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Nature-inspired optimisation algorithms assisted realisation of green communication via CR: a comparison study

IET COMMUNICATIONS 2018年第19期12卷 2511-2520页

作者： Kaur, Avneet Sharma, Surbhi Mishra, Amit Thapar Inst Engn & Technol Dept Elect & Commun Engn Patiala Punjab India

Cognitive radio (CR) technology enables adaptation of transmission parameters according to the operating environment and different quality of service (QoS) requirements. This feature can be applied to realise green radios as recent research trends are focused on improving the energy efficiency of wireless communication systems. A power amplifier (PA) is found to consume a major portion of energy in radio-frequency circuits for medium- and long-range transmissions. Thus, minimising PA power consumption is one of the major challenges to realise green radios. In this study, mathematical formulation of total system power consumption at CR transmitter with Class B PA is shown and its optimisation is done by parameter reconfiguration for data transmission scenario employing recently proposed nature-inspired optimisation techniques. Performance of ant lion optimiser, grasshopper optimisation algorithm, Grey wolf optimiser, Moth-flame optimisation and whale optimisation algorithm (WOA) is compared for solving this problem in terms of different performance metrics. Simulation results show the effectiveness of WOA in minimising the system power consumption by parameter adaptation in multicarrier CR system while satisfying different QoS constraints.

关键词： cognitive radio power consumption optimisation quality of service telecommunication power management radiofrequency power amplifiers parameter adaptation multicarrier CR system nature-inspired optimisation algorithms green communication cognitive radio transmission parameters green radios energy efficiency wireless communication systems power amplifier radio-frequency circuits long-range transmissions total system power consumption CR transmitter parameter reconfiguration data transmission scenario ant lion optimiser grasshopper optimisation algorithm Grey wolf optimiser Moth-flame optimisation whale optimisation algorithm performance metrics QoS constraints CR technology quality of service PA power consumption minimization mathematical formulation class B PA

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Maiden coordinated load frequency control strategy for ST-AWEC-GEC-BDDG-based independent three-area interconnected microgrid system with the combined effect of diverse energy storage and DC link using BOA-optimised PFOID controller

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IET RENEWABLE POWER GENERATION 2019年第14期13卷 2634-2646页

作者： Latif, Abdul Das, Dulal Chandra Barik, Amar Kumar Ranjan, Sudhanshu NIT Silchar Dept Elect Engn Silchar Assam India

This work presents a maiden approach of coordinated frequency control of novel solar tower (ST)-Archimedes wave energy conversion (AWEC)-geothermal energy conversion (GEC)-biodiesel driven generator (BDDG)-energy storage (ES) units and direct current (DC) links based independent three-area interconnected microgrid system (ImGS). A recent metaheuristic technique, named butterfly optimisation algorithm (BOA) is applied to obtain the optimal gains of the controllers employed with the ImGS and system participation factors. The dynamic performance of proportional-integral derivative (PID), PID with filter (PIDN), proportional-fractional-order integral derivative (PFOID) controllers with their gains tuned by different algorithms such as particle swarm optimisation (PSO), firefly algorithm (FA), whale optimisation algorithm (WOA), and BOA have been compared. Further, the effect of ES units and DC links in all the areas is analysed first time in ImGS. The results have established the superiority of the BOA-based PFOID controllers under different real-world scenarios in terms of frequency deviation, tie-line power, and objective functions. Finally, rigorous sensitivity analysis has been conducted to evaluate the superiority of BOA-optimised PFOID controller towards preserving system stability of ImGS with +/- 25% change in synchronising tie-line coefficients and bias values, and +20% change in loading condition without resetting the nominal condition gain values.

关键词： sensitivity analysis three-term control optimisation load regulation power generation control particle swarm optimisation power system interconnection frequency control distributed power generation poles and towers wave power generation geothermal power stations biofuel AC generators energy storage power system stability solar power stations ImGS system participation factors particle swarm optimisation whale optimisation algorithm DC links BOA-optimised PFOID controller load frequency control strategy diverse energy storage coordinated frequency control novel solar tower-Archimedes wave energy conversion-geothermal energy conversion-biodiesel driven generator-energy storage units butterfly optimisation algorithm ST-AWEC-GEC-BDDG-based independent three-area interconnected microgrid system direct current link-based independent three-area interconnected microgrid system metaheuristic technique dynamic performance proportional-integral derivative control PID with filter PIDN proportional-fractional-order integral derivative controllers firefly algorithm ES unit effect frequency deviation tie-line power objective functions sensitivity analysis system stability tie-line coefficient synchronization bias values

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Utilisation of ultra-capacitor in load frequency control under restructured STPP-thermal power systems using WOA optimised PIDN-FOPD controller

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第13期11卷 3318-3331页

作者： Saha, Arindita Saikia, Lalit Chandra Natl Inst Technol Silchar Dept Elect Engn Silchar India

This study addresses the impact of ultra-capacitor (UC) on load frequency control of a restructured two-area multi-source system, incorporating conventional thermal and solar thermal power plant (STPP). The thermal system consists of single reheat turbine, appropriate generation rate constraint and governor dead band. Two generating companies and distribution companies are considered in each area. Maiden attempt has been made to use a cascade combination of integer-order (IO) proportional-integral-derivative with filter and fractional-order proportional-derivative (PIDN-FOPD) controller. Performance comparison of some commonly used IO controllers with proposed PIDN-FOPD controller reveals superiority of the latter in terms of peak deviations and settling time in policy schemes like poolco, bilateral and contract violation. A more recent optimisation technique called whale optimisation algorithm (WOA) is successfully used for simultaneous optimisation of above controllers. Effect of UC using PIDN-FOPD has been studied for above policy schemes and analysis explores that use of UC improves the system dynamics. Sensitivity analysis reveals that WOA optimised PIDN-FOPD gains and system parameters are robust. It is also proved that PIDN-FOPD parameters obtained at nominal distribution companies participation matrix (DPM) are fit enough and parameters need not be optimised for change in DPMs.

关键词： supercapacitors frequency control load regulation thermal power stations three-term control solar power stations power generation control PD control sensitivity analysis optimisation participation matrix system parameters system dynamics sensitivity analysis whale optimisation algorithm policy schemes IO controllers fractional-order proportional-derivative controller integer-order proportional-integral-derivative with filter control governor dead band generation rate constraint single reheat turbine STPP solar thermal power plant restructured two-area multisource system WOA optimised PIDN-FOPD controller restructured STPP-thermal power systems load frequency control ultra-capacitor

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M-Lionwhale: multi-objective optimisation model for secure routing in mobile ad-hoc network

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IET COMMUNICATIONS 2018年第12期12卷 1406-1415页

作者： Chintalapalli, Ram Mohan Ananthula, Venugopal Reddy CVR Coll Engn Dept Comp Sci & Engn Hyderabad 500044 Telangana India Jawaharlal Nehru Technol Univ Hyderabad 500085 Telangana India

Mobile ad-hoc network (MANET) is an emerging technology that comes under the category of wireless network. Even though the network assumes that all its mobile nodes are trusted, it is impossible in the real world as few nodes may be malicious. Therefore, it is essential to put forward a mechanism that can provide security by selecting an optimal route for data forwarding. In this study, a goal programming model is designed using a hybrid optimisation algorithm, called M-Lionwhale, for secure routing. M-Lionwhale is an optimisation algorithm that incorporates lion algorithm (LA) into whale optimisation algorithm (WOA) for the optimal selection of the path in MANET. The multi-objective optimisation model considers several quality of service (QoS) parameters, namely energy, distance, link lifetime, delay, and trust. With the estimated multi-objective parameters, a fitness function is developed for the best selection of routes. The performance of the proposed algorithm is evaluated using three metrics, such as packet delivery ratio (PDR), throughput, and energy and is compared with that of existing trust-based QoS routing model, LA, and WOA. The proposed M-Lionwhale algorithm could attain the maximum performance with 24.1313% residual energy, throughput of 0.2966kbps, and PDR of 0.3051 at maximum simulation time.

关键词： mobile ad hoc networks telecommunication security routing protocols mobile radio quality of service optimisation ad hoc networks telecommunication network routing secure routing lion algorithm whale optimisation algorithm optimal selection MANET multiobjective optimisation model multiobjective parameters QoS routing model M-Lionwhale algorithm ad-hoc network Mobile emerging technology wireless network mobile nodes optimal route data forwarding goal programming model hybrid optimisation algorithm called M-Lionwhale

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Optimal day-ahead scheduling in micro-grid with renewable based DGs and smart charging station of EVs using an enhanced manta-ray foraging optimisation

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IET RENEWABLE POWER GENERATION 2022年第11期16卷 2413-2428页

作者： Bastawy, Mostafa Ebeed, Mohamed Ali, Abdelfatah Shaaban, Mostafa F. Khan, Baseem Kamel, Salah Aswan Univ Dept Elect Engn Aswan Egypt Sohag Univ Dept Elect Engn Sohag Egypt South Valley Univ Dept Elect Engn Qena Egypt Amer Univ Sharjah Dept Elect Engn Sharjah U Arab Emirates Hawassa Univ Dept Elect & Comp Engn Hawassa Ethiopia

Micro-grids (MGs) are small parts of the electrical power system that work along with the electric system or autonomously based on environmental or economic conditions. The renewable-based distributed generators (RDGs) and electric vehicle charging stations (EVCSs) are wildly incorporated in MGs. Optimal day-ahead scheduling of the MG is ahead corner of energy management for cost reduction. In addition, solving the economic load dispatch and day-ahead scheduling of the MG is a complex optimisation problem, especially considering the RDGs, EVCS, and uncertainties in the electrical system. This paper aims to optimise the day-ahead scheduling of the MG with and without a smart charging strategy for electric vehicles. An enhanced manta-ray foraging optimisation (EMRFO) algorithm is proposed to solve this optimisation problem. EMRFO depends upon boosting population diversity and the searching ability of the standard MRFO using strategies. The proposed strategies are based on quasi-oppositional-based learning and local chaotic mutation. The studied MG consists of wind turbines, fuel cells, and diesel generators. The day-ahead scheduling of the MG is solved with and without considering the uncertainties of the load demand and the wind speed. The proposed algorithm for day-ahead scheduling of the MG is compared to well-known algorithms such as anti lion optimisation, particle swarm optimisation, whale optimisation algorithm, sine cosine algorithm, and harmony search algorithm. The simulation results demonstrate that the proposed algorithm is superior to these algorithms for solving the optimisation problem. The results show that the generation cost is reduced considerably from 77,745.61 $ to 76,984.2 $ by applying the smart operation strategy.

关键词： enhanced manta-ray foraging optimisation algorithm battery powered vehicles power engineering computing distributed power generation load dispatching electric vehicle charging smart charging strategy power generation economics optimisation cost reduction renewable-based distributed generators electrical power system particle swarm optimisation metaheuristics renewable based DGs power grids electric vehicle whale optimisation algorithm wind turbines complex optimisation problem electrical system search problems electric system power generation scheduling optimal day-ahead scheduling ahead corner

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Research on ultrasonic echo signal denoising via integration of adaptive variational mode decomposition algorithm and convolutional neural network

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INSIGHT 2024年第9期66卷 567-575页

作者： Wang, Tao Yu, Cijun Zhejiang Univ Sch Mech Engn Hangzhou 310027 Peoples R China

This paper presents a novel ultrasonic signal denoising method that integrates adaptive variational mode decomposition (AVMD) with convolutional neural networks (CNNs). Initially, the whale optimisation algorithm ( WOA) is employed to optimise key parameters of variational mode decomposition, specifically the decomposition modes K and the penalty factor alpha. The ultrasonic signals are then decomposed into intrinsic mode functions (IMFs) and various statistical feature parameters, such as energy entropy, sample entropy, kurtosis and correlation factors, are calculated for each IMF. The signal-to-noise ratio (SNR) of the reconstructed signal from the IMFs is used to assign label values, forming a feature dataset. Subsequently, a CNN is utilised to train and recognise this dataset, achieving an accuracy rate of 93.94% on the test set. The results demonstrate that the CNN effectively distinguishes between various IMF combinations based on the reconstructed SNR and can proficiently identify IMF combinations with higher SNR. Finally, denoising experiments on actual ultrasonic echo signals validate the feasibility of this method for noise reduction applications.

关键词： denoise variational modal decomposition whale optimisation algorithm convolutional neural network ultrasonic signal

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Optimal operation of the multi-reservoir system: a comparative study of robust metaheuristic algorithms

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INTERNATIONAL JOURNAL OF HYDROLOGY SCIENCE AND TECHNOLOGY 2024年第3期17卷 239-266页

作者： Verma, Shashikant Prasad, A. D. Verma, Mani Kant Natl Inst Technol Raipur Dept Civil Engn Great Eastern Rd Raipur 492010 Chhattisgarh India

In a multi-reservoir system, the functioning of one reservoir affects the other reservoirs. A multi-reservoir system must be managed as a single entity for sustainable water management. Thus, assessing a multi-reservoir system's existing operating policy is crucial for integrated operation. In this study, the recently introduced grey wolf optimisation (GWO) was compared to the robust metaheuristic algorithm the whale optimisation algorithm (WOA). First, the performance of these algorithms was evaluated and compared for multi-reservoir system operation optimisation. The statistical indices coefficient of determination (R-2), root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), normalise mean square error (NMSE), and mean absolute percentage error (MAPE) were used to compare the algorithms' performance. According to the test findings, among the ten algorithms evaluated. GWO was shown to be the most effective, and it is suggested as a reliable and promising technique for optimising multi-reservoir systems.

关键词： metaheuristic algorithm multi-reservoir system Mahanadi reservoir project grey wolf optimisation GWO whale optimisation algorithm WOA

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A novel e-healthcare diagnosing system for COVID-19 via whale optimization algorithm

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2024年第7期36卷 1101-1119页

作者： Punitha, S. Al-Turjman, Fadi Stephan, Thompson Graph Era Deemed Univ Dept Comp Sci & Engn Dehra Dun Uttarakhand India Near East Univ Artificial Intelligence Engn Dept AI & Robot Inst Nicosia Turkiye Univ Kyrenia Fac Engn Res Ctr AI & IoT Kyrenia Turkiye MS Ramaiah Univ Appl Sci Fac Engn & Technol Dept Comp Sci & Engn Bangalore Karnataka India

Accurate and early diagnosis of COVID-19 can reduce the mortality rate caused by the disease across the globe. Computer-aided diagnosis (CAD) helps radiologists efficiently extract and diagnose the abnormal portions. The healthcare market is currently experiencing rapid development owing to the Internet of Things (IoT). This paper proposes a framework that integrates machine learning and intelligence-based e-Health service systems that can be used as an application of the Internet of Medical Things (IoMT) for the early diagnosis of COVID-19 disease. This framework consists of a classification approach for diagnosing the abnormalities in lung CT images using a whale optimisation algorithm (WOA) optimised wavelet neural network (WNN). WOA optimises the input features, initial weights, hidden nodes, momentum constant, and learning parameters of a WNN in the proposed system. The proposed approach extracts the Laws 16 Texture Energy Measures (LTEM) from the preprocessed CT lung images and classifies the abnormal regions with the help of a WNN classifier. The proposed framework is evaluated using a publicly available COVID-19 dataset that contains both theCOVID-19 and non-COVID-19 cases. The result shows that theproposed approach has a sensitivity of 82%, a specificity of 73.3%, and an accuracy of 84.8%.

关键词： Internet of medical things wavelet neural network whale optimisation algorithm multilayer perceptron resilient backpropagation

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Path optimisation study of door-to-door bulky waste collection by pure electric vehicles based on hard time window constraints 7

Path optimisation study of door-to-door bulky waste collecti...

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7th International Conference on Traffic Engineering and Transportation System (ICTETS)

作者： Zhou, Yuhan Xie, Chenchen Shanghai Maritime Univ Sch Logist Engn Pudong New Area 1550 Harbour Ave Shanghai Peoples R China

ISBN: (纸本)9781510674479

In recent years, with the gradual improvement of China's waste classification policy, the problem of sorting and recycling of bulky waste has attracted increasing attention. At present, there are relatively few studies on the use of pure electric vehicles for waste collection and transportation operations in China. In view of this problem, this paper proposes a path optimisation study of door-to-door collection and transportation of bulky waste by pure electric vehicles considering the loading rate, with the goal of reducing the cost of bulky waste collection and transportation, constructs a door-to-door collection and transportation path optimisation model for pure electric vehicles considering the loading rate of bulky waste and the constraints of a hard time window, and conducts optimisation experiments through the whale optimisation algorithm based on the greedy crossover mechanism. The optimisation experiments are carried out through the whale optimisation algorithm based on the greedy crossover mechanism, and the experimental results verify that the proposed greedy whale optimisation algorithm can efficiently optimise the collection and transportation paths of pure electric vehicles and save the logistics and transportation costs.

关键词： pure electric vehicle bulky waste collection path optimisation whale optimisation algorithm

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Enhancing Ellipsometry Analysis with whale optimisation: A Novel Approach for Precise Material Characterisation

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Procedia CIRP 2024年 129卷 127-132页

作者： Liyuan Ma Xipeng Xu Changcai Cui Shan Lou Yuchu Qin Paul Scott Wenhan Zeng Institute of Manufacturing Engineering Huaqiao University Xiamen China National & Local Joint Engineering Research Center for Intelligent Manufacturing Technology of Brittle Materials Products Huaqiao University Xiamen China The EPSRC Future Metrology Hub University of Huddersfield Huddersfield UK College of Metrology Measurement and Instrument China Jiliang University Hangzhou China

To address the challenges associated with initial point selection and to enhance the speed of analysis in ellipsometry data interpretation, this study introduces a novel algorithm based on whale optimisation. The algorithm is designed to accurately determine samples' thickness and refractive index across transparent wavelength ranges. Comparative analysis demonstrates that the Psi and Delta spectra generated by the proposed algorithm align more closely with those obtained through the traditional transfer matrix method (TMM), underscoring the algorithm's improved accuracy. Furthermore, the algorithm's efficacy is validated by the excellent agreement between the properties of the established sample model (thickness and refractive index) and those acquired by transfer matrix data. This validation confirms the utility of the proposed algorithm in fitting ellipsometry data, establishing it as a robust tool for extracting essential fitting parameters of samples with enhanced precision and efficiency.

关键词： Spectroscopic ellipsometry whale optimisation algorithm Data fitting Thickness Refractive index

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