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An Adaptive Fuzzy C Means with seagull optimization algorithm for Analysis of WSNs in Agricultural Field with IoT

WIRELESS PERSONAL COMMUNICATIONS 2022年第2期126卷 1459-1480页

作者： Karunkuzhali, D. Meenakshi, Balasubramanian Lingam, Keerthi Panimalar Engn Coll Dept Informat Technol Chennai Tamil Nadu India Sri Sairam Engn Coll Dept Elect & Elect Engn Chennai Tamil Nadu India Gitam Univ Dept Comp Sci & Engn Visakhapatnam Andhra Pradesh India

In recent years, the environmental monitoring in agriculture field is an essential required application. To achieve the environmental monitoring of agriculture fields, the wireless sense networks (WSN) and internet of things is utilized. In the WSN, the energy consumption is a main issue to access the medium and transfer the networks. Hence, in this paper, adaptive fuzzy C means clustering and seagull optimization algorithm is developed for monitoring environmental conditions in agriculture field. Two main objective functions are utilized to empower the presentation of the WSN such as load balancing and energy efficient operation. The proposed method is a combination of fuzzy C means clustering and seagull optimization algorithm (SOA). The energy efficient and load balancing is achieved by optimal routing scheme by proposed method. The fuzzy C-means clustering is utilized to empower the energy efficient operation and load balancing. In the fuzzy C-means clustering, the SOA is utilized to select the optimal path selection. The proposed method is executed by NS2 simulator and performances are compared with existing methods such as atom search optimization and emperor penguin optimization respectively. The performance metrics are delay, drop, throughput, energy consumption, network lifetime, overhead and delivery ratio.

关键词： Clustering Griculture fields Environmental monitoring Fuzzy c means clustering seagull optimization algorithm

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A Multi-Objective Quantum-Inspired seagull optimization algorithm Based on Decomposition for Unmanned Aerial Vehicle Path Planning

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IEEE ACCESS 2022年 10卷 110497-110511页

作者： Wang, Peng Deng, Zhiliang Nanjing Univ Informat Sci & Technol Sch Automat Nanjing 210044 Peoples R China

The traditional seagull optimization algorithm cannot handle multi-objective optimization problems, so a multi-objective quantum-inspired seagull optimization algorithm based on decomposition (MOQSOA/D) is proposed. Multi-objective computing and quantum computing are introduced into MOQSOA/D. MOQSOA/D transforms the multi-objective problem into multiple scalar optimization sub-problems, and establishes a dynamic archive and a leadership archive at the same time. The Pareto solution of each sub-problem is stored in a dynamic archive, and the non-dominated Pareto solution is stored in the leader archive. While processing each sub-problem, each seagull is represented by a string of qubits, which is used to calculate the current seagull direction of flight, and a variable angular-distance rotation (VAR) gate is used to change the probability amplitude of the qubits, thereby updating the direction of flight. Penalty-based boundary intersection approach is introduced to determine whether the generated Pareto solution is retained. The proposed algorithm and six different algorithms were tested on 69 indicators, and the results show that the algorithm achieved better results in 40 indicators. In addition, a Unmanned Aerial Vehicle (UAV) path planning model in three-dimensional environment is designed to test the utility of MOQSOA/D, and the algorithm is compared with the other algorithm to demonstrate its effectiveness.

关键词： Linear programming Metaheuristics Benchmark testing Qubit Pareto optimization Heuristic algorithms Spirals Autonomous aerial vehicles seagull optimization algorithm multi-objective problem qubits encoding path planning

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Orthogonal Design-Based Control Vector Parameterization Combined With Improved seagull optimization algorithm for Dynamic optimization Problems

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IEEE ACCESS 2022年 10卷 65238-65256页

作者： Guo, Haidong Mo, Yuanbin Zhang, Yuedong Guangxi Minzu Univ Sch Artificial Intelligence Nanning 530006 Peoples R China Guangxi Key Lab Hybrid Computat & IC Design Anal Nanning 530006 Peoples R China Guangxi Minzu Univ Coll Elect Informat Nanning 530006 Peoples R China

This paper proposes an orthogonal design-based control vector parameterization (OCVP, for short) and a Gaussian distribution-based seagull optimization algorithm (GSOA) for dynamic optimization problems (DOPs). OCVP uses orthogonal experimental design to analyze the dynamic model to capture the fluctuation characteristics of the optimal control trajectory. Then using the ranges obtained in the orthogonal experiment to guide the construction of the time grid. Based on the seagull optimization algorithm (SOA), GSOA introduces the initialization idea based on Gaussian distribution and the dimension-order mutation operator based on Gaussian distribution. The initialization idea cleverly uses Gaussian distribution to generate the initial population that conforms to the chemical process. The mutation operator uses the dimension-order mutation method to improve the optimization performance of SOA. OCVP and GSOA are combined to form a new optimization method, named OCVP-GSOA. In the application of four typical chemical DOPs, the simulation results show that OCVP-GSOA can achieve similar or even higher solution accuracy. Furthermore, OCVP and control vector parameterization are compared, and GSOA and other meta-heuristic algorithms are compared. The results show that OCVP can achieve higher solution accuracy in most cases, and GSOA can achieve better performance.

关键词： optimization Chemicals Heuristic algorithms Mathematical models Process control Optimal control Gaussian distribution Dynamic optimization Gaussian distribution nonuniform control vector parameterization orthogonal design seagull optimization algorithm

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Improved seagull optimization algorithm using Levy flight and mutation operator for feature selection

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NEURAL COMPUTING & APPLICATIONS 2022年第10期34卷 7437-7472页

作者： Ewees, Ahmed A. Mostafa, Reham R. Ghoniem, Rania M. Gaheen, Marwa A. Univ Bisha Dept E Syst Bisha 61922 Saudi Arabia Damietta Univ Dept Comp Dumyat Egypt Mansoura Univ Fac Comp & Informat Sci Informat Syst Dept Mansoura 35516 Egypt Princess Nourah bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Technol Riyadh 84428 Saudi Arabia Mansoura Univ Dept Comp Mansoura 35516 Egypt

seagull optimization algorithm (SOA) is a recent bio-inspired technique utilized to improve the constrained large-scale problems in low computational cost and quick convergence speed. However, the globally optimized search space for the SOA is linear, which means that the SOA's global search capability could not be fully utilized. Thus, we propose an improved SOA algorithm (ISOA) using Levy flight and mutation operators. The ISOA obtains some Levy flight features, which improves the original SOA by performing large jumps, making the search escape from the local optima and begin at a different search space region. The mutation operator, which improves the exploration-exploitation trade-off, allows the catch of the optimal solution quickly and accurately. In order to examine the performance of the proposed ISOA approach, three experiments were conducted. The first one evaluates the ISOA in solving the global optimization problem. The second one is a comparative study based on twenty benchmark datasets to evaluate the general capability of ISOA in feature selection, compared to ten recent and well-established algorithms constructed using the other meta-heuristics methods. Furthermore, the third experiment is conducted using a real dataset with various face poses to investigate the efficiency of the ISOA in pose-variation recognition. Compared to the other meta-heuristics methods, the results show that the proposed model is more accurate and efficient in global optimization, feature selection purposes, and pose variation recognition. Furthermore, the ISOA approach outperforms the other methods proposed in the state-of-the-art literature.

关键词： seagull optimization algorithm Levy flight Mutation operator Feature selection

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Identification of oil authenticity and adulteration using deep long short-term memory-based neural network with seagull optimization algorithm

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NEURAL COMPUTING & APPLICATIONS 2022年第10期34卷 7611-7625页

作者： Surya, V Senthilselvi, A. SRM Inst Sci & Technol Dept Comp Sci Engn Chennai Tamil Nadu India

One of the most important aspects of people's everyday diet is edible oils. Good quality cooking oil plays a key role in one's health. Due to the increased demand for oil in both the international and domestic markets, vendors often mix the high-quality oil with low-quality ones causing adulteration which is a serious issue to be solved. Thus, qualified (authentic or pure) edible oils are expensive. Gall bladder cancer is mainly caused when the oil is adulterated with butter yellow, argemone oil, mixing good quality oil with low-quality oils, and wrong ingredients with fraudulent labeling. In the past decades, spectrophotometric methods and machine learning techniques are utilized for adulteration and authenticity identification of sunflower oil, olive oil, corn oil, coconut oil, mustard oil, soybean oils. Nevertheless, the performance of these methods is decreased due to data imbalance, overfitting, higher cost, more execution time, computational complexity, and inaccurate classification. To tackle these issues, we have proposed Deep Long Short-Term Memory (LSTM) neural network with a seagull optimization algorithm (SOA) for the authenticity and adulteration of edible oils classification. In this study, 5 kinds of edible oils such as coconut oil, rice oil, sesame oil, sunflower oil, and Olive oil are used. Each of the oil samples was kept in the refrigerator at 4 degrees C. During data acquisition, the proton resonance frequency was 19.91 MHz and the magnetic field strength was 0.467 T. The obtained signals are applied for edible oil classification, which is handled using a deep LSTM neural network with SOA. Based on the experimental investigation, the proposed method accomplished superior performances than existing methods including LFNMR-CNN, LFNMR-SVM, DLC, Pre-trained CNN.

关键词： Edible oils seagull optimization algorithm Deep LSTM neural network Authenticity Adulteration

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An enhanced seagull optimization algorithm for solving engineering optimization problems

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APPLIED INTELLIGENCE 2022年第11期52卷 13043-13081页

作者： Che, Yanhui He, Dengxu Guangxi Univ Nationalities Sch Math & Phys Nanning 530006 Peoples R China

The seagull optimization algorithm (SOA) is a recently proposed meta-heuristic optimization algorithm inspired by seagull foraging behavior. It has the advantages of simple structure and easy implementation. However, it also has some shortcomings, such as easily falling into local optimal and low convergence accuracy when solving complex engineering optimization problems. In this paper, to overcome the defects of the original SOA, an enhanced seagull optimization algorithm (ESOA) based on mutualism mechanism and commensalism mechanism is proposed. To evaluate the performance of the ESOA algorithm, the IEEE CEC2020 benchmark suite is utilized to verify the effectiveness of the ESOA algorithm, and the results are compared and analyzed with the latest meta-heuristic optimization algorithms. In addition, the ESOA algorithm is applied to twelve different types of engineering optimization problems, including pressure vessel design problem, multiple disc clutch brake design problem, three bar truss design problem, car crashworthiness problem, cantilever beam problem, abrasive water jet machine, gas transmission compressor design problem, hydro-static thrust bearing design problem, speed reducer problem, tubular column design problem, I beam design problem and industrial refrigeration system design problem. The convergence curves of ESOA and the comparison results of the latest metaheuristic algorithms are analyzed and compared with those reported in the latest literature. The results show that the ESOA algorithm is an optimization method that can find the optimal solution in engineering design problems, and has strong competitiveness compared with other algorithms.

关键词： seagull optimization algorithm Mutualism mechanism Commensalism mechanism Engineering optimization problems Metaheuristics

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Modified seagull optimization algorithm based MPPT for augmented performance of Photovoltaic solar energy systems

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AUTOMATIKA 2022年第1期63卷 1-15页

作者： Subramanian, Annapoorani Raman, Jayaparvathy Agni Coll Technol Dept Elect & Elect Engn Thalambur India Sri Sivasubramaniya Nadar Coll Engn Dept Elect & Commun Engn Kalavakkam India

The changing weather conditions and Partial Shading Situation (PSS) create numerous challenges in harvesting available maximum power from the solar Photovoltaic (PV) systems. The limitations of classical and bio-inspired optimization-based Maximum Power Point Tracking (MPPT) methods are incapable of extracting maximum power under PSS. Therefore, this paper presents a Modified seagull optimization algorithm (MSOA) based MPPT approach by incorporating Levy Flight Mechanism (LFM) and the formula for heat exchange in Thermal Exchange optimization (TEO) in the original seagull optimization algorithm (SOA) for accurate tracking of Global Maximum Power Point (GMPP) under transient and steady state operating conditions. The MSOA increases the capability of optimization in finding the optimal value of boost DC-DC converter's duty cycle, D, for operating at GMPP. The superiority of the presented MPPT approach is contrasted with SOA MPPT under uniform irradiation situation and partial shading situations using Matlab Simulink platform. With the presented MSOA MPPT, the settling time and percentage maximum overshoot are reduced by 0.92 times and 0.55 times in comparison to SOA MPPT with increased efficiency. The hardware results validated the simulation results proving the proposed MSOA MPPT as an efficient MPPT for solar PV systems.

关键词： Solar Photovoltaic systems maximum power point tracking seagull optimization algorithm partial shading condition DC-DC boost converter

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seagull optimization algorithm-Based Fractional-Order Fuzzy Controller for LFC of Multi-Area Diverse Source System With Realistic Constraints

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FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Naga Sai Kalyan, CH. Goud, B. Srikanth Reddy, Ch. Rami Udumula, Ramanjaneya Reddy Bajaj, Mohit Sharma, Naveen Kumar Elgamli, Elmazeg Shouran, Mokhtar Kamel, Salah Vasireddy Venkatadri Inst Technol Elect & Elect Engn Guntur India Anurag Univ Dept Elect & Elect Engn Ghatkesar India Malla Reddy Engn Coll Dept Elect & Elect Engn Secunderabad India SRM Univ Dept Elect & Elect Engn Amaravati India Natl Inst Technol Delhi Dept Elect & Elect Engn Delhi India IKG Punjab Tech Univ Elect Engn Dept Jalandhar India Cardiff Univ Wolfson Ctr Magnet Sch Engn Cardiff Wales Aswan Univ Fac Engn Elect Engn Dept Aswan Egypt

This study initiates the implementation of fractional-order (FO) fuzzy (F) PID (FOFPID) controller fine-tuned using a seagull optimization algorithm (SOA) for the study of load frequency control (LFC). Initially, the SOA-tuned FOFPID regulator is implemented on the widely utilized model of dual-area reheat-thermal system (DARTS), named test system-1 in this work for a perturbation of 10% step load (10% SLP) on area-1. Dynamical analysis of the DARTS system reveals the viability of the SOA-tuned FOFPID control scheme in regulating frequency deviations effectively compared to other control schemes covered in the literature. Later, the presented regulator is implemented on the multi-area diverse sources (MADS) system possessing realistic constraints in this study, termed test system-2. The sovereignty of the presented FOFPID controller is once again evidenced with controllers of PID/FOPID/FPID fine-tuned with the SOA approach. Moreover, the effect of considering practical realistic nonlinearity constraints such as communication time delays (CTDs) on MADS system performance is visualized and the necessity of its consideration is demonstrated. Furthermore, AC-DC lines are incorporated with the MADS system to enhance the performance under heavy-load disturbances and the robustness of the proposed regulatory mechanism is deliberated.

关键词： load frequency control seagull optimization algorithm FOFPID controller 10% SLP AC-DC lines

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seagull optimization algorithm for solving real-world design optimization problems

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MATERIALS TESTING 2020年第6期62卷 640-644页

作者： Panagant, Natee Pholdee, Nantiwat Bureerat, Sujin Yildiz, Ali Riza Sait, Sadiq M. Khon Kaen Univ Dept Mech Engn Khon Kaen Thailand Bursa Uludag Univ Dept Automot Engn Bursa Turkey KFUPM Comp Engn Dhahran Saudi Arabia KFUPM Ctr Commun & IT Res Dhahran Saudi Arabia

In this research paper, a new surrogate-assisted metaheuristic for shape optimization is proposed. A seagull optimization algorithm (SOA) is used to solve the shape optimization of a vehicle bracket. The design problem is to find structural shape while minimizing structural mass and meeting a stress constraint. Function evaluations are carried out using finite element analysis and estimated by using a Kriging model. The results show that SOA has outstanding features just as the whale optimization algorithm and salp swarm optimization algorithm for designing optimal components in the industry.

关键词： seagull optimization algorithm control arm shape optimization structural design

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optimization approach of berth-quay crane-truck allocation by the tide, environment and uncertainty factors based on chaos quantum adaptive seagull optimization algorithm

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APPLIED SOFT COMPUTING 2024年 152卷

作者： Li, Ming-Wei Xu, Rui-Zhe Yang, Zhong-Yi Hong, Wei-Chiang An, Xiao-Gang Yeh, Yi-Hsuan Harbin Engn Univ Coll Shipbuilding Engn Harbin 150001 Peoples R China Harbin Engn Univ Nanhai Inst Sanya 572024 Hainan Peoples R China Harbin Engn Univ Sch Econ & Management Harbin 150001 Peoples R China Asia Eastern Univ Sci & Technol Dept Informat Management New Taipei 22046 Taiwan Yuan Ze Univ Dept Informat Management Zhongli 320315 Taiwan China Waterborne Transport Res Inst Beijing 100088 Peoples R China

The post-epidemic era has led to the accumulation of cargo, which has brought greater pressure to container ports. Since traditional methods cannot simultaneously consider the effect of tidal, uncertain, and environmental factors on the allocation plan. To relieve this pressure, firstly, considering tidal factors, formulating time window rules, thinking out uncertain factors, and determining constraints from three perspectives of vessel berthing process, quay crane and container truck operation, a new berth-quay crane-truck joint scheduling model is constructed by minimizing three aspects of vessels turnaround time, the carbon emissions of quay cranes and trucks, namely TEU-BQCT model. Then, aiming at obtaining a relatively high-quality solution, combining chaotic mapping and quantum entanglement, a new chaotic quantum adaptive seagull optimization algorithm is proposed, namely CQASOA, exclusive coding rules suitable for the TEU-BQCT model is formulated, a feasible integer algorithm is developed, the external penalty function is constructed to limit constraints, and a novel joint scheduling solution method of berth-quay crane-truck is proposed, namely TEU-BQCT_CQASOA. Subsequently, two ports of different scales in South China are used to test the constructed solution method feasibility. The simulation results indicate that the constructed TEU-BQCT model can obtain a more suitable scheduling scheme. The proposed CQASOA has better performance than other comparison algorithms selected in this paper, which can obtain a better solution when solving the TEU-BQCT model.

关键词： Berth-quay crane-truck joint scheduling Tidal factor Uncertainty factor seagull optimization algorithm External penalty function method Quantum computing Chaotic mapping

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