ji, Jie Univ Sci & Technol China Dept Thermal Sci & Energy Engn 96 Jinzhai Rd Hefei 230026 Anhui Peoples R China Univ Sci & Technol China Dept Precis Machinery & Instrumentat 96 Jinzhai Rd Hefei 230026 Anhui Peoples R China Univ Sci & Technol China State Key Lab Fire Sci 96 Jinzhai Rd Hefei 230026 Anhui Peoples R China Univ Agr Faisalabad Dept Farm Machinery & Power Jail Rd Faisalabad 38000 Pakistan

Solar energy is one of the most promising renewable energy resource due to its variety of advantages. The photovoltaic systems have a remarkable development over the past few decades. As the maximum power point of the photovoltaic system varies with the change in environmental conditions, the maximum power point tracking technology is necessary to harvest maximum power from the photovoltaic systems. However, multiple peaks occur in the power-voltage (P-V) curve during partial shading conditions. In such condition, many traditional maximum power point tracking methods like perturbation and observation, and incremental conductance may become invalid due to involvement in the local maximum power point. Many advanced methods based on the artificial intelligence like artificial neural network, and fuzzy logic control can track the global maximum power point. However, they are not feasible in real complex environment because they need massive training and broader experience. Alternatively, bio-inspired maximum power point tracking algorithms deal properly with such situations. In recent years, researchers have widely applied bio-inspired algorithms to track the global maximum power point of photovoltaic system during partial shading situations. This paper presents a comprehensive review of the bio-inspired algorithms used for global maximum power point tracking. Various tracking methods are discussed and compared in terms of their characteristics and corresponding improved methods. It also presents the advantages and disadvantages of each method. The modified and combined forms of these methods found to have better performance than original algorithms. Overall, the performance of swarm intelligence based algorithms is found better than evolutionary algorithms. This review may help the researchers to acquire comprehensive information about the application of bio-inspired algorithms to gain maximum power from the photovoltaic systems, and furthermore, help them to choose an

关键词： Photovoltaic (PV) system Maximum power point tracking bio-inspired algorithm Partial shading situations Global maximum power point tracking

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BEPO: A novel binary emperor penguin optimizer for automatic feature selection

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KNOWLEDGE-BASED SYSTEMS 2021年 211卷

作者： Dhiman, Gaurav Oliva, Diego Kaur, Amandeep Singh, Krishna Kant Vimal, S. Sharma, Ashutosh Cengiz, Korhan Govt Bikram Coll Commerce Dept Comp Sci Patiala 147001 Punjab India Univ Guadalajara Dept Ciencias Computac CUCEI Guadalajara Jalisco Mexico Univ Oberta Catalunya Comp Sci Dept IN3 Castelldefels Spain Sri Guru Granth Sahib World Univ Dept Comp Sci & Engn Fatehgarh Sahib Punjab India KIET Grp Inst Dept Elect & Commun Engn Ghaziabad India Natl Engn Coll Dept Informat Technol Kovilpatti Tamil Nadu India Lovely Profess Univ Sch Elect & Elect Engn Phagwara Punjab India Trakya Univ Dept Elect Elect Engn TR-22030 Edirne Turkey

Emperor Penguin Optimizer (EPO) is a metaheuristic algorithm which is recently developed and illustrates the emperor penguin's huddling behaviour. However, the original version of the EPO will fix issues that are continuing in fact but not discrete. The eight separate EPO variants have been provided in this article. Four transfer features, s-shaped and v-shaped, that are used in order to map the search space into a separate research space are considered in the proposed algorithm. The output of the proposed algorithm is validated using 25 standard benchmark functions. It also analyses the statistical sense of the proposed algorithm. Experimental findings and comparisons suggest that the proposed algorithm performs better than other algorithms. The solution also applies to the issue of feature selection. The findings reveal the supremacy of the binary emperor penguin optimization algorithm. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Emperor penguin optimizer Feature selection Discrete optimization bio-inspired algorithm

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A novel global MPPT technique based on squirrel search algorithm for PV module under partial shading conditions

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ENERGY CONVERSION AND MANAGEMENT 2021年 230卷

作者： Fares, Dalila Fathi, Mohamed Shams, Immad Mekhilef, Saad Univ MHamed Bougara Lab Signaux & Syst Inst Genie Elect & Elect Ave Independence Boumerdes 35000 Algeria UDES CDER Unite Dev Equipements Solaires Ctr Dev Energies Renouvelables Tipasa 42415 Algeria Univ Malaya Dept Elect Engn Power Elect & Renewable Energy Res Lab PEARL Fac Engn Kuala Lumpur 50603 Malaysia Swinburne Sch Software & Elect Engn Melbourne Vic Australia

The partial shading condition (PSC) makes it challenging for the PV system to harvest maximum power via maximum power point tracking (MPPT). Various MPPT algorithms based on bio-inspired optimization methods were proposed in the literature. The mechanism employed by these algorithms varies from one to another, making them perform differently when tracking the GMPP. This paper introduces a novel MPPT technique based on the improved squirrel search algorithm (ISSA). The performance of the proposed ISSA improved the tracking time by 50% in comparison with the conventional SSA algorithm. Similarly, the proposed method has also been compared with popular Genetic algorithm (GA), and particle swarm optimization (PSO). The results proved the ability of the proposed algorithm in tracking the GMPP with faster convergence and fewer power oscillations in comparison. The feasibility and effectiveness of the proposed ISSA based MPPT have been validated experimentally, and the results clearly demonstrate its capability in tracking the GMPP with an average efficiency of 99.48% and average tracking time of 0.66 s.

关键词： Global maximum power point Partial shading PV system bio-inspired algorithm Squirrel search algorithm

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Fast Convergence bio-inspired Traffic Grooming for Energy-Efficient IP-over-WDM Networks 11

Fast Convergence Bio-Inspired Traffic Grooming for Energy-Ef...

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11th International Conference on Information and Communication Technology Convergence (ICTC) - Data, Network, and AI in the age of Untact (ICTC)

作者： Lee, Chankyun Noh, Minki Seok, Woojin Korea Inst Sci & Technol Informat KISTI Adv KREONET Ctr Daejeon South Korea

ISBN: (纸本)9781728167589

In order to achieve energy-efficient IP-over-WDM networks, this paper proposes a new bio-inspired traffic grooming algorithm with short convergence time. inspired by the gene conversion mechanism in the biology, the proposed algorithm performs a lightpath conversion function in the IP-over-WDM networks. The lightpath conversion function efficiently introduces further performance improvement for the well-known traffic grooming heuristic in the literature. Under the practical network traffic condition, the proposed algorithm reduces 2-order-of-magnitude in convergence time from that of the mutation-based conventional genetic algorithm, at the cost of acceptable energy consumption overhead.

关键词： Energy-efficient IP-over-WDM networks Network dimensioning bio-inspired algorithm

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bio-inspired Scheme of Killer Whale Hunting-Based Behaviour for Enhancing Performance of Wireless Sensor Network 3rd

Bio-Inspired Scheme of Killer Whale Hunting-Based Behaviour ...

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3rd Conference on Data Engineering and Communication Technology (ICDECT)

作者： Parvathi, C. Talanki, Suresha Sri Venkateshwara Coll Engn Dept Comp Sci & Engn Bengaluru India Sri Venkateshwara Coll Engn Bengaluru India

ISBN: (纸本)9789811510977;9789811510960

Optimization is one of the best alternative solutions for addressing majority of the computational challenges associated with the wireless sensor network (WSN). At present, the successful implications of bio-inspired approaches are significantly proven to solve diversified complex problems in wireless network. However, after reviewing the existing literatures, it was found that yet there is bigger scope to enhance the capability of bio-inspired approach in the area of WSN. Therefore, this paper introduces a novel bio-inspired algorithm called as killer whale hunting (KWH) targeted for optimizing the data aggregation process with highly improved network sustenance capability. The formulation of the algorithm is designed on the basis of social and cognitive behaviour of killer whale which has a distinct style of hunting its prey. Using analytical modelling, the proposed approach was found to offer better energy efficiency and sufficient data forwarding capability in contrast to existing energy-efficient bio-inspired techniques.

关键词： Energy bio-inspired algorithm Optimization Data aggregation Wireless sensor network

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Cooperative Driven algorithm for Sheep Herd Trajectory Tracking by Two Sheepdogs

Cooperative Driven Algorithm for Sheep Herd Trajectory Track...

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第40届中国控制会议

作者： Qizhang Dong Mingjun Jiang Xinhang Xu Yu Liu Yaqi He He Cai Huanli Gao School of Automation Science and Engineering South China University of Technology

In this paper, we consider the design of sheep herd driven algorithm by two sheepdogs. The sheep herd follow simple interaction rules among sheep, namely, separation and cohesion, which are treated as prior knowledge to the sheepdogs. The objective for the sheepdogs is to drive the sheep herd to follow a desired trajectory in a cooperative way. inspired by the behaviors of real sheepdogs, for each sheepdog, we have developed a semi-circle reciprocation algorithm featuring dynamic turn-around point selection taken into consideration the ingredients of the positions of the sheep, the desired trajectory and the position of the other sheepdog. Numerical simulations are conducted to examine the performance of the proposed algorithm.

关键词： bio-inspired algorithm computational intelligence sheep herd trajectory tracking swarm system

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Spotted hyena optimizer with lateral inhibition for image matching

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MULTIMEDIA TOOLS AND APPLICATIONS 2019年第24期78卷 34277-34296页

作者： Luo, Qifang Li, Jie Zhou, Yongquan Guangxi Univ Nationalities Coll Informat Sci & Engn Nanning 530006 Peoples R China Key Lab Guangxi High Sch Complex Syst & Computat Nanning 530006 Peoples R China Jinan Univ Dept Comp Sci Guangzhou 510632 Peoples R China

A hybrid spotted hyena optimizer (SHO) based on lateral inhibition (LI) is proposed, it has been applied to solve complication image matching problems. Lateral inhibition mechanism is applied for image pre-process to make intensity gradient in the image contrast enhanced and has the ability to enhance the characters of image, which is able to improve the accuracy of image matching. SHO is inspired from the behavior of social relationship and collaborative of spotted hyenas. This algorithm search for the global optimum mainly through four steps: prey, encircling, attacking prey, and searching prey. In the algorithm, the computation of search location is drastically reduced by incorporating of fitness calculation strategy for solving the real-life optimization problems. The proposed LI-SHO method for image matching mixed together the advantages of SHO and lateral inhibition mechanism. The experiment shows that the proposed algorithm based on lateral inhibition is more effective and feasible in image matching than the other comparing algorithm.

关键词： Spotted hyena optimizer (SHO) Lateral inhibition (LI) Imagematching bio-inspired algorithm

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bio-inspired power control and channel allocation for cellular networks with D2D communications

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WIRELESS NETWORKS 2019年第3期25卷 1273-1288页

作者： Xu, Jun Guo, Chengcheng Yang, Jianfeng Wuhan Univ Elect Informat Sch Wuhan Hubei Peoples R China

With the tremendous increment of traffic in the next generation mobile networks, device to device (D2D) communication is proposed to relieve the traffic burden of the base station and improve the overall network capacity. It supports direct communications between devices and could reuse the resources of cellular users (CUs). Despite the advantages, D2D communications bring great challenges in interference management. In this paper, we study the power control and channel allocation problems in three scenarios: (1) one CU and one D2D pair;(2) one CU and multiple D2D pairs;(3) multiple CUs and multiple D2D pairs. The goal is to coordinate the mutual interferences and maximize the overall network capacity. We derive sufficient conditions to guarantee the efficiency of D2D communications in scenarios with one CU and one D2D pair. We propose the bio-inspired PSO-P power control algorithm for the scenarios with one CU and multiple D2D pairs, and the PSO-CP algorithm for the scenarios with multiple CUs and multiple D2D pairs to jointly assign channels and powers. Simulation results show that the proposed algorithms are efficient in improving the overall network capacity.

关键词： D2D Channel allocation Power control bio-inspired algorithm PSO

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PSO-Based Dynamic UAV Positioning algorithm for Sensing Information Acquisition in Wireless Sensor Networks

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IEEE ACCESS 2019年 7卷 77499-77513页

作者： Na, Ho Jeong Yoo, Sang-Jo Inha Univ Dept Informat & Commun Engn Jncheon 402751 South Korea

In this paper, we propose a dynamic unmanned aerial vehicle (UAV) positioning method to maximize the value of the sensor data information acquired from multiple UAVs in wireless sensor networks. In operations of UAVs to monitor environmental disasters or perform military missions, the value of the acquired sensing information depends on the sensor types and the elapsed time after the previous sensing time. To support real-time sensor data monitoring, the sensed data should be successfully delivered to the ground base station (or sink node) using a UAV flying ad-hoc network;herein, it is important to maintain the connectivity between the UAVs and to consider the reliability of the communication links. In this paper, particle swarm optimization (PSO) is used to derive optimum UAV locations. For a specified wireless sensor network, at each time instance, new UAV locations are updated that guarantee complete connectivity of the UAVs and maximize the value of the aggregated sensor data information. In this paper, we have formulated the UAV location search model as a constrained optimization problem with multi-objective utility functions using PSO-based bio-inspired algorithm. The simulation results demonstrate that by maximizing the intended multi-objective utility function, the proposed method can dynamically derive the optimal locations of multiple UAVs and achieve better sensing information acquisition compared to other methods.

关键词： UAV wireless sensor networks positioning bio-inspired algorithm particle swarm optimization

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