检索结果-内蒙古大学图书馆

18th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Uchida, Kento Hamano, Ryoki Nomura, Masahiro Saito, Shota Shirakawa, Shinichi Yokohama Natl Univ Yokohama Kanagawa Japan CyberAgent Inc Shibuya Japan SKILLUP NeXt Ltd Tokyo Japan

ISBN: (纸本)9783031700675;9783031700682

Discrete and mixed-variable optimization problems have appeared in several real-world applications. Most of the research on mixed-variable optimization considers a mixture of integer and continuous variables, and several integer handlings have been developed to inherit the optimization performance of the continuous optimization methods to mixed-integer optimization. In some applications, acceptable solutions are given by selecting possible points in the disjoint subspaces. This paper focuses on the optimization on sets of points and proposes an optimization method by extending the covariance matrix adaptation evolution strategy (CMA-ES), termed the CMA-ES on sets of points (CMA-ES-SoP). The CMA-ES-SoP incorporates margin correction that maintains the generation probability of neighboring points to prevent premature convergence to a specific non-optimal point, which is an effective integer-handling technique for CMA-ES. In addition, because margin correction with a fixed margin value tends to increase the marginal probabilities for a portion of neighboring points more than necessary, the CMA-ES-SoP updates the target margin value adaptively to make the average of the marginal probabilities close to a predefined target probability. Numerical simulations demonstrated that the CMA-ES-SoP successfully optimized the optimization problems on sets of points, whereas the naive CMA-ES failed to optimize them due to premature convergence.

关键词： CMA-ES discrete optimization mixed-variable optimization adaptation

来源：评论

学校读者我要写书评

暂无评论

A particle swarm optimization algorithm for mixed-variable optimization problems

引用

SWARM AND EVOLUTIONARY COMPUTATION 2021年 60卷

作者： Wang, Feng Zhang, Heng Zhou, Aimin Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China East China Normal Univ Sch Comp Sci & Technol Shanghai Key Lab Multidimens Informat Proc Shanghai 200241 Peoples R China

Many optimization problems in reality involve both continuous and discrete decision variables, and these problems are called mixed-variable optimization problems (MVOPs). The mixed decision variables of MVOPs increase the complexity of search space and make them difficult to be solved. The Particle Swarm optimization (PSO) algorithm is easy to implement due to its simple framework and high speed of convergence, and has been successfully applied to many difficult optimization problems. Many existing PSO variants have been proposed to solve continuous or discrete optimization problems, which make it feasible and promising for solving MVOPs. In this paper, a new PSO algorithm for solving MVOPs is proposed, namely PSOmv, which can deal with both continuous and discrete decision variables simultaneously. To efficiently handle mixed variables, the PSOmv employs a mixed-variable encoding scheme. Based on the mixed-variable encoding scheme, two reproduction methods respectively for continuous variables and discrete variables are proposed. Furthermore, an adaptive parameter tuning strategy is employed and a constraints handling method is utilized to improve the overall efficiency of the PSOmv. The experimental results on 28 artificial MVOPs and two practical MVOPs demonstrate that the proposed PSOmv is a competitive algorithm for MVOPs.

关键词： Particle swarm optimization mixed-variable optimization Parameter tuning

来源：评论

学校读者我要写书评

暂无评论

A Group Rotate-Vector Algorithm for mixed-variable optimization Problems

引用

IEEE ACCESS 2024年 12卷 184629-184643页

作者： Li, Zhongming Yan, Lutao Li, Haiyuan Wang, Lianxin Beijing Univ Posts & Telecommun Sch Intelligent Engn & Automat Beijing 100876 Peoples R China China Acad Chinese Med Sci Inst Basic Res Clin Med Beijing 100700 Peoples R China

Many engineering design optimization problems can be represented as mixed-variable optimization problems. This study presents a heuristic approach for solving mixed-variable optimization using rotation and contraction of vectors. The current optimization algorithm, known as rotate-vector, has shown promising results in solving optimization problems with real or integer variables. Building upon this concept, the authors of this paper develop methods to solve 0-1 type and general discrete type variables, and introduce corresponding rotation and contraction operators. To address the simultaneous search and optimization of multiple variable types in nonlinear mixed integer programming problems, a grouping and comprehensive processing method is employed. The proposed group rotate-vector algorithm (GRV) is evaluated through the solution of multiple 0-1 programming problems and mixed-variable optimization problems. The study also investigates the impact of parameter settings on solution quality and efficiency. The experimental results demonstrate that the GRV algorithm outperforms other algorithms in terms of solution quality.

关键词： optimization Vectors Heuristic algorithms Search problems Robots Integer programming Programming Convergence Constraint handling Classification algorithms mixed-variable optimization 0-1 programming rotate-vector heuristic algorithm

来源：评论

学校读者我要写书评

暂无评论

Three-partition coevolutionary differential evolution algorithm for mixed-variable optimization problems

引用

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartC期133卷

作者： Gan, Guojun Ye, Hengzhou Dong, Minggang Ye, Wei Wang, Yan Guilin Univ Technol Sch Informat Sci & Engn Guilin 541004 Peoples R China Guangxi Key Lab embedded technol & intelligent Sys Guilin 541004 Peoples R China

Both in industrial and scientific fields, many optimization problems involve continuous and discrete decision variables. Such problems are called mixed -variable optimization problems (MVOPs). MVOPs remain challenging due to the different spatial distribution characteristics of continuous, ordinal, and categorical variables. In this study, a new variant of differential evolution (DE), called the three -partition coevolutionary DE algorithm for MVOPs (TCDEmv) is proposed. First, a mixed -variable three -partition coevolutionary scheme that can simultaneously handle MVOPs comprising continuous, ordinal, and categorical variables with the same evolution operator is proposed. Additionally, the TCDEmv adopts a dynamic adaptive (DA) mechanism to maintain the balance between ordinal and categorical variables, avoiding the quantity dominance issue. Furthermore, to enhance the efficiency of the TCDEmv, a statistical probability -based two -layer optimization strategy (SPT) was employed for ordinal and categorical variables. The experimental results on 34 artificial MVOPs show that the TCDEmv obtained better solutions and convergence than seven representative algorithms. Compared with similar algorithms in three real -world MVOPs, the TCDEmv also shows competitive performance.

关键词： mixed-variable optimization Differential evolution Three-partition Coevolutionary

来源：评论

学校读者我要写书评

暂无评论

A coevolutionary estimation of distribution algorithm based on dynamic differential grouping for mixed-variable optimization problems

引用

EXPERT SYSTEMS WITH APPLICATIONS 2024年 245卷

作者： Huang, Shijia Wang, Zhe Ge, Yang Wang, Feng Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

The mixed variable optimization problems (MVOPs), which involves both continuous and discrete decision variables, are difficult to be solved due to the complex search space. Recently, many EA-based algorithms have been designed to address MVOPs. However, due to the mixed variables with different evolutionary operators and complex search space, it is difficult to handle the mixed variables effectively and the search efficiency cannot be guaranteed. How to solve MVOPs efficiently has been a challengeable issue. In this paper, we propose a mixed-variable optimization algorithm called coevolutionary estimation of distribution algorithm (CoEDAmv). First, a dynamic differential grouping (DDG) method is employed to improve the search efficiency of CoEDAmv, in which both the interaction of variables and search performance on the current search region are considered simultaneously. Second, two probabilistic models, i.e. fitness rank based continuous histogram (FRCH) and fitness rank based discrete histogram (FRDH), are proposed to handle continuous and discrete variables respectively, which can benefit from elite individuals obtained during the fitness ranking strategy and enhance the convergence performance with the elite neighborhood-based updating probability strategy. Compared with eight state -of -the -art algorithms, the experimental results on 28 artificial MVOPs show that CoEDAmv is effective and efficient.

关键词： mixed-variable optimization Dynamic differential grouping Estimation of distribution algorithm Coevolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

mixed-variable engineering optimization based on evolutionary and social metaphors

引用

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2007年第4-6期196卷 803-817页

作者： Dimopoulos, George G. Natl Tech Univ Athens Sch Naval Architecture & Marine Engn GR-15773 Athens Greece

The co-existence of discrete and continuous independent variables in an engineering optimization problem with a multimodal objective function makes many methods incapable of solving the problem. Four methods are tested here: (a) a Simple Genetic Algorithm (SGA), (b) a Struggle Genetic Algorithm (StrGA), (c) a Particle Swarm optimization Algorithm (PSOA), and (d) a Particle Swarm optimization Algorithm with Struggle Selection (PSOStr). The last one has been developed by the author, and it is a hybrid of the evolutionary StrGA and the socially inspired PSOA. They are tested in four purely mathematical and three engineering optimization problems of the aforementioned type. All of the methods solved successfully all the problems and located the global optimum. The PSOStr, however, outperformed the other methods in terms of both solution accuracy and computational cost (i.e. function evaluations). (c) 2006 Elsevier B.V. All rights reserved.

关键词： mixed-variable optimization evolutionary algorithms particle swarm optimization hybrid algorithms

来源：评论

学校读者我要写书评

暂无评论

A Novel mixed-variable Fireworks optimization Algorithm for Path and Time Sequence optimization in WRSNs 13th

A Novel Mixed-Variable Fireworks Optimization Algorithm for ...

引用

13th European-Alliance-for-Innovation (EAI) International Conference on Communications and Networking in China (ChinaCom)

作者： Xia, Chengkai Wei, Zhenchun Lyu, Zengwei Wang, Liangliang Liu, Fei Feng, Lin Hefei Univ Technol Sch Comp & Informat Hefei Peoples R China Minist Educ Engn Res Ctr Safety Crit Ind Measurement & Contro Hefei Peoples R China Key Lab Ind Safety & Emergency Technol Hefei Anhui Peoples R China

ISBN: (纸本)9783030061616;9783030061609

To prolong the lifespan of the network, the auxiliary charging equipment is introduced into the traditional Wireless Sensor Networks (WSNs), known as Wireless Rechargeable Sensor Networks (WRSNs). Different from existing researches, in this paper, a periodic charging and data collecting model in WRSNs is proposed to keep the network working perpetually and improve data collection ratio. Meanwhile, the Wireless Charging Vehicle (WCV) has more working patterns, charging, waiting, and collecting data when staying at the sensor nodes. Then, the simultaneous optimization for the traveling path and time sequence is formulated to be a mixed-variable optimization problem. A novel mixed-variable Fireworks optimization Algorithm (MVFOA) is proposed to solve it. A large number of experiments show the feasibility of the MVFOA, and MVFOA is superior to the Greedy Algorithm.

关键词： Wireless rechargeable sensor networks mixed-variable optimization Fireworks algorithm

来源：评论

学校读者我要写书评

暂无评论

A General Mathematical Framework for Constrained mixed-variable Blackbox optimization Problems with Meta and Categorical variables

引用

Operations Research Forum 2023年第1期4卷 12页

作者： Audet, Charles Hallé-Hannan, Edward Le Digabel, Sébastien GERAD and Department of Mathematics and Industrial Engineering Polytechnique Montréal Montreal Canada

A mathematical framework for modelling constrained mixed-variable optimization problems is presented in a blackbox optimization context. The framework introduces a new notation and allows solution strategies. The notation framework allows meta and categorical variables to be explicitly and efficiently modelled, which facilitates the solution of such problems. The new term meta variables is used to describe variables that influence which variables are included or excluded: meta variables may affect the number of variables and constraints. The flexibility of the solution strategies supports the main blackbox mixed-variable optimization approaches: direct search methods and surrogate-based methods (Bayesian optimization). The notation system and solution strategies are illustrated through an example of a hyperparameter optimization problem from the machine learning community. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.

关键词： Blackbox optimization Categorical variables Derivative-free optimization Meta variables mixed-variable optimization

来源：评论

学校读者我要写书评

暂无评论

Machine-Learning-Assisted Swarm Intelligence Algorithm for Antenna optimization With mixed Continuous and Binary variables

引用

IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2025年第3期73卷 1662-1673页

作者： Fu, Kai Leung, Kwok Wa City Univ Hong Kong State Key Lab Terahertz & Millimeter Wave Hong Kong Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong Peoples R China CityU Shenzhen Res Inst Informat & Commun Technol Ctr Shenzhen 518057 Peoples R China

Many existing surrogate-assisted optimization algorithms are limited to designing antennas with continuous variables only. However, numerous challenges emerge when tackling antenna optimization problems that involve both continuous and binary design variables. This article proposes an efficient surrogate-assisted mixed continuous/binary particle swarm optimization (SAMPSO) algorithm to address these mixed-variable antenna optimization problems. The SAMPSO tightly integrates machine learning (ML) models with PSO in two key aspects: an ML-guided swarm updating method and an ML-assisted prescreening strategy. In addition, a novel local ML model training method is developed to reduce the algorithm time complexity. To verify its effectiveness, the SAMPSO is compared with two existing algorithms in solving benchmark functions and designing antennas. The results demonstrate that SAMPSO can achieve design objectives with a faster convergence speed.

关键词： optimization Antennas Vectors Computational modeling Particle swarm optimization Heuristic algorithms optimization methods Accuracy Training Partitioning algorithms Antenna optimization mixed-variable optimization particle swarm optimization (PSO) surrogate-assisted evolutionary algorithm

来源：评论

学校读者我要写书评

暂无评论

mixed-variable Correlation-Aware Metaheuristic for Deployment optimization of 3-D Sensor Networks

Mixed-Variable Correlation-Aware Metaheuristic for Deploymen...

引用

Genetic and Evolutionary Computation Conference (GECCO)

作者： Wu, Tongyu Zhang, Yuntian Miao, Changhao Chen, Chen Ding, Shuxin Natl Key Lab Autonomous Intelligent Unmanned Syst Beijing 100081 Peoples R China Beijing Inst Technol Sch Automat Beijing 100081 Peoples R China China Acad Railway Sci Corp Ltd Signal & Commun Res Inst Beijing 100081 Peoples R China Natl Engn Res Ctr Syst Technol High Speed Railway Traff Management Lab High Speed Railway Beijing 100081 Peoples R China

ISBN: (纸本)9798400704949

Deployment optimization of 3-D sensor networks is essential for the overall cost of the system and the downstream tasks performance. The key of establishing realistic deployment is twofold: a high-fidelity mathematical programming model and an efficient algorithm for solving it. In this paper, we revisit the 3-D sensor networks deployment and present a mixed-variable optimization problem (MVOP) which jointly considers the discrete subset selection decision, continuous orientation decision, and decision-making under uncertainty. Based on the proposed real-world application, we innovatively design a mixed-variable correlation-aware genetic algorithm as the solver. Different from mainstream two-partition methods in MVOP, our algorithm captures the problemspecific features of deployment optimization and introduces a correlation-aware search paradigm which interactively updates the discrete and continuous decision variables. On the one hand, we update the discrete part (i.e., subset selection of candidate locations) first and then optimize the continuous part (i.e., sensor orientation parameters). On the other hand, we customize a heuristic mechanism to start with continuous part to identify the suitable discrete part. Experiments demonstrate that our approach can improve the performance of small-scale and large-scale scenarios of deployment by up to 55.7% and 56.4%, respectively, compared to state-of-the-art MVOP algorithms.

关键词： Deployment sensor networks metaheuristics mixed-variable optimization correlation-aware

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：