检索结果-内蒙古大学图书馆

13th International Symposium on Intelligent Systems (INTELS)

作者： Konstantinov, S. V. Diveev, A. I. Balandina, G. I. Baryshnikov, A. A. RUDN Univ Miklukho Maklaya Str 6 Moscow 117198 Russia RAS Fed Res Ctr Comp Sci & Control Vavilova Str 44 B 2 Moscow 119333 Russia

An experimental comparison of evolutionary algorithms and random search algorithms for the optimal control problem is carried out. The problem is solved separately by several representatives of each type of algorithms. The simulation is performed on a mobile robot model. The results of each algorithm performance are compared according to the best found value of the fitness function, the mean value and the standard deviation. (C) 2019 The Authors. Published by Elsevier B.V.

关键词： evolutionary algorithms random search algorithms optimal control

来源：评论

学校读者我要写书评

暂无评论

Harnessing Hybrid random search algorithms for Intelligent State Control in Technological Processes

Harnessing Hybrid Random Search Algorithms for Intelligent S...

引用

International Conference on Intelligent and Fuzzy Systems (INFUS)

作者： Musaev, A. A. Grigoriev, D. A. ITMO Univ St Petersburg Russia St Petersburg State Univ SPBU Ctr Econometr & Business Analyt CEBA St Petersburg Russia

ISBN: (纸本)9783031671913;9783031671920

This article presents a research focused on improving the control of stochastic search procedures within an e-neighborhood surrounding current monitoring outcomes of control parameters in technological processes. The research aimed to enhance the effectiveness of control measures through the utilization of state-of-the-art random search (RS) technology. A mathematical structure was established to define an e-neighborhood for manipulating parameters and setting boundaries for the search space. Various methods were explored for selecting manipulation parameters using operator-driven processes via Human-Machine Interface (HMI) tools. The RS-Control Module's functional structure accounted for the dynamics of controlled parameter evolution using a sliding observation window. The RS-Optimization Module's software implementation allowed for forecasting output parameters and assessing forecast accuracy using quality indicators. The implementation of the main program provided a user-friendly interface for adjusting process parameters, optimizing criteria, and monitoring control based on RS forecasts. The research demonstrated the effectiveness of the proposed approach in improving output and technological parameters in industrial processes.

关键词： Stationarity violation random search algorithms Hybrid algorithms Predictive algorithms

来源：评论

学校读者我要写书评

暂无评论

Comparative Research of random search algorithms and Evolutionary algorithms for the Optimal Control Problem of the Mobile Robot

引用

Procedia Computer Science 2019年 150卷 462-470页

作者： S.V. Konstantinov A.I. Diveev G.I. Balandina A.A. Baryshnikov RUDN University Miklukho-Maklaya str. 6 Moscow 117198 Russia Federal Research Centre "Computer Science and Control" of RAS Vavilova str. 44 b. 2 Moscow 119333 Russia

关键词： evolutionary algorithms random search algorithms optimal control

来源：评论

学校读者我要写书评

暂无评论

Finding crystal structures from few diffraction data by a combination of a random search with genetic algorithms

引用

JOURNAL OF APPLIED CRYSTALLOGRAPHY 2008年第4期41卷 784-790页

作者： Immirzi, Attilio Erra, Loredana Tedesco, Consiglia Univ Salerno Dipartimento Chim I-84084 Fisciano SA Italy

A new procedure for performing structural analysis of crystalline materials from diffraction data, using internal coordinates, is described. For starting information only unit-cell content, space group, chemical formula, molecular connectivity and a limited amount of diffraction data are required. After first selecting a number of solutions using a Monte Carlo approach with severe filters, which reject the most unrealistic solutions, genetic algorithms (crossover and mutations) are applied. In fact, the initial selection step alone is, frequently, a powerful tool for discovering structures, without recourse to the genetic algorithms. The procedure, while suffering from the limitation that connectivity must be known, is effective in cases where direct methods are not applicable because the diffraction data are scarce, are limited to low diffraction angles or are missing in specific portions of the reciprocal space. The main features of the algorithm are described and examples of validation given. The routines are now available as part of the freely distributed general-purpose program TRY. The program is available on the Web at http://***/***.

关键词： structural analysis sparse diffraction data random search algorithms genetic algorithms computer programs TRY

来源：评论

学校读者我要写书评

暂无评论

Gaussian Process-Based random search for Continuous Optimization via Simulation

引用

OPERATIONS REsearch 2025年第1期73卷 iii-vii, 1-582, C2-C3页

作者： Wang, Xiuxian Hong, L. Jeff Jiang, Zhibin Shen, Haihui Shanghai Jiao Tong Univ Sino US Global Logist Inst Antai Coll Econ & Management Shanghai 200030 Peoples R China Fudan Univ Sch Management Shanghai 200433 Peoples R China Fudan Univ Sch Data Sci Shanghai 200433 Peoples R China Shanghai Jiao Tong Univ Antai Coll Econ & Management Shanghai 200030 Peoples R China

random search is an important category of algorithms to solve continuous optimization via simulation problems. To design an efficient random search algorithm, the handling of the triple "E" (i.e., exploration, exploitation and estimation) is critical. The first two E's refer to the design of sampling distribution to balance explorative and exploitative searches, whereas the third E refers to the estimation of objective function values based on noisy simulation observations. In this paper, we propose a class of Gaussian process-based random search (GPRS) algorithms, which provide a new framework to handle the triple "E." In each iteration, algorithms under the framework build a Gaussian process surrogate model to estimate the objective function based on single observation of each sampled solution and randomly sample solutions from a lower-bounded sampling distribution. Under the assumption of heteroscedastic and known simulation noise, we prove the global convergence of GPRS algorithms. Moreover, for Gaussian processes having continuously differentiable sample paths, we show that the rate of convergence of GPRS algorithms can be no slower than Op(n �1=(d+2)). Then, we introduce a specific GPRS algorithm to show how to design an integrated GPRS algorithm with adaptive sampling distributions and how to implement the algorithm efficiently. Numerical experiments show that the algorithm has good performances, even for problems where the variances of simulation noises are unknown.

关键词： continuous optimization via simulation random search algorithms Gaussian process regression convergence rate of convergence

来源：评论

学校读者我要写书评

暂无评论

search schemes for random optimization algorithms that preserve the asymptotic distribution

引用

JOURNAL OF APPLIED PROBABILITY 1999年第3期36卷 825-836页

作者： Dorea, CCY Gonçalves, CR Univ Brasilia Dept Matemat BR-70910900 Brasilia DF Brazil Univ Estadual Paulista Dept Matemat BR-19060900 Presdidente Prudente SP Brazil

Markovian algorithms for estimating the global maximum or minimum of real valued functions defined on some domain Omega subset of R-d are presented. Conditions on the search schemes that preserve the asymptotic distribution are derived. Global and local search schemes satisfying these conditions are analysed and shown to yield sharper confidence intervals when compared to the i.i.d. case.

关键词： random search algorithms global optimization search schemes asymptotic distribution

来源：评论

学校读者我要写书评

暂无评论

Population-based algorithm for discrete facility location with ranking of candidate locations

引用

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS 2025年 457卷

作者： Lancinskas, Algirdas Zilinskas, Julius Fernandez, Pascual Pelegrin, Blas Vilnius Univ Inst Data Sci & Digital Technol Akad 4 LT-08663 Vilnius Lithuania Univ Murcia Dept Stat & Operat Res Campus Espinardo Murcia 30071 Spain

Facility location problems are mathematical optimization problems that involve finding the best locations for facilities (e.g., factories, warehouses, stores) to serve customers within a given geographic area. The goal is typically to minimize costs, maximize efficiency, or optimize other objectives. Facility location problems can vary in several ways, including customer behavior rules, the type of search space, and constraints on locations for new facilities being located. These variations directly impact the complexity of the problem and the appropriate solution methods that can be used to tackle the problem. This research is focused on the discrete competitive facility location problem for an entering firm, which is a crucial scenario for new firms entering the existing market. The goal is to strategically locate new facilities to maximize their profit, while considering existing competitors. A new random search heuristic algorithm to approximate the optimal solution for discrete competitive facility location problems for firm expansion has been developed. The algorithm extends its precursor that ranks potential locations for the new facilities depending on their usefulness and uselessness in creating new solutions in the past. The new algorithm uses a population to handle and reuse the best solutions found so far and new strategies for ranking potential locations, considering features of the solutions in the population. The designed algorithm has been investigated by solving competitive facility location problems actual for an entering firms using real geographical data.

关键词： Competitive facility location Discrete optimization random search algorithms

来源：评论

学校读者我要写书评

暂无评论

random search immune algorithm for community detection

引用

SOFT COMPUTING 2023年第12期27卷 8061-8090页

作者： Spampinato, Antonio G. Scollo, Rocco A. Cutello, Vincenzo Pavone, Mario Univ Catania Dept Math & Comp Sci V Le A Doria 6 I-95125 Catania Italy

Community detection is a prominent research topic in Complex Network Analysis, and it constitutes an important research field on all those areas where complex networks represent a powerful interpretation tool for describing and understanding systems involved in neuroscience, biology, social science, economy, and many others. A challenging approach to uncover the community structure in complex network, and then revealing the internal organization of nodes, is Modularity optimization. In this research paper, we present an immune optimization algorithm (opt-IA) developed to detect community structures, with the main aim to maximize the modularity produced by the discovered communities. In order to assess the performance of opt-IA, we compared it with an overall of 20 heuristics and metaheuristics, among which one Hyper-Heuristic method, using social and biological complex networks as data set. Unlike these algorithms, opt-IA is entirely based on a fully random search process, which in turn is combined with purely stochastic operators. According to the obtained outcomes, opt-IA shows strictly better performances than almost all heuristics and metaheuristics to which it was compared;whilst it turns out to be comparable with the Hyper-Heuristic method. Overall, it can be claimed that opt-IA, even if driven by a purely random process, proves to be reliable and with efficient performance. Furthermore, to prove the latter claim, a sensitivity analysis of the functionality was conducted, using the classic metrics NMI, ARI and NVI.

关键词： Metaheuristics Community detection Immune-inspired algorithms random search algorithms Combinatorial optimization Complex networks Modularity optimization

来源：评论

学校读者我要写书评

暂无评论

SUNSHINE A novel random search for continuous global optimization 1

SUNSHINE A novel random search for continuous global optimiz...

引用

1st Conference on Swarm Intelligence and Evolutionary Computation (CSIEC)

作者： Jahedbozorgan, Mohammadreza Amjadifard, Roya Kharazmi Univ Dept Elect & Comp Engn Tehran Iran

ISBN: (纸本)9781467387378

random search algorithms are widely used in many ill-structured global optimization problems. This wide application is due to random search algorithms' capability to model and solve continuous, discrete, or hybrid problems. Moreover, the researchers discuss that the random search algorithms yield a proper solution in terms of fitness and time consumed. However, these algorithms lack guarantee of achieving the global optimum. Regarding the discussed researches, this paper considers the most critical shortcoming of studied algorithms as getting trapped in local optimums. Focusing on continuous global optimization problems, a novel algorithm is proposed. This algorithm, called "SUNSHINE", fulfills the aforementioned shortcoming. Besides, the other advantages of SUNSHINE, including efficient time complexity, robustness, and low sensitivity of accurate adjustment of parameters, are illustrated through a comprehensive case study. Moreover, the paper discusses the capability of SUNSHINE in parallel implementation.

关键词： Continuous Optimization Evolutionary algorithms Global Optimization Metaheuristic Methods random search algorithms

来源：评论

学校读者我要写书评

暂无评论

Pattern Hit-and-Run for sampling efficiently on polytopes

引用

OPERATIONS REsearch LETTERS 2012年第1期40卷 6-11页

作者： Mete, Huseyin Onur Zabinsky, Zelda B. Univ Washington Seattle WA 98195 USA

Pattern Hit-and-Run (PHR) is a Markov chain Monte Carlo sampler for a target distribution that was originally designed for general sets embedded in a box. A specific set of interest to many applications is a polytope intersected with discrete or mixed continuous/discrete lattices. PHR requires an acceptance/rejection mechanism along a bidirectional walk to guarantee feasibility. We remove this inefficiency by utilizing the linearity of the constraints defining the polytope, so each iteration of PHR can be efficiently implemented even though the variables are allowed to be integer valued. Moreover, PHR converges to a uniform distribution in polynomial time for a class of discrete polytopes. (C) 2011 Elsevier B.V. All rights reserved.

关键词： random search algorithms Global optimization Simulated annealing Markov chain Monte Carlo sampling

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：