检索结果-内蒙古大学图书馆

Multiple optimal solutions in synthesis, design and operation optimization of a marine energy system with the differential evolution algorithm

引用

OCEAN ENGINEERING 2025年 328卷

作者： Dugast, Florian Benac, Stephane Marty, Pierre Chesse, Scal Nantes Univ Ecole Cent Nantes CNRS LHEEA UMR 6598 F-44000 Nantes France Technocampus Ocean Naval Grp CEMEP Bouguenais France

This article deals with the optimization of a ship energy system on multiple levels (synthesis, design and operation). These complex problems often induce many local optima, making it difficult to obtain reliable optimization results. A clustering method based on the absence or presence of components in the architecture coupled with an evolutionary algorithm (i.e Differential Evolution) is proposed to tackle this issue. The method enables multiple optimal solutions to be identified for a real-world optimization problem. The reference ship for this study is a destroyer but the use of the Admiralty coefficient in the model description makes the algorithm easily adaptable for any kind of ships. The specific fuel consumption is pre-calculated for groups of components to alleviate the computational cost of the optimization problem. Two numerical cases are computed representing the ship energy system with or without a heat recovery and a heat creation system to illustrate the capabilities of the method. The objective in both cases is to minimize the weight of the ship (fuel consumption + components weights) for a given mission profile. The influence of the clustering technique, population size and repeatability of the differential algorithm is also investigated to assess the reliability of the method.

关键词： Ship architecture design Synthesis optimization Design optimization evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

An enhanced tree-seed algorithm for global optimization and neural architecture search optimization in medical image segmentation

引用

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 104卷

作者： Qiao, Zenglin Wu, Lingyu Heidari, Ali Asghar Zhao, Xinchao Chen, Huiling Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Key Lab Math & Informat Networks Minist Educ Beijing Peoples R China Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran Iran Wenzhou Univ Dept Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

The spread of infectious diseases poses a threat to people's health. Screening and diagnosis using deep learning techniques can alleviate the pressure of the condition, especially medical image segmentation techniques, which can assist doctors inefficiently diagnosing and treating patients. However, most existing deep learning segmentation methods for medical images are mainly designed by experts based on their expertise. This paper proposes a novel wormhole and salp swarm strategy enhanced tree-seed algorithm (WSTSA). With its high efficiency, this algorithm could provide a sense of reassurance to the medical imaging field, instilling confidence in its potential. Secondly, WSTSA is integrated with a genetic algorithm to develop an automatic deep-learning neural architecture search model. Within this model, WSTSA optimizes hyperparameters during architecture search to enhance search accuracy, while the genetic algorithm explores the optimal convolutional neural network within a predefined search space. Finally, extensive experiments validate the performance of WSTSA and the proposed neural architecture search model. Statistical analyses demonstrate the superiority of WSTSA over existing state-of-the-art methods. Moreover, the neural architecture search model effectively discovers excellent neural networks for medical image segmentation.

关键词： evolutionary deep learning evolutionary algorithms Image segmentation Neural architecture search Tree-seed algorithm

来源：评论

学校读者我要写书评

暂无评论

Dynamic constrained multi-objective optimization algorithm based on co-evolution and diversity enhancement

引用

SWARM AND evolutionary COMPUTATION 2024年 89卷

作者： Che, Wang Zheng, Jinhua Hu, Yaru Zou, Juan Yang, Shengxiang Xiangtan Univ Sch Comp Sci Key Lab Intelligent Comp & Informat Proc Minist Educ Xiangtan Hunan Peoples R China Xiangtan Univ Sch Cyberspace Sci Xiangtan 411105 Hunan Peoples R China Xiangtan Univ Fac Sch Comp Sci Xiangtan 411105 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH England

Dynamic constrained multi-objective optimization problems (DCMOPs) involve objectives, constraints, and parameters that change over time. This kind of problem presents a greater challenge for evolutionary algorithms because it requires the population to quickly track the changing pareto-optimal set (PS) under constrained conditions while maintaining the feasibility and good distribution of the population. To address these challenges, this paper proposes a dynamic constrained multi-objective optimization algorithm based on co-evolution and diversity enhancement (CEDE), in which we have made improvements to both the static optimization and dynamic response parts, innovatively utilizing the valuable information latent in the optimization process to help the population evolve more comprehensively. The static optimization involves the co-evolution of three populations, through which their mutual synergy can more comprehensively identify potential true PS and provide more useful historical information for dynamic response. Additionally, to prevent the elimination of potentially valuable infeasible individuals (i.e., individuals that are not dominated by feasible individuals) due to pareto domination, we employ an archive set to store and update these individuals. When the environment changes, to effectively enhance population diversity under complex dynamic constraints and help the population to respond quickly to changes, we propose a diversity enhancement strategy, which includes a diversity maintenance strategy and a center point-based exploration strategy. This strategy effectively enhances population diversity in complex and changing environments, helping the population respond quickly to changes. The effectiveness of the algorithm is validated through two test sets. The experimental results show that CEDE can effectively use valuable information to cope with complex dynamic constraint environments. Compared with several of the most advanced algorithms, it is superio

关键词： Dynamic constrained multiobjective optimization evolutionary algorithms Co-evolution Change response strategy

来源：评论

学校读者我要写书评

暂无评论

Analysis of the (1+1) EA on LeadingOnes with Constraints

引用

ALGORITHMICA 2025年第5期87卷 661-689页

作者： Friedrich, Tobias Koetzing, Timo Neumann, Aneta Neumann, Frank Radhakrishnan, Aishwarya Univ Potsdam Hasso Plattner Inst Algorithm Engn Potsdam Germany Univ Adelaide Optimisat & Logist Adelaide Australia

Understanding how evolutionary algorithms perform on constrained problems has gained increasing attention in recent years. In this paper, we study how evolutionary algorithms optimize constrained versions of the classical LeadingOnes problem. We first provide a run time analysis for the classical (1+1) EA on the LeadingOnes problem with a deterministic cardinality constraint, giving Theta(n(n-B)log(B)+nB)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\Theta (n (n-B)\log (B) + nB)$$\end{document} as the tight bound. Our results show that the behaviour of the algorithm is highly dependent on the constraint bound of the uniform constraint. Afterwards, we consider the problem in the context of stochastic constraints and provide insights using theoretical and experimental studies on how the (mu\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mu $$\end{document}+1) EA is able to deal with these constraints in a sampling-based setting.

关键词： evolutionary algorithms Chance constraint optimization Run time analysis Theory

来源：评论

学校读者我要写书评

暂无评论

Exploring Metaheuristic Optimization algorithms in the Context of Textual Cyberharassment: A Systematic Review

引用

EXPERT SYSTEMS 2025年第2期42卷

作者： Shannaq, Fatima Shehab, Mohammad Alshorman, Areej Hammad, Mahmoud Hammo, Bassam Al-Omari, Wala'a Prince Sultan Univ Coll Comp & Informat Sci Riyadh Saudi Arabia Amman Arab Univ Coll Informat Technol Amman Jordan Al al Bayt Univ Fac Prince Al Hussein Bin Abdallah II IT Mafraq Jordan Jordan Univ Sci & Technol Fac Comp & Informat technol Irbid Jordan Univ Jordan King Abdullah II Sch Informat Technol Amman Jordan Princess Sumaya Univ Technol King Hussein Sch Comp Sci Amman Jordan

The digital landscape and rapid advancement of Information and Communication Technology have significantly increased social interactions, but it has also led to a rise in harmful behaviours such as offensive language, cyberbullying, and HS. Addressing online harassment is critical due to its severe consequences. This study offers a comprehensive evaluation of existing studies that employed metaheuristic optimization algorithms for detecting textual harassment content across social media platforms, highlighting their strengths and limitations. Using the PRISMA methodology, we reviewed and analysed 271 research papers, ultimately narrowing down the selection to 36 papers based on specific inclusion and exclusion criteria. By analysing key factors such as optimization techniques, feature engineering strategies, and dataset characteristics, we identify crucial trends and challenges in the field. Finally, we offer practical recommendations to improve the accuracy of predictive models, including adopting hybrid approaches, enhancing multilingual capabilities, and expanding models to operate effectively across various social media platforms.

关键词： abusive language cyberbullying evolutionary algorithms meta-heauristic algorithms offensive language online harassment

来源：评论

学校读者我要写书评

暂无评论

GeQuPI: : Quantum Program Improvement with Multi-Objective Genetic Programming☆

引用

JOURNAL OF SYSTEMS AND SOFTWARE 2025年 219卷

作者： Gemeinhardt, Felix Klikovits, Stefan Wimmer, Manuel Johannes Kepler Univ Linz Inst Business Informat Software Engn Altenberger Str 69 A-4040 Linz Austria

Processing quantum information poses novel challenges regarding the debugging of faulty quantum programs. Notably, the lack of accessible information on intermediate states during quantum processing, renders traditional debugging techniques infeasible. Moreover, even correct quantum programs might not be processable, as current quantum computers are limited in computation capacity. Thus, quantum program developers have to consider trade-offs between accuracy (i.e., probabilistically correct functionality) and computational cost of the proposed solutions. Manually finding sufficiently accurate and efficient solutions is a challenging task, even for quantum computing experts. To tackle these challenges, we propose a quantum program improvement framework for an automated generation of accurate and efficient solutions, coined Genetic Quantum Program Improver (GeQuPI). In particular, we focus on the tasks of debugging and optimization of quantum programs. Our framework uses techniques from quantum information theory and applies multi-objective genetic programming, which can be further hybridized with quantum-aware optimizers. To demonstrate the benefits of GeQuPI, it is applied to 47 quantum programs reused from literature and openly published libraries. The results show that our approach is capable of correcting faulty programs and optimize inefficient ones for the majority of the studied cases, showing average optimizations of 35% with respect to computational cost.

关键词： Quantum computing evolutionary algorithms Quantum software engineering Quantum circuit optimization

来源：评论

学校读者我要写书评

暂无评论

An Adversarial Approach for Automated Pokemon Team Building and Metagame Balance

引用

IEEE TRANSACTIONS ON GAMES 2024年第2期16卷 365-375页

作者： Reis, Simao Novais, Rita Reis, Luis Paulo Lau, Nuno Univ Porto Fac Engn Artificial Intelligence & Comp Sci Lab P-4099002 Porto Portugal Univ Aveiro Inst Elect & Informat Engn Aveiro P-3810193 Aveiro Portugal

Metagame balance is a crucial task in game development, and automation of this process could assist game developers by vastly reducing time costs. We explore and evaluate a metagame balance model over the recently proposed VGC AI Competition Framework. We propose an adversarial model where team builder agents try to maximize their win rate by narrowing to the most optimal team configurations, resulting in a reduction of the diversity of Pok & eacute;mon employed, while a balancing agent readapts the Pok & eacute;mon inner attributes to incentivize the team builder agents to incorporate a greater variety of Pok & eacute;mon into their teams increasing the metagame's overall diversity and balance. Furthermore, we develop multiple team builder agents divided into two groups: the first group assumes that individual Pok & eacute;mon advantages are the primary factor to determine the outcome of game matches;the second group also exploits the implicit synergy between teammates. These agents make use of metagaming, linear optimization, and evolutionary search to find strong combinations against the current metagame. The strongest team builder is faced against the team metagame balance agent for its evaluation. Deep learning is also employed to predict the outcome of matches and recommend constructive elements of teams.

关键词： Games Artificial intelligence Genetic algorithms Optimization Deep learning Buildings Training Automatic game design competitive games deep reinforcement learning evolutionary algorithms game balance game theory optimization

来源：评论

学校读者我要写书评

暂无评论

A novel differential evolution algorithm based on periodic intervention and systematic regulation mechanisms

引用

APPLIED INTELLIGENCE 2024年第22期54卷 11779-11803页

作者： Yuan, Guanyu Sun, Gaoji Deng, Libao Li, Chunlei Yang, Guoqing Harbin Inst Technol Sch Informat Sci & Engn Weihai 264209 Peoples R China Zhejiang Normal Univ Coll Econ & Management Jinhua 321004 Peoples R China Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Peoples R China Hebei Univ Sch Management Baoding 071002 Peoples R China

Differential evolution (DE) has attracted widespread attention due to its outstanding optimization performance and ease of operation, but it cannot avoid the dilemmas of premature convergence or stagnation when faced with complex optimization problems. To reduce the probability of such difficulties for DE, we sort out the factors that influence the balance between global exploration and local exploitation in the DE algorithm, and we design a novel DE variant (abbreviated as PISRDE) by integrating the corresponding influence factors through a periodic intervention mechanism and a systematic regulation mechanism. The periodic intervention mechanism divides the optimization operations of PISRDE into routine operation and intervention operation, and it balances global exploration and local exploitation at the macro level by executing the two operations alternately. The systematic regulation mechanism treats the involved optimization strategies and parameter settings as an organic system for targeted design, to balance global exploration and local exploitation at the meso or micro level. To evaluate and verify the optimization performance of PISRDE, we employ seven DE variants with excellent optimization performance to conduct comparison experiments on the IEEE CEC 2014 and IEEE CEC 2017 benchmarks. The comparison results indicate that PISRDE outperforms all competitors overall, and its relative advantage is even more significant when dealing with high-dimensional and complex optimization *** abstractSchematic design philosophy of PISRDE

关键词： Differential evolution Metaheuristic algorithms evolutionary algorithms Periodic intervention Systematic regulation

来源：评论

学校读者我要写书评

暂无评论

Ecological Flow Analysis through an Ecohydraulic-Based Catchment Scale Approach

引用

WATER 2024年第1期16卷 59页

作者： Sedighkia, Mahdi Datta, Bithin Australian Natl Univ ICEDS & MSI Canberra ACT 2601 Australia James Cook Univ Coll Sci & Engn Townsville Qld 4811 Australia

Ecological flow regime analysis through developing a novel ecohydraulic optimization method is the objective of this study in which three components are linked. Hydrological analysis is the first component in which average monthly flow is assessed in different hydrological conditions by applying a drought index in the selected control points or representative reaches in the river basin. Another component is the ecological model in which field ecological studies are used for selecting the target species, and habitat loss was modelled through the fuzzy method. The outputs of the hydrological analysis and hydraulic habitat simulation were then applied in the structure of the optimization model in which minimizing ecological impacts and water supply loss were defined as the purposes. Different evolutionary algorithms were used in the optimization process. A decision-making system was utilized to finalize ecological flow by selecting the privileged algorithm. According to the outputs, the proposed method can mitigate ecological impacts and water supply losses simultaneously. Either particle swarm optimization or differential evolution algorithm is the best approach for ecological flow in this research work. The outputs of optimization indicated that the reliability of the water supply in dry years is less than 32%, while it is more than 80% in wet years, which means that changing the hydrological condition will increase the portion of ecological flow regime significantly. In other words, the reliability of the water supply can be reduced by more than 50%. Hence, using other water resources such as groundwater is necessary in dry years in the study area.

关键词： environmental flow habitat simulation stream drought index fuzzy method evolutionary algorithms

来源：评论

学校读者我要写书评

暂无评论

evolutionary reinforcement learning via cooperative coevolutionary negatively correlated search

引用

SWARM AND evolutionary COMPUTATION 2022年第0期68卷 100974-100974页

作者： Yang, Peng Zhang, Hu Yu, Yanglong Li, Mingjia Tang, Ke Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China Beijing Electromech Engn Inst Sci & Technol Complex Syst Control & Intelligent Beijing 100074 Peoples R China

evolutionary algorithms (EAs) have been successfully applied to optimize the policies for Reinforcement Learning (RL) tasks due to their exploration ability. The recently proposed Negatively Correlated Search (NCS) provides a distinct parallel exploration search behavior and is expected to facilitate RL more effectively. Considering that the commonly adopted neural policies usually involves millions of parameters to be optimized, the direct application of NCS to RL may face a great challenge of the large-scale search space. To address this issue, this paper presents an NCS-friendly Cooperative Coevolution (CC) framework to scale-up NCS while largely preserving its parallel exploration search behavior. The issue of traditional CC that can deteriorate NCS is also discussed. Empirical studies on 10 popular Atari games show that the proposed method can significantly outperform three state-ofthe-art deep RL methods with 50% less computational time by effectively exploring a 1.7 million-dimensional search space.

关键词： evolutionary algorithms Deep reinforcement learning Cooperative coevolution

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：