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EMoSOA: a new evolutionary multi-objective seagull optimization algorithm for global optimization

INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS 2021年第2期12卷 571-596页

作者： Dhiman, Gaurav Singh, Krishna Kant Slowik, Adam Chang, Victor Yildiz, Ali Riza Kaur, Amandeep Garg, Meenakshi Govt Bikram Coll Commerce Dept Comp Sci Patiala 147001 Punjab India Delhi NCR Dept Elect & Commun Engn KIET Grp Inst Ghaziabad India Koszalin Univ Technol Dept Elect & Comp Sci Sniadeckich 2 PL-75453 Koszalin Poland Teesside Univ Sch Comp Engn & Digital Technol Middlesbrough Cleveland England Uludag Univ Dept Automot Engn Coll Engn TR-16059 Bursa Turkey Sri Guru Granth Sahib World Univ Dept Comp Sci & Engn Fatehgarh Sahib Punjab India

This study introduces the evolutionary multi-objective version of seagull optimization algorithm (SOA), entitled Evolutionary Multi-objective seagull optimization algorithm (EMoSOA). In this algorithm, a dynamic archive concept, grid mechanism, leader selection, and genetic operators are employed with the capability to cache the solutions from the non-dominatedPareto. The roulette-wheel method is employed to find the appropriate archived solutions. The proposed algorithm is tested and compared with state-of-the-art metaheuristic algorithms over twenty-four standard benchmark test functions. Four real-world engineering design problems are validated using proposedEMoSOAalgorithm to determine its adequacy. The findings of empirical research indicate that the proposed algorithm is better than other algorithms. It also takes into account those optimal solutions from theParetowhich shows high convergence.

关键词： seagull optimization algorithm Multi-objective optimization Evolutionary Pareto Engineering Design Problems Convergence Diversity

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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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Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm

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ENERGY REPORTS 2019年 5卷 1616-1625页

作者： Cao, Yan Li, Yiqing Zhang, Geng Jermsittiparsert, Kittisak Razmjooy, Navid Xian Technol Univ Sch Mechatron Engn Xian 710021 Shaanxi Peoples R China Chulalongkorn Univ Social Res Inst Bangkok 10330 Thailand Tsfresh Univ Dept Elect Engn Tafresh Iran

This study proposes an optimal model to design and simulate the proton exchange membrane fuel cell (PEMFC) systems. The purpose of this paper is to present an improved version of seagull optimization algorithm for optimal parameter identification of the PEMFC stacks. The new algorithm uses the Levy flight mechanism to give faster convergence rates. The sum of the squared error between the empirical values and achieved optimal model is analyzed based on two empirical PEMFC models including BCS 500-W and NedStack PS6. This analysis is performed to show the potential of the presented method by considering different conditions. Simulation results are compared with several optimization algorithms and show the algorithm's superiority in terms of the solutions quality and the convergence speed. (C) 2019 The Authors. Published by Elsevier Ltd.

关键词： seagull optimization algorithm Levy flight Proton exchange membrane fuel cells Modeling

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A New Hybrid seagull optimization algorithm for Feature Selection

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IEEE ACCESS 2019年 7卷 49614-49631页

作者： Jia, Heming Xing, Zhikai Song, Wenlong Northeast Forestry Univ Coll Mech & Elect Engn Harbin 150040 Heilongjiang Peoples R China

Hybrid algorithms have attracted more and more attention in the field of optimization algorithms. In this paper, three hybrid algorithms are proposed to solve feature selection problems based on seagull optimization algorithm (SOA) and thermal exchange optimization (TEO). In the first algorithm, we take the roulette wheel to choose one of the two algorithms for located updating. Another method is to join the TEO algorithm for optimization after SOA algorithm iteration. The last method is to adopt TEO algorithm's heat exchange formula to improve the seagull attack mode of SOA algorithm, so as to improve the exploitation ability of SOA algorithm. The performance of proposed methods is evaluated on 20 standard benchmark datasets in the UCI repository and compared with three well-known hybrid optimization feature selection methods in the literature. The experimental results illustrate that the proposed algorithm has high efficiency in improving classification accuracy, ensuring the ability of hybrid SOA algorithm in feature selection and classification task information attribute selection, and reducing the CPU time.

关键词： Feature selection hybrid optimization seagull optimization algorithm thermal exchange optimization

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Optimal operation of multi-reservoir systems for increasing power generation using a seagull optimization algorithm and heading policy

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ENERGY REPORTS 2021年 7卷 3703-3725页

作者： Ehteram, Mohammad Banadkooki, Fatemeh Barzegari Fai, Chow Ming Moslemzadeh, Mohsen Sapitang, Michelle Ahmed, Ali Najah Irwan, Dani El-Shafie, Ahmed Semnan Univ Fac Civil Engn Dept Water Engn & Hydraul Struct Semnan Iran Payame Noor Univ Agr Dept Tehran Iran Univ Tenaga Nasl UNITEN Coll Engn Dept Civil Engn Kajang 43000 Selangor Malaysia Monash Univ Malaysia Sch Engn Discipline Civil Engn Jalan Lagoon Selatan Bandar Sunway 47500 Selangor Malaysia Univ Tenaga Nasl UNITEN Coll Engn Inst Energy Infrastruct IEI Dept Civil Engn Kajang 43000 Selangor Malaysia Int Islamic Univ Malaysia Civil Engn Dept Kulliyyah Engn Kuala Lumpur Malaysia Univ Malaya UM Fac Engn Dept Civil Engn Kuala Lumpur 50603 Malaysia United Arab Emirates Univ Natl Water & Energy Ctr POB 15551 Al Ain U Arab Emirates

Power supply is a key issue for decision-makers. The reservoir operation of multi-reservoir systems is an important aspect to consider in efforts to increase power generation. This research studies a multi reservoir system comprising of the Khersan-I (KHI), Karoon-III (KAIII) and Karoon-IV (KAIV) with the intent being to increase power generation. To achieve this, the Two-Point Heading Rule was integrated with a new optimization algorithm, namely the seagull optimization algorithm (SEOA). The Two Point Heading Rule was used based on four distinct scenarios, namely Two-Point Heading Rule (1), Two-Point Heading Rule (2), Two-Point Heading Rule (3) and Two-Point Heading Rule (4). The seagull optimization algorithm was then used to find two heading parameters of the TPHRs. The seagull optimization algorithm was subsequently benchmarked against the Salp Swarm algorithm (SSA), Bat algorithm (BA) and the Shark optimization algorithm (SOA). Various inflow scenarios consisting of the first inflow scenario (dry condition), the second inflow scenario (normal) and the third inflow scenario (wet condition) were considered for the optimal operation of this multi-reservoir system. The results indicated that the global solution of the MSOO based on NLP for Two-Point Heading Rule (1) under the first inflow scenario and was 3.22 while the average solution of seagull optimization algorithm, Salp Swarm algorithm, Shark optimization algorithm, and Bat algorithm in respective order was 3.25, 3.93, 4.87 and 6.03. The results indicated that the global solution of the MSOO based on NLP for Two-Point Heading Rule (1) under the second inflow scenario was 2.14 while the average best solution of seagull optimization algorithm, Salp Swarm algorithm, Shark optimization algorithm, and Bat algorithm in respective order was 2.16, 2.98, 3.96, and 4.89. It can be concluded that the SEOA outperformed all of the other algorithms. It was also found that the SEOA based on the Two-Point Heading Rule (3) un

关键词： seagull optimization algorithm Multi-reservoir operation Heading policy Power generation

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Simultaneous feeder reconfiguration, DSTATCOM allocation, and sizing using seagull optimization algorithm in unbalanced radial distribution systems

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SOFT COMPUTING 2023年第7-8期28卷 6403页

作者： Samal, Padarbinda Panigrahy, Damodar KIIT Deemed Be Univ Dept Elect Engn Bhubaneswar 751024 Orissa India SRM Inst Sci & Technol Dept Elect & Commun Engn Kattankulathur Tamil Nadu India

This paper introduces a planning mechanism for optimal feeder reconfiguration, DSTATCOM allocation, and sizing in unbalanced radial distribution systems. The minimization of total active power losses (TACPL) is considered as the main objective of this work. A three phase forward-backward sweep load flow algorithm is utilized as a subroutine for the computation of the TACPL. The seagull optimization algorithm (SOA) is used as the solution methodology for this mixed integer non-linear planning problem. The simultaneous feeder reconfiguration, DSTATCOM installation obtains significant reduction in in the 19- and 25- bus unbalanced distribution network. Furthermore, the bus voltage magnitudes (pu) is also found to be enhanced. The SOA obtains lower TACPL in comparison to some of the well-known optimization algorithms like, differential evolution algorithm (DEA), moth-flame optimization (MFO) algorithm, and Jaya optimization algorithm (JOA). The proposed technique also attains lower TACPL in comparison to some of the established techniques reported in the literature while determining the optimal location, and rating of the DSTATCOM considering TACPL minimization.

关键词： Unbalanced radial distribution systems Three phase load flow DSTATCOM Feeder reconfiguration 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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An enhanced hybrid seagull optimization algorithm with its application in engineering optimization

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ENGINEERING WITH COMPUTERS 2023年第2期39卷 1653-1696页

作者： Hu, Gang Wang, Jiao Li, Yan Yang, MingShun Zheng, Jiaoyue Xian Univ Technol Sch Mech & Precis Instrument Engn 5 South Jinhua Rd Xian 710048 Peoples R China Xian Univ Technol Dept Appl Math Xian 710054 Peoples R China

Aiming at the problems such as slow search speed, low optimization accuracy, and premature convergence of standard seagull optimization algorithm, an enhanced hybrid strategy seagull optimization algorithm was proposed. First, chaos mapping is used to generate the initial population to increase the diversity of the population, which lays the foundation for the global search. Then, a nonlinear convergence parameter and inertia weight are introduced to improve the convergence factor and to balance the global exploration and local development of the algorithm, so as to accelerate the convergence speed. Finally, an imitation crossover mutation strategy is introduced to avoid premature convergence of the algorithm. Comparison and verification between MSSOA and its incomplete algorithms are better than SOA, indicating that each improvement is effective and its incomplete algorithms all improve SOA to different degrees in both exploration and exploitation. 25 classic functions and the CEC2014 benchmark functions were tested, and compared with seven well-known meta-heuristic algorithms and its improved algorithm to evaluate the validity of the algorithm. The algorithm can explore different regions of the search space, avoid local optimum and converge to global optimum. Compared with other algorithms, the results of non-parametric statistical analysis and performance index show that the enhanced algorithm in this paper has better comprehensive optimization performance, significantly improves the search speed and convergence precision, and has strong ability to get rid of the local optimal solution. At the same time, in order to prove its applicability and feasibility, it is used to solve two constrained mechanical engineering design problems contain the interpolation curve engineering design and the aircraft wing design. The engineering curve shape with minimum energy, minimum curvature, and the smoother shape of airfoil with low drag are obtained. It is proved that enhanced

关键词： seagull optimization algorithm Chaotic mapping Nonlinear strategy Inertial weight Variation operation Imitation quantum crossover

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