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A New Hybrid Metaheuristic algorithm for Wind Farm Micrositing

MEHRAN UNIVERSITY RESEARCH JOURNAL OF ENGINEERING AND TECHNOLOGY 2017年第3期36卷 635-648页

作者： Massan, Shafiq-ur-Rehman Wagan, Asim Imdad Shaikh, Muhammad Mujtaba ShaheedZulfiqar Ali Bhutto Inst Sci & Technol Karachi Pakistan Mohammad Ali Jinnah Univ Karachi Pakistan Mehran Univ Engn & Technol Dept Basic Sci & Related Studies Jamshoro Pakistan

This work focuses on proposing a new algorithm, referred as HMA (Hybrid Metaheuristic algorithm) for the solution of the WTO (Wind Turbine Optimization) problem. It is well documented that turbines located behind one another face a power loss due to the obstruction of the wind due to wake loss. It is required to reduce this wake loss by the effective placement of turbines using a new HMA. This HMA is derived from the two basic algorithms i.e. DEA (differential evolution algorithm) and the FA (Firefly algorithm). The function of optimization is undertaken on the N.O. Jensen model. The blending of DEA and FA into HMA are discussed and the new algorithm HMA is implemented maximize power and minimize the cost in a WTO problem. The results by HMA have been compared with GA (Genetic algorithm) used in some previous studies. The successfully calculated total power produced and cost per unit turbine for a wind farm by using HMA and its comparison with past approaches using single algorithms have shown that there is a significant advantage of using the HMA as compared to the use of single algorithms. The first time implementation of a new algorithm by blending two single algorithms is a significant step towards learning the behavior of algorithms and their added advantages by using them together.

关键词： Wind Farm Jensen Wake Model Nature Inspired algorithms differential evolution algorithm Firefly algorithm Genetic algorithm Hybrid Meta-Heuristic algorithm

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A comparative study of different optimization algorithms for the optimum operation of the Mahabad dam reservoir

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RESULTS IN ENGINEERING 2024年 21卷

作者： Feizi, Hajar Sattari, Mohammad Taghi Apaydin, Halit Univ Tabriz Fac Agr Dept Water Engn Tabriz 51666 Iran Ankara Univ Fac Agr Dept Agr Engn TR-06110 Ankara Turkiye

In this study, the performance of the differential evolution (DE) algorithm, the Genetic algorithm (GA) and Teaching-Learning Based Optimization (TLBO) algorithm are compared for the optimum operation of the Mahabad dam reservoir. The Reservoir supplies agricultural, environmental, municipal and industrial water demands of the area. The desktop reserve model is used for estimating the minimum environmental demand. Six alternative agricultural water management scenarios are proposed and a sensitivity analysis is performed on the agricultural demands under different scenarios. The results of the study indicate that both DE and GA algorithms performed nearly equally;however, the DE algorithm reached its result more quickly with the reliability of 74.19 % compared to the results of the GA and TLBO. A 30 % decrease in agricultural demand in the fifth scenario with 87.56 % reliability provides the best results.

关键词： Optimal operation differential evolution algorithm Genetic algorithm Teaching-learning based optimization algorithm Environmental flow

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Designing safety-oriented tourist routes for heterogeneous tourist groups using an artificial multi-intelligence system

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JOURNAL OF INDUSTRIAL AND PRODUCTION ENGINEERING 2023年第7期40卷 589-609页

作者： Pitakaso, Rapeepan Nanthasamroeng, Natthapong Gonwirat, Sarayut Khonjun, Surajet Kaewta, Chutchai Srichok, Thanatkit Dinkoksung, Sairoong Ubon Ratchathani Univ Dept Ind Engn Ubon Ratchathani Thailand Ubon Ratchathani Rajabhat Univ Dept Engn Technol Ubon Ratchathani Thailand Kalasin Univ Dept Comp Engn & Automat Kalasin Thailand Ubon Ratchathani Rajabhat Univ Dept Comp Sci Ubon Ratchathani Thailand Ubon Ratchathani Univ Dept Hotel Management Ubon Ratchathani Thailand

The prior research on tourist route design for heterogeneous groups primarily focused on total tourist preferences and individual fairness. In this study, we present an innovative approach by incorporating safety criteria into the optimal tourist route, an aspect that has been previously overlooked. Ensuring tourist safety during travel and attractions is of utmost importance for creating a secure and enjoyable experience. Our proposed safety-focused multi-objective tourist trip design, coupled with the modified artificial multiple-intelligence system, underwent testing in a case study involving 50 attractions and 44 tourist groups (comprising a total of 140 members). The computational results demonstrate that our approach outperforms existing methods by 16.16% to 36.26%, effectively reducing total traveling and attraction visiting risk by 26.1% compared to conventional techniques proposed in the literature. Moreover, our approach enhances the total preferences and tourist fairness by 5.83% and 25.57%, respectively.

关键词： Artificial multiple intelligence system (AMIS) tourist trip design problem tourist recommendation system differential evolution algorithm genetic algorithm >

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Radial Basis Function Neural Network in Vibration Control of Civil Engineering Structure

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年第1期8卷 347-356页

作者： Lu, Jing Chen, Qinyuan Mohamed, Hamdy Anhui Open Univ Sch Informat & Architectural Engn Hefei 230022 Peoples R China Appl Sci Univ Coll Engn Eker Bahrain

This article uses the radial basis function artificial neural network and the MATLAB toolbox to study the vibration control of civil engineering structures. The article proposes a dynamic structure design method based on a generalized radial basis function neural network. Furtheunore, the RBF neural network theory is used to optimize the structure-related controller parameters in geotechnical engineering. The research results show that RBF neural network can more accurately predict the vibration response of civil engineering. It can effectively solve the time lag problem in vibration control.

关键词： Radial basis function Neural network Lyapunov stability theory Civil engineering Vibration control differential evolution algorithm

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Dynamic Robust Particle Swarm Optimization algorithm Based on Hybrid Strategy

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2023年第1期14卷 1页

作者： Zeng, Jian Yu, Xiaoyong Yang, Guoyan Gui, Haitao Guilin Power Supply Bur Guangxi Power Grid Co Guangdong Peoples R China Elect Power Sci Res Inst Guangxi Power Grid Co Nanning Peoples R China

Robust optimization over time can effectively solve the problem of frequent solution switching in dynamic environments. In order to improve the search performance of dynamic robust optimization algorithm, a dynamic robust particle swarm optimization algorithm based on hybrid strategy (HS-DRPSO) is proposed in this paper. Based on the particle swarm optimization, the HS-DRPSO combines differential evolution algorithm and brainstorms an optimization algorithm to improve the search ability. Moreover, a dynamic selection strategy is employed to realize the selection of different search methods in the proposed algorithm. Compared with the other two dynamic robust optimization algorithms on five dynamic standard test functions, the results show that the overall performance of the proposed algorithm is better than other comparison algorithms.

关键词： Brain Storm Optimization differential evolution algorithm Dynamic Robust Optimization Particle Swarm Optimization

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A hyper-heuristic based reinforcement-learning algorithm to train feedforward neural networks

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ENGINEERING SCIENCE AND TECHNOLOGY-AN INTERNATIONAL JOURNAL-JESTECH 2022年 35卷

作者： Ozsoydan, Fehmi Burcin Golcuk, Lker Dokuz Eylul Univ Fac Engn Dept Ind Engn TR-35397 Izmir Turkey Izmir Bakircay Univ Dept Ind Engn TR-35665 Izmir Turkey

Artificial Neural Networks (ANNs) offer unique opportunities in numerous research fields. Due to their remarkable generalization capabilities, they have grabbed attention in solving challenging problems such as classification, function approximation, pattern recognition and image processing that can be quite complex to model mathematically in practice. One of the most vital issues regarding the ANNs is the training process. The aim at this stage is to find the optimum values of ANN parameters such as weights and biases, which indeed embed the whole information of the network. Traditional gradient-descent -based training methods include various algorithms, of which the backpropagation is one of the best-known. Such methods have been shown to exhibit outstanding results, however, they are known have two major theoretical and computational limitations, which are slow convergence speed and possible local minima issues. For this purpose, numerous stochastic search algorithms and heuristic methods have been individually used to train ANNs. However, methods, bringing diverse features of different optimiz-ers together are still lacking in the related literature. In this regard, this paper aims to develop a training algorithm operating based on a hyper-heuristic (HH) framework, which indeed resembles reinforcement learning-based machine learning algorithm. The proposed method is used to train Feed-forward Neural Networks, which are specific forms of ANNs. The proposed HH employs individual metaheuristic algo-rithms such as Particle Swarm Optimization (PSO), differential evolution (DE) algorithm and Flower Pollination algorithm (FPA) as low-level heuristics. Based on a feedback mechanism, the proposed HH learns throughout epochs and encourages or discourages the related metaheuristic. Thus, due its stochas-tic nature, HH attempts to avoid local minima, while utilizing promising regions in search space more conveniently by increasing the probability of invoking relatively more

关键词： Artificial neural networks Machine learning Hyper -heuristics Particle swarm optimization Flower pollination algorithm differential evolution algorithm

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An improved IWO-FCM Data Mining algorithm

An improved IWO-FCM Data Mining Algorithm

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26th Chinese Control and Decision Conference (CCDC)

作者： Zhao Xiaoqiang Zhou Jinhu Gansu Mfg Informationizat Engn Technol Res Ctr Lanzhou 730050 Peoples R China

ISBN: (纸本)9781479937066

The FCM algorithm based on invasive weed optimization algorithm (IWO-FCW) has stronger global optimization ability and higher clustering precision than the basic FCM algorithm, but the IWO-FCW algorithm exists some questions that the convergence become slow and the clustering precision is not high for high and complex testing data sets. So an improved IWO-FCM algorithm is proposed in this paper. This algorithm uses the chaos sequence to initialize the initial population in order to improve initial solution (seed) quality, then the crossover, mutation and part selection operation of the differential evolution algorithm are applied in the spatial distribution and selection process of IWO-FCM algorithm to keep the population diversity and enhance global optimization ability. By testing multiple high-dimensional data sets, the simulation results show that the proposed algorithm has faster convergence speed and higher optimization precision than FCM algorithm and IWO-FCM algorithm.

关键词： Data mining IWO-FCM Chaos differential evolution algorithm

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Comparative Analysis of evolutionary algorithms for the Problem of Parametric Optimization of PID Controllers

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Procedia Computer Science 2017年 103卷 100-107页

作者： S.V. Konstantinov A.A. Baryshnikov RUDN University 6 Mikluho-Maklaya str. Moscow 117198 Russia

Six modern and promising evolutionary algorithms are described: genetic algorithm, differential evolution method, variational genetic algorithm, particle swarm optimization algorithm, bat-inspired method and firefly algorithm. For all algorithms brief description and main steps of receiving solution are given. In the experimental part all algorithms are compared by the effectiveness of solving the parametric optimization problem for PID controllers.

关键词： evolutionary algorithms global optimization problem genetic algorithm differential evolution algorithm variational genetic algorithm particle swarm optimization bat-inspired algorithm fireflies algorithm

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A method for the allocation of sequencing resources in genotyped livestock populations

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GENETICS SELECTION evolution 2017年第20170522期49卷 47-47页

作者： Gonen, Serap Ros-Freixedes, Roger Battagin, Mara Gorjanc, Gregor Hickey, John M. Univ Edinburgh Roslin Inst Easter Bush Midlothian Scotland Univ Edinburgh Royal Dick Sch Vet Studies Easter Bush Midlothian Scotland

Background: This paper describes a method, called AlphaSeqOpt, for the allocation of sequencing resources in livestock populations with existing phased genomic data to maximise the ability to phase and impute sequenced haplotypes into the whole population. Methods: We present two algorithms. The first selects focal individuals that collectively represent the maximum possible portion of the haplotype diversity in the population. The second allocates a fixed sequencing budget among the families of focal individuals to enable phasing of their haplotypes at the sequence level. We tested the performance of the two algorithms in simulated pedigrees. For each pedigree, we evaluated the proportion of population haplotypes that are carried by the focal individuals and compared our results to a variant of the widely-used key ancestors approach and to two haplotype-based approaches. We calculated the expected phasing accuracy of the haplotypes of a focal individual at the sequence level given the proportion of the fixed sequencing budget allocated to its family. Results: AlphaSeqOpt maximises the ability to capture and phase the most frequent haplotypes in a population in three ways. First, it selects focal individuals that collectively represent a larger portion of the population haplotype diversity than existing methods. Second, it selects focal individuals from across the pedigree whose haplotypes can be easily phased using family-based phasing and imputation algorithms, thus maximises the ability to impute sequence into the rest of the population. Third, it allocates more of the fixed sequencing budget to focal individuals whose haplotypes are more frequent in the population than to focal individuals whose haplotypes are less frequent. Unlike existing methods, we additionally present an algorithm to allocate part of the sequencing budget to the families (i.e. immediate ancestors) of focal individuals to ensure that their haplotypes can be phased at the sequence level, whic

关键词： Sequence Level differential evolution algorithm Focal Individual Livestock Population Quantitative Trait Nucleotide

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An algorithm for efficient constrained mate selection

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GENETICS SELECTION evolution 2011年第1期43卷 4-4页

作者： Kinghorn, Brian P. Univ New England Sch Environm & Rural Sci Armidale NSW 2350 Australia

Background: Mate selection can be used as a framework to balance key technical, cost and logistical issues while implementing a breeding program at a tactical level. The resulting mating lists accommodate optimal contributions of parents to future generations, in conjunction with other factors such as progeny inbreeding, connection between herds, use of reproductive technologies, management of the genetic distribution of nominated traits, and management of allele/genotype frequencies for nominated QTL/markers. Methods: This paper describes a mate selection algorithm that is widely used and presents an extension that makes it possible to apply constraints on certain matings, as dictated through a group mating permission matrix. Results: This full algorithm leads to simpler applications, and to computing speed for the scenario tested, which is several hundred times faster than the previous strategy of penalising solutions that break constraints. Conclusions: The much higher speed of the method presented here extends the use of mate selection and enables implementation in relatively large programs across breeding units.

关键词： Group Combination Female Group Male Group differential evolution algorithm Target Number

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