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IEEE ACCESS 2022年 10卷 46690-46696页

作者： Su, Mu-Chun Chen, Jieh-Haur Utami, Andina Mugi Lin, Shih-Chieh Wei, Hsi-Hsien Natl Cent Univ Dept Comp Sci & Informat Engn Taoyuan Taiwan Natl Cent Univ Dept Civil Engn Taoyuan Taiwan Natl Cent Univ Res Ctr Smart Construct Taoyuan 320317 Taiwan Hong Kong Polytech Univ Dept Bldg & Real Estate Hong Kong Peoples R China

Popular methods to deal with computation become strenuous due to the optimization demands that develop more complex nowadays. This research aims to propose a new optimal algorithm, Dove Swarm optimization (DSO), that adopts the foraging behaviors of doves to have six benchmark functions expressing DSO performance. By considering competition for forage, DSO is designed to ensure the most satisfied dove as well as optimization, then compared with 15 popular optimization algorithms using random initial and lattice initial values. The results reveal that DSO performs the best in time efficiency and well in both convergences for these functions in a reasonable region from 1 to 3 seconds, and population diversity for the initialization method from less than 1 second to 9 seconds dependent on the population size. As a result, DSO is indeed a time-efficient and effective algorithm in solving optimization problems.

关键词： optimization Particle swarm optimization Genetic algorithms Birds Neurons Statistics Sociology Swarm intelligence optimization algorithm computational intelligence

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An improved grasshopper optimization algorithm based on dynamic dual elite learning and sinusoidal mutation

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COMPUTING 2022年第5期104卷 981-1015页

作者： Chen, Lei Tian, Yu Ma, Yunpeng Tianjin Univ Commerce Sch Informat Engn Tianjin Peoples R China Tianjin Univ Commerce Sch Sci Tianjin Peoples R China

Grasshopper optimization algorithm (GOA) is a meta-heuristic algorithm for solving optimization problems by modeling the biological habit and social behavior of grasshopper swarms in nature. Compared with other optimization algorithms, GOA still has room to improve its performance on solving complex problems. Therefore, this paper proposes an improved grasshopper optimization algorithm (EMGOA) based on dynamic dual elite learning and sinusoidal mutation. First of all, dynamic elite learning strategy is adopted to improve the influence of elites on the update process, enabling the algorithm to have a faster convergence speed. Then, sinusoidal function is utilized to guide the mutation of the current global optimal individual during each iteration to avoid the algorithm falling into the local optimum and improve the convergence accuracy of the algorithm. In order to investigate the performance of the proposed EMGOA algorithm, experiments are conducted on 26 benchmark functions and CEC2019 in this paper. The experimental results show that the optimization performance of EMGOA is obviously better than GOA, and EMGOA is competitive with six state-of-the-art meta-heuristic optimization algorithms.

关键词： Grasshopper optimization algorithm Dynamic elite learning Sinusoidal mutation optimization algorithm Meta-heuristic algorithm

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Efficient and merged biogeography-based optimization algorithm for global optimization problems

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SOFT COMPUTING 2019年第12期23卷 4483-4502页

作者： Zhang, Xinming Kang, Qiang Tu, Qiang Cheng, Jinfeng Wang, Xia Henan Normal Univ Coll Comp & Informat Engn Xinxiang Peoples R China Engn Technol Res Ctr Comp Intelligence & Data Min Xinxiang Peoples R China

In order to improve the optimization efficiency of the biogeography-based optimization (BBO) algorithm, this study proposes a novel BBO algorithm, namely an efficient and merged biogeography-based optimization (EMBBO) algorithm. Firstly, BBO's mutation operator is got rid of. Then, a differential mutation operator and a sharing operator are merged into BBO's migration operator to obtain an improved migration operator. In the improved migration operator, the emigration habitats are selected by a new example learning approach. The above improvements can enhance the optimization performance and reduce the computation complexity. Thirdly, a new single-dimensional and all-dimensional alternating strategy is combined with the improved migration operator to balance exploration and exploitation and reduce more computation complexity. Fourthly, the opposition-based learning approach is merged to prevent the algorithm from falling into the local optima. Finally, the greedy selection method is used instead of the elitist strategy to avoid setting the elitist parameter and to get rid of one sorting step. We make a large number of experiments on a set of classic benchmark functions and CEC2017 test set and apply EMBBO to clustering optimization. Experiment results verify that EMBBO can obtain the highest optimization efficiency compared with quite a few state-of-the-art algorithms.

关键词： optimization algorithm Evolutionary algorithm Biogeography-based optimization Differential mutation operator Example learning approach

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Jaya optimization algorithm for transient response and stability enhancement of a fractional-order PID based automatic voltage regulator system

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ALEXANDRIA ENGINEERING JOURNAL 2020年第4期59卷 2429-2440页

作者： Jumani, Touqeer Ahmed Mustafa, Mohd Wazir Hussain, Zohaib Rasid, Madihah Md Saeed, Muhammad Salman Memon, Mehran M. Khan, Ilyas Nisar, Kottakkaran Sooppy Univ Teknol Malaysia Sch Elect Engn Skudai Johor Bahru Malaysia Mehran Univ Engn & Technol Dept Elect Engn SZAB Campus Khairpur Mirs Pakistan Mehran Univ Engn & Technol Dept Elect Engn Jamshoro Pakistan Multan Elect Power Co Multan Pakistan Ton Duc Thang Univ Fac Math & Stat Ho Chi Minh City 72915 Vietnam Prince Sattam bin Abdulaziz Univ Coll Arts & Sci Dept Math Wadi Aldawaser 11991 Saudi Arabia

Considering the higher flexibility in tuning process and finer control action of the fractional-order proportional integral derivative (FOPID) controller over the conventional propor-tional integral derivative (PID) controller, this paper explores its application in the automatic volt-age regulator system. FOPID contains five tuning parameters as compared to three in the conventional PID controller. The additional tuning knobs in FOPID provide increased control flex-ibility and precise control action, however, their inclusion makes the tuning process more complex and tedious. Thus, the intelligence of an artificial intelligence (AI) technique called jaya optimiza-tion algorithm (JOA) is utilized in order to obtain an optimal combination of FOPID gains which further led to the optimal transient response and improved stability of the considered AVR system. To validate the performance superiority of the proposed approach its corresponding system's dynamic response is compared with that of the other well-known AI-based approaches explored in recent literature. Furthermore, the stability study of the proposed AVR system is carried out by evaluating its pole/zero and bode maps. Finally, the robustness of the proposed optimized AVR system against the system's parameter variation is evaluated by varying the time constants of all the four components of AVR (generator, exciter, amplifier and sensor) from-50% to +50% independently. The proposed algorithm based FOPID tuning technique provides 59.82%, 56.09%, 14.94%, 34.24%, 35.70%, 21.64%, 12.0%, 41.33%, 14.84% and 15.17% reduced over-shoot than that of differential evolution (DE), particle swarm optimization (PSO), Artificial Bee Colony (ABC), Bibliography Based optimization (BBO), Grasshopper optimization algorithm (GOA), Pattern Search algorithm (PSA), Improved Kidney Inspired algorithm (IKA), Whale optimization algorithm (WOA), Salp Swarm algorithm (SSA) and Local Unimodal Sampling (LUS) algorithm respectively, thus validat

关键词： optimization algorithm Transient response FOPID AVR JOA

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optimization of Self-Learning in Computer Engineering Courses: An Intelligent Software System Supported by Artificial Neural Network and Vortex optimization algorithm

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COMPUTER APPLICATIONS IN ENGINEERING EDUCATION 2017年第1期25卷 142-156页

作者： Kose, Utku Arslan, Ahmet Usak Univ Comp Sci Applicat & Res Ctr Usak Turkey Konya Food & Agr Univ Dept Comp Engn Konya Turkey

Self-learning process is an important factor that enables learners to improve their own educational experiences when they are away of face-to-face interactions with the teacher. A well-designed self-learning activity process supports both learners and teachers to achieve educational objectives rapidly. Because of this, there has always been a remarkable trend on developing alternative self-learning approaches. In this context, this study is based on two essential objectives. Firstly, it aims to introduce an intelligent software system, which optimizes and improves computer engineering students' self-learning processes. Secondly, it aims to improve computer engineering students' self-learning during the courses. As general, the software system introduced here evaluates students' intelligence levels according to the Theory of Multiple Intelligences and supports their learning via accurately chosen materials provided over the software interface. The evaluation mechanism of the system is based on a hybrid Artificial Intelligence approach formed by an Artificial Neural Network, and an optimization algorithm called as Vortex optimization algorithm (VOA). The system is usable for especially technical courses taught at computer engineering departments of universities and makes it easier to teach abstract subjects. For having idea about success of the system, it has been tested with students and positive results on optimizing and improving self-learning have been obtained. Additionally, also a technical evaluation has been done previously, in order to see if the VOA is a good choice to be used in the system. It can be said that the whole obtained results encourage the authors to continue to future works. (C) 2017 Wiley Periodicals, Inc.

关键词： self-learning theory of multiple intelligences artificial neural networks vortex optimization algorithm computer engineering

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Research and application of an innovative combined model based on a modified optimization algorithm for wind speed forecasting

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MEASUREMENT 2018年 124卷 395-412页

作者： Jiang, Ping Li, Chen Dongbei Univ Finance & Econ Sch Stat Jianshan Rd Dalian 116025 Liaoning Peoples R China

Wind energy is emerging as a promising substitute for conventional energy and plays a pivotal role in the power industry. For wind speed forecasting, many challenges have exposed due to its fluctuation and intermittence. To address these difficulties, different models have been adopted to various wind speed time series in previous studies. However, few methodologies have focused on the importance of model parameter optimization or data pre-processing, resulting in undesirable forecasting performance. In this study, an innovative combined model that combines data pre-processing, modified optimization algorithms, three neural networks and an effective deciding weight method is proposed for short-term wind speed forecasting. To improve the forecasting capacity of the combined model, a modified optimization algorithm is proposed and employed to determine the parameters of the single models. Furthermore, a deciding weight method based on multivariate statistical estimation is applied for weight optimization. Additionally, ten-minute wind speed data from a wind farm in Penglai, China, are selected for multi-step ahead forecasting. The results obtained confirmed an adequate approximation of the actual wind speed series and a significant improvement of the forecasting accuracy of the proposed model.

关键词： Wind speed forecasting optimization algorithm Multi-step ahead forecasting Combined model Weight coefficient optimization

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An optimization algorithm for solving the rich vehicle routing problem based on Variable Neighborhood Search and Tabu Search metaheuristics

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JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2016年 291卷 468-477页

作者： Antonio Sicilia, Juan Quemada, Carlos Royo, Beatriz Escuin, David Univ Int La Rioja Escuela Ingn Logrono 26002 Spain Virginia Polytech Inst & State Univ Blacksburg VA USA Univ Zaragoza Dept Mech Engn Zaragoza 50018 Spain Inst Tecnol Aragon Zaragoza 50018 Spain

This paper presents a novel optimization algorithm that consists of metaheuristic processes to solve the problem of the capillary distribution of goods in major urban areas taking into consideration the features encountered in real life: time windows, capacity constraints, compatibility between orders and vehicles, maximum number of orders per vehicle, orders that depend on the pickup and delivery and not returning to the depot. With the intention of reducing the wide variety of constraints and complexities, known as the Rich Vehicle Routing Problem, this algorithm proposes feasible alternatives in order to achieve the main objective of this research work: the reduction of costs by minimizing distances and reducing the number of vehicles used as long as the service quality to customers is optimum and a load balance among vehicles is maintained. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Rich vehicle routing problem optimization algorithm Metaheuristic methods Load balance

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Sensing Cloud Computing in Internet of Things: A Novel Data Scheduling optimization algorithm

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IEEE ACCESS 2020年 8卷 42141-42153页

作者： Sun, Zeyu Lv, Zhiguo Wang, Huihui Li, Zhixian Jia, Fuqian Lai, Chunxiao Luoyang Inst Sci & Technol Sch Comp Sci & Informat Engn Luoyang 471023 Peoples R China Henan Inst Sci & Technol Sch Informat Engn Xinxiang 453003 Henan Peoples R China Jacksonville Univ Dept Engn Jacksonville FL 32211 USA

In order to addressing the issues of data matching deviation and load imbalance during the data scheduling process of the Internet of Things, Sensing Cloud Computing in IoT: A Novel Data Scheduling optimization (SCC-DSO) algorithm is proposed in this paper. First, according to the processing capacity of the IoT working node, a data placement algorithm is designed to reasonably place the input data of the job. Second, the data scheduling queue is optimized based on the data block storage location information to reduce non-local execution of data scheduling. Furthermore, a data prefetching method is designed to pull the data required for non-local data scheduling in advance, and shorten the waiting time of the task for input data. Finally, simulation experiment evaluated by the task localization execution rate and response time is performed. The effectiveness and stability of the algorithm is verified compared with other algorithms.

关键词： Task analysis Predictive models Processor scheduling Internet of Things Data models Load modeling Energy consumption Sensing-cloud computing Internet of Things data scheduling optimization algorithm

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Modeling Magnetic Emissions Combining Image Processing and an optimization algorithm

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IEEE TRANSACTIONS ON ELECTROMAGNETIC COMPATIBILITY 2009年第4期51卷 909-918页

作者： Vives-Gilabert, Yolanda Arcambal, Christian Louis, Anne Eudeline, Philippe Mazari, Belahcene Port Informacio Cient Bellaterra 08193 Spain CEA Saclay IRFU SAP F-91191 Gif Sur Yvette France Ecole Super Ingenieurs F-76801 St Etienne France Inst Rech Syst Elect Embarques F-76801 St Etienne France Thales Air Syst F-76520 Ymare France Ecole Super Ingenieurs Genie Elect F-76800 Rouen France

An automated procedure for modeling the radiated emissions of electronic devices is presented in this paper. The principle of the model is to replace the device by a set of electric dipoles defined by their positions, orientations, lengths, and currents. Thus, this modeling procedure aims to determine the number of dipoles and their parameters with a minimum user intervention. The procedure is divided into three parts: the near-field measurements of the tangential components of the magnetic field, an image processing to obtain the number of sources of the model, and the application of an optimization algorithm in order to obtain the different parameters of the model, such as the current and the position of each source.

关键词： Electromagnetic measurements image processing modeling emissions near-field scan optimization algorithm

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Mass Ionized Particle optimization algorithm Applied to Optimal FEA-Based Design of Electric Machine

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IEEE TRANSACTIONS ON MAGNETICS 2016年第3期52卷 8200404-1-8200404-4页

作者： Han, Wonseok Tran, Trung Tin Kim, Jong-Wook Kim, Yong-Jae Jung, Sang-Yong Sungkyunkwan Univ Sch Elect & Engn Suwon 440746 South Korea Dong A Univ Dept Elect Engn Busan 604714 South Korea Chosun Univ Dept Elect Engn Kwangju 501759 South Korea

A finite-element analysis-based optimal design of an electric machine takes considerable time for its objective evaluation and has many local minima. Thus, selecting an appropriate global convergence optimization with fast convergence speed is necessary in the optimal design of an electric machine. In this paper, a novel global search optimization algorithm, mass ionized particle optimization (MIPO), is newly proposed. The MIPO is the population-based algorithm, which reflects the interactive force between the ionized particles. The global convergence and the convergence speed are validated by comparison with the particle swarm optimization, which have already been proved for its global convergence when applied to a well-known Goldstein-Price function as a benchmark function. In addition, the algorithm has been applied to the optimal design of an interior permanent magnet synchronous machine aiming for its torque ripple reduction.

关键词： Electric machine finite-element analysis (FEA) optimization algorithm population-based algorithm

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