检索结果-内蒙古大学图书馆

Performance of metaheuristic algorithms for Wind Resource Modelling: A Comparison Using 80 m Mast Measurements

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第12期49卷 16065-16081页

作者： Samal, Rajat Kanti Veer Surendra Sai Univ Technol Dept Elect Engn Sambalpur 768006 Odisha India

Wind speed probability distributions are integral part of wind resource assessment (WRA) and subsequent power potential estimation. Though a number of probability distribution and parameter estimation methods have been proposed, estimation of appropriate distribution is a challenging task. Therefore, this work compares the performance of four established metaheuristic techniques for parameter estimation. The comparison is performed for both marginal and two-component mixture distributions utilizing wind resource data from 80 m measurement masts. Estimation accuracy is evaluated based on goodness-of-fit statistics. It is found that the choice of the fitness functions is of paramount importance irrespective of the chosen optimization method. The efficacy of the techniques is found to be dependent on the targeted distribution. However, it is found that selected algorithms are not very effective and inconsistent in estimating mixture distribution parameters. The time of convergence is dependent on probability distributions and to lesser extent on fitness functions. Finally, the 1 m/s bin is found to be as effective and sometimes better than a 0.5 m/s bin. The findings of this work will help researchers select suitable algorithms for WRA studies.

关键词： Marginal distribution Mixture distribution metaheuristic algorithm Fitness function Chi Square test statistic

来源：评论

学校读者我要写书评

暂无评论

Population Generation Methods for metaheuristic algorithms Used to Construct Compact Fuzzy Classifiers of Medical Data

引用

PATTERN RECOGNITION AND IMAGE ANALYSIS 2024年第3期34卷 396-411页

作者： Bardamova, M. Svetlakov, M. Sarin, K. Hodashinskaya, A. Shurygin, Y. Hodashinsky, I. Tomsk State Univ Control Syst & Radioelect Tomsk 634050 Russia

Fuzzy classifiers differ from other machine learning algorithms in their ability to interpret the inference process, which is especially important in high responsibility subject areas such as medicine. The membership functions of fuzzy terms and the rule base are easy to visualize, so it is not difficult for the user to understand why a particular result was obtained. However, the interpretability of the model suffers if the resulting model is highly complex, when the classifier has more than a dozen of high-length rules. Balancing accuracy and complexity in fuzzy classifiers is a nontrivial task. This article, the first in a series about constructing compact classifiers for medical data, addresses the problem of maximizing accuracy with as few rules as possible using metaheuristic algorithms. Using metaheuristics to optimize fuzzy rules allows a more accurate representation of the subject domain, which has a positive effect on classification accuracy. To increase the efficiency of population metaheuristics, it is important to use an appropriate method for a particular algorithm to form optimization starting points. The paper investigates the effect of using different population identification methods for two metaheuristics - the swallow swarm algorithm and the hybrid of the gravitational search algorithm and the shuffled leaping frogs algorithm.

关键词： machine learning fuzzy systems fuzzy classifier metaheuristic algorithm model parameter tuning data classification metaheuristic population initialization

来源：评论

学校读者我要写书评

暂无评论

Gold Seekers algorithm: An Innovative metaheuristic Approach for Global Optimization and Its Application in Image Segmentation

引用

INTERNATIONAL JOURNAL OF ENGINEERING 2025年第9期38卷 2114-2129页

作者： Ehsaeyan, E. Sirjan Univ Technol Elect Engn Dept Sirjan Iran

A significant disadvantage of meta-heuristic algorithms is their tendency to get stuck in local optima, which prevents them from finding the global optimal solution and potentially limits their overall effectiveness. To mitigate this drawback, a novel metaheuristic optimization algorithm known as the Gold Seekers algorithm (GSA) is introduced in this paper to address complex optimization problems. This algorithm mimics the competitive behavior seen among miners hunting for gold. GSA has several key advantages, including fast convergence due to parallel execution, low sensitivity to control parameters due to adaptive coefficients, and high accuracy in global searches thanks to its structured division of search agents into specific roles. The exploitation phase involves searching near the leader to identify valuable areas, while the exploration phase focuses on searching among gold seekers to discover unknown golden locations. To validate GSA's effectiveness, the algorithm was tested on 20 classical benchmark functions of various types and dimensions (30, 60, and 90) and applied to image segmentation to find optimal thresholds. Performance in this practical application was compared to eight well-known heuristic techniques from existing *** findings indicate that the suggested algorithm achieves superior solution accuracy and expedited convergence across most benchmark functions, in addition to determining optimal thresholds for image segmentation tasks.

关键词： Gold Seekers algorithm metaheuristic algorithm Swarm Intelligence Optimization Techniques Benchmark Problems Image Thresholding

来源：评论

学校读者我要写书评

暂无评论

Application of state-of-the-art multiobjective metaheuristic algorithms in reliability-based design optimization: a comparative study

引用

STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2023年第8期66卷 1-27页

作者： Meng, Zeng Yildiz, Betul Sultan Li, Gang Zhong, Changting Mirjalili, Seyedali Yildiz, Ali Riza Hefei Univ Technol Sch Civil Engn Hefei 230009 Peoples R China Uludag Univ Dept Mech Engn Bursa Turkiye Dalian Univ Technol Dept Engn Mech State Key Lab Struct Anal Ind Equipment Dalian 116024 Peoples R China Hainan Univ Sch Civil Engn & Architecture Haikou Peoples R China Torrens Univ Brisbane Australia

Multiobjective reliability-based design optimization (RBDO) is a research area, which has not been investigated in the literatures comparing with single-objective RBDO. This work conducts an exhaustive study of fifteen new and popular metaheuristic multiobjective RBDO algorithms, including non-dominated sorting genetic algorithm II, differential evolution for multiobjective optimization, multiobjective evolutionary algorithm based on decomposition, multiobjective particle swarm optimization, multiobjective flower pollination algorithm, multiobjective bat algorithm, multiobjective gray wolf optimizer, multiobjective multiverse optimization, multiobjective water cycle optimizer, success history-based adaptive multiobjective differential evolution, success history-based adaptive multiobjective differential evolution with whale optimization, multiobjective salp swarm algorithm, real-code population-based incremental learning and differential evolution, unrestricted population size evolutionary multiobjective optimization algorithm, and multiobjective jellyfish search optimizer. In addition, the adaptive chaos control method is employed for the above-mentioned algorithms to estimate the probabilistic constraints effectively. This comparative analysis reveals the critical technologies and enormous challenges in the RBDO field. It also offers new insight into simultaneously dealing with the multiple conflicting design objectives and probabilistic constraints. Also, this study presents the advantage and future development trends or incurs the increased challenge of researchers to put forward an effective multiobjective RBDO algorithm that assists the complex engineering system design.

关键词： Reliability-based design optimization Multiobjective metaheuristic algorithm Evolutionary algorithm Probabilistic constraint

来源：评论

学校读者我要写书评

暂无评论

An exhaustive review of the metaheuristic algorithms for search and optimization: taxonomy, applications, and open challenges

引用

ARTIFICIAL INTELLIGENCE REVIEW 2023年第11期56卷 13187-13257页

作者： Rajwar, Kanchan Deep, Kusum Das, Swagatam Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttarakhand India Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 W Bengal India

As the world moves towards industrialization, optimization problems become more challenging to solve in a reasonable time. More than 500 new metaheuristic algorithms (MAs) have been developed to date, with over 350 of them appearing in the last decade. The literature has grown significantly in recent years and should be thoroughly reviewed. In this study, approximately 540 MAs are tracked, and statistical information is also provided. Due to the proliferation of MAs in recent years, the issue of substantial similarities between algorithms with different names has become widespread. This raises an essential question: can an optimization technique be called 'novel' if its search properties are modified or almost equal to existing methods? Many recent MAs are said to be based on 'novel ideas', so they are discussed. Furthermore, this study categorizes MAs based on the number of control parameters, which is a new taxonomy in the field. MAs have been extensively employed in various fields as powerful optimization tools, and some of their real-world applications are demonstrated. A few limitations and open challenges have been identified, which may lead to a new direction for MAs in the future. Although researchers have reported many excellent results in several research papers, review articles, and monographs during the last decade, many unexplored places are still waiting to be discovered. This study will assist newcomers in understanding some of the major domains of metaheuristics and their real-world applications. We anticipate this resource will also be useful to our research community.

关键词： Optimization metaheuristic algorithm Nature inspired algorithm Parameter

来源：评论

学校读者我要写书评

暂无评论

Optimal operation of the multi-reservoir system: a comparative study of robust metaheuristic algorithms

引用

INTERNATIONAL JOURNAL OF HYDROLOGY SCIENCE AND TECHNOLOGY 2024年第3期17卷 239-266页

作者： Verma, Shashikant Prasad, A. D. Verma, Mani Kant Natl Inst Technol Raipur Dept Civil Engn Great Eastern Rd Raipur 492010 Chhattisgarh India

In a multi-reservoir system, the functioning of one reservoir affects the other reservoirs. A multi-reservoir system must be managed as a single entity for sustainable water management. Thus, assessing a multi-reservoir system's existing operating policy is crucial for integrated operation. In this study, the recently introduced grey wolf optimisation (GWO) was compared to the robust metaheuristic algorithm the whale optimisation algorithm (WOA). First, the performance of these algorithms was evaluated and compared for multi-reservoir system operation optimisation. The statistical indices coefficient of determination (R-2), root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), normalise mean square error (NMSE), and mean absolute percentage error (MAPE) were used to compare the algorithms' performance. According to the test findings, among the ten algorithms evaluated. GWO was shown to be the most effective, and it is suggested as a reliable and promising technique for optimising multi-reservoir systems.

关键词： metaheuristic algorithm multi-reservoir system Mahanadi reservoir project grey wolf optimisation GWO whale optimisation algorithm WOA

来源：评论

学校读者我要写书评

暂无评论

Skill Optimization algorithm:A New Human-Based metaheuristic Technique

引用

Computers, Materials & Continua 2023年第1期74卷 179-202页

作者： Hadi Givi Marie Hubalovska Department of Electrical Engineering Shahreza CampusUniversity of IsfahanIran Department of Technics Faculty of EducationUniversity of Hradec KraloveCzech Republic

metaheuristic algorithms are widely used in solving optimization *** this paper,a new metaheuristic algorithm called Skill Optimization algorithm(SOA)is proposed to solve optimization *** fundamental inspiration in designing SOA is human efforts to acquire and improve *** stages of SOA are mathematically modeled in two phases,including:(i)exploration,skill acquisition from experts and(ii)exploitation,skill improvement based on practice and individual *** efficiency of SOA in optimization applications is analyzed through testing this algorithm on a set of twenty-three standard benchmark functions of a variety of unimodal,high-dimensional multimodal,and fixed-dimensional multimodal *** optimization results show that SOA,by balancing exploration and exploitation,is able to provide good performance and appropriate solutions for optimization *** addition,the performance of SOA in optimization is compared with ten metaheuristic algorithms to evaluate the quality of the results obtained by the proposed *** and comparison of the obtained simulation results show that the proposed SOA has a superior performance over the considered algorithms and achievesmuch more competitive results.

关键词： Optimization human-based skill exploration exploitation metaheuristic algorithm

来源：评论

学校读者我要写书评

暂无评论

Billiards Optimization algorithm:A New Game-Based metaheuristic Approach

引用

Computers, Materials & Continua 2023年第3期74卷 5283-5300页

作者： Hadi Givi Marie Hubálovská Department of Electrical Engineering Shahreza CampusUniversity of IsfahanIran Department of Technics Faculty of EducationUniversity of Hradec KraloveCzech Republic

metaheuristic algorithms are one of themost widely used stochastic approaches in solving optimization *** this paper,a new metaheuristic algorithm entitled Billiards Optimization algorithm(BOA)is proposed and designed to be used in optimization *** fundamental inspiration in BOA design is the behavior of the players and the rules of the billiards *** steps of BOA are described and then its mathematical model is thoroughly *** efficiency of BOA in dealing with optimization problems is evaluated through optimizing twenty-three standard benchmark functions of different types including unimodal,high-dimensional multimodal,and fixed-dimensionalmultimodal *** order to analyze the quality of the results obtained by BOA,the performance of the proposed approach is compared with ten well-known *** simulation results show that BOA,with its high exploration and exploitation abilities,achieves an impressive performance in providing solutions to objective functions and is superior and far more competitive compared to the ten competitor algorithms.

关键词： Optimization game-based billiards game exploration exploitation metaheuristic algorithm

来源：评论

学校读者我要写书评

暂无评论

metaheuristic algorithms for Multiagent Routing Problems

引用

AUTOMATION AND REMOTE CONTROL 2021年第10期82卷 1787-1801页

作者： Germanchuk, M. S. Lemtyuzhnikova, D., V Lukianenko, V. A. Vernadsky Crimean Fed Univ Simferopol 295007 Russia Russian Acad Sci Trapeznikov Inst Control Sci Moscow 117997 Russia Moscow Inst Aviat Technol Moscow 125993 Russia

The problems of constructing routes in complex networks by many sales agents are considered. Formalization leads to pseudo-Boolean discrete optimization problems with constraints that take into account the specifics of route construction. The sparsity of the constraint matrix permits one to apply decomposition approaches and network clustering. The development of approximate algorithms for selecting routes in complex networks involves taking into account the properties of the network structure, its complexity, the presence of restrictions, regulations, reachability conditions, and the number of sales agents. It is shown that the solution of routing problems can be based on the application of a multiagent approach in combination with clustering (decomposition) of the original problem and metaheuristics. Multiagent systems with swarm intelligence are used to solve complex discrete optimization problems that cannot efficiently be solved by classical algorithms. The agent model for a complex network of a problem of the many traveling salesmen type becomes an intelligent system that defines heuristic algorithms for finding the optimal solution by reactive agents (that follow the rules laid down in them). We use and describe in detail the composition of the algorithms, which have proven themselves well in computational experiments;those are a modification of the genetic algorithm, ant colony optimization, artificial bee colony algorithm, and simulated annealing. A generalized algorithm is proposed and implemented in which a simpler network (a flyover network) is matched to the source network. Here a numerical experiment was carried out for the problem of routing on a GIS map for urban infrastructure. Clustering algorithms are implemented in which the initially traversed routes are refined using 2-opt algorithms, simulated annealing, and other metaheuristics. A comparison of the algorithms used and an illustration of their operation are given.

关键词： metaheuristic algorithm multiagent optimization problem discrete optimization pseudo-Boolean problem

来源：评论

学校读者我要写书评

暂无评论

Comparison of metaheuristic algorithms for Photovoltaic Systems Allocation in a Power Distribution Feeder 2

Comparison of Metaheuristic Algorithms for Photovoltaic Syst...

引用

2nd IEEE Conference on Artificial Intelligence (CAI)

作者： Jaramillo-Leon, Brian Almeida, Jose Soares, Joao Leite, Jonatas B. Zambrano-Asanza, Sergio Vale, Zita Sao Paulo State Univ Dept Elect Engn Sao Paulo Brazil Polytech Porto GECAD LASI Porto Portugal Sao Paulo State Univ CENTROSUR Dept Planning Dept Elect Engn Cuenca Ecuador

ISBN: (纸本)9798350354102;9798350354096

The government's endorsement of renewable energy objectives and the requirement to use carbon-free energy sources to keep up with the growth in energy consumption have expanded the integration of solar photovoltaic (PV) systems in distribution networks. However, an excessive PV penetration may lead to operational threshold violations. PV system allocation that is optimal in terms of placement and sizing can enhance power quality and grid performance. We formulate the allocation of PV systems as a combinatorial mixed-integer nonlinear model to maximize the distribution network PV hosting capacity (PVHC). We chose three differential evolution (DE) mutation strategies, namely DE/rand/1/bin, DE/***/1/bin, and DE/rand/1/***, and the vortex search (VS) algorithm to solve that optimization problem. This study aims to identify the method that solves the PV allocation problem with higher quality. We performed manual parameter tuning to set both the population and iteration numbers for each algorithm. In addition, for the DE mutation strategies, we set the scale factor and crossover rate parameters. The results show that the VS provides the highest grid PVHC.

关键词： differential evolution distribution system hosting capacity metaheuristic algorithm photovoltaic allocation vortex search

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：