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A Hybrid of Grey Wolf Optimization and Genetic algorithm for Optimization of Hybrid Wind and Solar Renewable Energy System

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Journal of the Operations Research Society of China 2022年第4期10卷 749-762页

作者： Diriba Kajela Geleta Mukhdeep Singh Manshahia Department of Mathematics Punjabi UniversityPatialaPunjab147002India Department of Mathematics Madda Walabu UniversityBale Robe4540OromiaEthiopia

In this paper,a hybrid of grey wolf optimization(GWO)and genetic algorithm(GA)has been implemented to minimize the annual cost of hybrid of wind and solar renewable energy *** was named as hybrid of grey wolf optimization and genetic algorithm(HGWOGA).HGWOGA was applied to this hybrid problem through three ***,the balance between the exploration and the exploitation process was done by grey wolf optimizer ***,we divided the population into subpopulation and used the arithmetical crossover operator to utilize the dimension reduction and the population partitioning *** last,mutation operator was applied in the whole population in order to refrain from the premature convergence and trapping in local *** code was designed to implement the proposed *** result of this algorithm is compared with the results of iteration method,GWO,GA,artificial bee colony(ABC)and particle swarm optimization(PSO)*** results obtained by this algorithm are better when compared with those mentioned in the text.

关键词： Hybrid renewable energy Optimization nature-inspired algorithm Grey wolf optimization Genetic algorithm

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One-pixel and X-pixel adversarial attacks based on smell bees optimization algorithm

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2023年第1期149卷 562-576页

作者： Ali, Yamina Mohamed Ben Univ Badji Mokhtar Annaba Comp Sci Dept Annaba 23000 Algeria

In the present paper, we propose two new black-box attacks to generate adversarial images. The first proposition is a new version One-pixel attack. It consists at reducing the search space of pixels by segmenting the image at the first stage. Then, a nature-inspired algorithm is applied to find the optimal pixel able to modify the decision of the deep learning model. The second proposition is X-pixel attack introduced to overcome the drawbacks of the One-pixel attack. It consists at firstly construct a large sub-set of potential pixels, then at apply a feature selection approach to optimize it. Many experiments are undertaken with CNN and ResNet learning models on MNIST and CIFAR-10 datasets. Some comparisons are also done with FGSM, PGD and JSMA attacks.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Generation of adversarial examples Deep neural networks nature-inspired algorithm

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CL-IoT: cross-layer Internet of Things protocol for intelligent manufacturing of smart farming

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021年第7期12卷 7777-7791页

作者： Mahajan, Hemant B. Badarla, Anil Junnarkar, Aparna A. Univ Technol Jaipur Rajasthan India Univ Technol Comp Sci & Engn Dept Jaipur Rajasthan India PES Modern Coll Engn Pune Maharashtra India

Internet of Things (IoT) for Intelligent Manufacturing of Smart Farming gained significant attention from researchers to automate various farming applications called Smart Farming (SF). The sensors and actuators deployed across the farm using which farmers receive periodic farm information related to temperature, soil moisture, light intensity, and water used, etc. The clustering-based methods are proven energy-efficient solutions for Wireless Sensor Networks (WSNs). However, by considering long-distance communications and scalable networks of IoT enabled SF;the present clustering solutions cannot be feasible and having higher delay and latency for various SF applications. To focus on requirements SF applications, an efficient and scalable protocol for remote monitoring and decision making of farms in rural regions called CL-IoT protocol proposed. A cross-layer-based clustering and routing algorithms have designed to reduce network communication delay, latency, and energy consumption. The cross-layer-based optimal Cluster Head (CH) selection solution proposed to overcome the energy asymmetry problem in WSN. The parameters of different layers like a physical, medium access control (MAC), and network layer of each sensor used to evaluate and select optimal CH and efficient data transmission. The nature-inspired algorithm proposed with a novel probabilistic decision rule functions as a fitness function to discover the optimal route for data transmission. The performance of the CL-IoT protocol analyzed using NS2 by considering the energy-efficiency, computational-efficiency, and QoS-efficiency factors. Compared to state-of-art IoT-based farming methods, the CL-IoT reduces energy consumption, communication overhead, and end-to-end delay up to a certain extent and maximizes the network throughput.

关键词： Cross-layer Clustering Intelligent manufacturing nature-inspired algorithm Smart farming Internet of Things

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A novel optimization algorithm inspired by the creative thinking process

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SOFT COMPUTING 2015年第10期19卷 2955-2972页

作者： Feng, Xiang Zou, Ru Yu, Huiqun Chinese Univ Sci & Technol Dept Comp Sci & Engn Shanghai 200237 Peoples R China

Creative thinking, which plays an essential role in the progress of human society, has an outstanding problem-solving ability. This paper presents a novel creativity-oriented optimization model(COOM) and algorithm (COOA) inspired by the creative thinking process. At first, COOM is constructed by simplifying the procedure of creative thinking-while retaining its main characteristics. And then, COOA is presented for continuous optimization problems. It is a realization of COOM. As a new nature-inspired algorithm, COOA is different from other similar algorithms in terms of the basic principle, mathematical formalization and properties. Features of the COOM and the corresponding algorithm include a powerful processing ability for the complex problems, namely high-dimensional, highly nonlinear and random problems. The proposed approach also has the advantages in terms of the higher intelligence, effectiveness, parallelism and lower computation complexity. The properties of COOA, including convergence and parallelism, are discussed in detail. The numerous simulations on the CEC-2013 real-parameter optimization benchmark functions' problems have shown the effectiveness and parallelism of the proposed approach.

关键词： Creativity-oriented optimization algorithm nature-inspired algorithm Creative thinking Numerical function optimization

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Bat algorithm: literature review and applications

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INTERNATIONAL JOURNAL OF BIO-inspired COMPUTATION 2013年第3期5卷 141-+页

作者： Yang, Xin-She He, Xingshi Middlesex Univ Sch Sci & Technol London NW4 4BT England Xian Polytech Univ Sch Sci Xian 710048 Peoples R China

Bat algorithm (BA) is a bio-inspired algorithm developed by Xin-She Yang in 2010 and BA has been found to be very efficient. As a result, the literature has expanded significantly in the last three years. This paper provides a timely review of the bat algorithm and its new variants. A wide range of diverse applications and case studies are also reviewed and summarised briefly here. In addition, we also discuss the essence of an algorithm and the links between algorithms and self-organisation. Further research topics are also discussed.

关键词： algorithm bat algorithm cuckoo search firefly algorithm eagle strategy nature-inspired algorithm optimisation metaheuristics

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Multi-objective RIME algorithm-based techno economic analysis for security constraints load dispatch and power flow including uncertainties model of hybrid power systems

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ENERGY REPORTS 2024年 11卷 4423-4451页

作者： Pandya, Sundaram B. Kalita, Kanak Jangir, Pradeep Cep, Robert Migdady, Hazem Chohan, Jasgurpreet Singh Abualigah, Laith Mallik, Saurav Shri KJ Polytech Dept Elect Engn Bharuch 392001 India Vel Tech Rangarajan Dr Sagunthala R&D Inst Science Dept Mech Engn Avadi 600062 India Saveetha Inst Med & Tech Sci Saveetha Sch Engn Dept Biosci Chennai 602105 India VSB Tech Univ Ostrava Fac Mech Engn 17 Listopadu 2172-15 Ostrava 70800 Poruba Czech Republic Oman Coll Management & Technol CSMIS Dept Barka 320 Oman Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Chandigarh Univ Univ Ctr Res & Dev Mohali 140413 India Al al Bayt Univ Prince Hussein Bin Abdullah Fac Informat Technol Comp Sci Dept Mafraq 25113 Jordan Al Ahliyya Amman Univ Hourani Ctr Appl Sci Res Amman 19328 Jordan Middle East Univ MEU Res Unit Amman Jordan Appl Sci Private Univ Appl Sci Res Ctr Amman 11931 Jordan Univ Sains Malaysia Sch Comp Sci Pulau 11800 Pinang Malaysia Sunway Univ Malaysia Sch Engn & Technol Petaling Jaya 27500 Malaysia Harvard TH Chan Sch Publ Hlth Dept Environm Hlth Boston MA 02115 USA

In recent times, the landscape of power systems has undergone significant evolution, particularly with the integration of diverse renewable energy sources (RESs). This advancement presents an invaluable opportunity to enhance energy efficiency in the modern power grid, primarily by bolstering the role of stochastic RESs. The challenge lies in the optimal power flow (OPF), a multifaceted and non-linear optimization challenge that grows more complex with the inclusion of stochastic RESs that aims to optimize the allocation of power system resources to minimize the operational cost while maintaining the stability and security of the system. Addressing this, the current study introduces an innovative optimization approach, the Multi-Objective RIME (MORIME) algorithm. Drawing inspiration from the physical phenomenon of rime-ice, called the RIME, the MORIME seeks to effectively tackle OPF issues. This algorithm enhances solution accuracy by smartly dividing with nondominated sorting and crowding distance mechanism. The proposed OPF model incorporates three types of RESs: solar photovoltaic, wind and small-scale hydropower units. While uncertainties in wind speed and solar irradiation are managed through Monte Carlo simulations, the small hydro unit is considered a constant power source. The efficacy of the MORIME algorithm is tested on IEEE 30 bus systems and results indicate that the MORIME method identifies the optimal solution for the multi-objective OPF problem while satisfying the power system constraints, thereby proving its effectiveness and superiority over MOWOA, MOGWO, MOALO, MOMRFO and MOAGDE in terms of Hyper Volume (HV) and reciprocal of Pareto Sets Proximity (1/PSP) metrices. The MORIME source code is available at: https://***/kanak02/MORIME

关键词： Multi-objective problem nature-inspired algorithm RIME, Pareto front Optimal power flow Metaheuristics Renewable energy resources

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A comparative study of optimization algorithms for parameter estimation of PV solar cells and modules: Analysis and case studies

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ENERGY REPORTS 2022年 8卷 13047-13065页

作者： Abdel-Basset, Mohamed Mohamed, Reda Sharawi, Marwa Abdel-Fatah, Laila Abouhawwash, Mohamed Sallam, Karam Zagazig Univ Fac Comp & Informat Dept Comp Sci Zagazig 44519 Egypt Amer Univ Kuwait Coll Engn & Appl Sci Salmiya Kuwait Mansoura Univ Fac Sci Dept Math Mansoura Egypt Michigan State Univ Coll Engn Dept Computat Math Sci & Engn CMSE E Lansing MI 48824 USA Univ Canberra 2601 Canberra ACT 2601 Australia

The parameter assessment of solar cells and photovoltaic (PV) modules is a challenging task due to the non-linearity behavior of the current-voltage (I-V) characteristic curve. This paper presents two hybrid nature-inspired algorithms for estimating the unknown parameters of the Single-Diode Model (SDM), and Double-Diode Model (DDM). These algorithms are based on borrowed exploration and exploitation schemes from three well-known optimization algorithms: Whale optimization algorithm (WOA), Marine Predators algorithm (MPA), and Generalized Normal Distribution Optimization (GNDO) algorithm. The first proposed algorithm is called Marine-Whale-Generalized (MWG) algorithm. In addition, MWG is effectively integrated with novel exploration and exploitation schemes to further its exploration and exploitation operators in a new variant known as (MWGG). A solar cell from RTC France and five commercial PV module models, including Photowatt-PWP201 (PWP), Ultra 85P (Ultra), and STM6-40/36 (STM), are being used to test the efficacy and efficiency of the proposed algorithms. The experimental findings proved that MWGG is more accurate and faster at convergent results compared to other comparators. (C) 2022 The Author(s). Published by Elsevier Ltd.

关键词： Parameter estimation nature-inspired algorithm Solar PV cell/module models Single-diode Model Double-diode Model

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Particle state change algorithm

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SOFT COMPUTING 2018年第8期22卷 2641-2666页

作者： Feng, Xiang Xu, Hanyu Yu, Huiqun Luo, Fei East Univ Sci & Technol Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ Smart City Collaborat Innovat Ctr Shanghai Peoples R China

The matter state change, which is a common phenomenon in nature, shows the process that the matter how to reach the optimal state in the environment. This paper presents a novel particle state change model (PSCM) inspired by mimicking the process of the matter state change. Based on PSCM, a novel particle state change (PSC) algorithm is proposed for solving continuous optimization problems. As a new algorithm, PSC has many differences from other similar nature-inspired algorithms in terms of the basic principle models, mathematical formalization and properties. This paper considers three states of the matter, namely gas state, liquid state and solid state. In a certain circumstance, the matter always converts from an unstable state into a stable state. It is similar to find the optimal solution of an optimization problem. The proposed algorithm also has the advantages in the respects of higher intelligence, effectiveness and lower computation complexity. And the convergence property of PSC is discussed in detail. In order to illustrate the ability of solving optimization problems in continuous domain, the new proposed algorithm is tested on basic function optimization, CEC2016 single-objective real-parameter numerical optimization and CEC2016 learning-base real-parameter single-objective optimization, and compared with eleven existing algorithms. The numerous simulations have shown the effectiveness and suitability of the proposed approach.

关键词： Particle state change algorithm nature-inspired algorithm Matter state change Numerical function optimization

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Philippine Eagle Optimization algorithm

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IEEE ACCESS 2022年 10卷 29089-29120页

作者： Enriquez, Erika Antonette T. Mendoza, Renier G. Velasco, Arrianne Crystal T. Univ Philippines Diliman Inst Math Quezon City 1101 Philippines Univ Philippines Diliman Nat Sci Res Inst Quezon City 1101 Philippines

We propose the Philippine Eagle Optimization algorithm (PEOA), which is a meta-heuristic and population-based search algorithm inspired by the territorial hunting behavior of the Philippine Eagle. From an initial random population of eagles in a given search space, the best eagle is selected and undergoes a local food search using the interior point method as its means of exploitation. The population is then divided into three subpopulations, and each subpopulation is assigned an operator which aids in the exploration. Once the respective operators are applied, the new eagles with improved function values replace the older ones. The best eagle of the population is then updated and conducts a local food search again. These steps are done iteratively, and the food searched by the final best eagle is the optimal solution of the search space. PEOA is tested on 20 optimization test functions with different modality, separability, and dimension properties. The performance of PEOA is compared to 13 other optimization algorithms. To further validate the effectiveness of PEOA, it is also applied to image reconstruction in electrical impedance tomography and parameter identification in a neutral delay differential equation model. Numerical results show that PEOA can obtain accurate solutions to various functions and problems. PEOA proves to be the most computationally inexpensive algorithm relative to the others examined, while also helping promote the critically endangered Philippine Eagle.

关键词： Electrical impedance tomography metaheuristic search nature-inspired algorithm neutral delay differential equation numerical optimization Philippine Eagle

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Cricket behaviour-based evolutionary computation technique in solving engineering optimization problems

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APPLIED INTELLIGENCE 2016年第2期44卷 362-376页

作者： Canayaz, Murat Karci, Ali Yuzuncu Yil Univ Comp Sci Res & Applicat Ctr TR-65080 Van Turkey Inonu Univ Fac Engn Dept Comp Engn TR-44280 Malatya Turkey

Meta-heuristicalgorithms are widely used in various areas such as engineering, statistics, industrial, image processing, artificial intelligence etc. In this study, the Cricket algorithm which is a novel nature-inspired meta-heuristic algorithm approach which can be used for the solution of some global engineering optimization problems was introduced. This novel approach is a meta-heuristic method that arose from the inspiration of the behaviour of crickets in the nature. It has a structure for the use in the solution of various problems. In the development stage of the algorithm, the good aspects of the Bat, Particle Swarm Optimization and Firefly were experimented for being applied to this algorithm. In addition to this, because of the fact that these insects intercommunicate through sound, the physical principles of sound propagation in the nature were practiced in the algorithm. Thanks to this, the compliance of the algorithm to real life tried to be provided. This new developed approach was applied on the familiar global engineering problems and the obtained results were compared with the results of the algorithm applied to these problems.

关键词： nature-inspired algorithm Meta-heuristic algorithm Evolutionary computation Cricket algorithm Applied intelligence

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