检索结果-内蒙古大学图书馆

evolutionary Multitasking With Centralized Learning for Large-Scale Combinatorial Multiobjective Optimization

IEEE TRANSACTIONS ON evolutionary computation 2024年第5期28卷 1499-1513页

作者： Huang, Yuxiao Zhou, Wei Wang, Yu Li, Min Feng, Liang Tan, Kay Chen Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Jing Dong Retail Dept User Growth & Operat Beijing 100176 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

evolutionary multitasking (EMT) has attracted much attention in the community of evolutionary computation recently. It intends to improve the performance of evolutionary optimization on multiple problems via knowledge learning and transfer across them while the optimization processes progress online. Existing EMT paradigms can be classified as explicit EMT (EEMT) and implicit EMT (IEMT) according to the mechanisms adopted in the knowledge transfer. With additional knowledge learning and transfer modules, the EEMT often brings flexible algorithmic designs and effective knowledge transfer against the IEMT. However, most of the existing EEMT studies are designed for continuous optimization problems. Due to the difficulty of learning problem-specific mappings across combinatorial optimization problems, EEMT for combinatorial optimization is still in the nascent stage. Furthermore, it is worth noting that, with the growing number of tasks in today's real-world applications and the enlarged number of decision variables in each of the tasks, learning mappings across tasks becomes more challenging. Keeping the above in mind, this article presents a novel EEMT algorithm with centralized learning for solving the large-scale and multiobjective combinatorial optimization in many-task manner, in which knowledge transfer across tasks is conducted based on a centralized learning model, instead of task-specific mappings which are required in existing EEMT studies. To investigate the performance of the proposed centralized learning assisted EEMT, comprehensive empirical studies have been conducted on the large-scale and multiobjective knapsack problems. Finally, the efficacy of our proposed method is further validated on a real-world combinatorial optimization application.

关键词： Task analysis Optimization Knowledge transfer Multitasking Search problems Companies evolutionary computation Combinatorial optimization evolutionary multitasking (EMT) large-scale optimization

来源：评论

学校读者我要写书评

暂无评论

A forecasting-based approach for optimal deployment of edge servers in 5G networks

引用

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第5期27卷 5721-5739页

作者： Tiwari, Vaibhav Pandey, Chandrasen Roy, Diptendu Sinha Natl Inst Technol Dept Comp Sci & Engn Shillong Meghalaya India

The growing need for applications with low latency and high bandwidth in 5G networks has shifted the emphasis towards integrating edge computing into mobile network architectures. The optimal deployment of edge servers (ESs) is crucial for balancing performance and cost. Current research primarily focuses on the optimal deployment of ESs. However, there is limited attention on forecasting the workload of ESs, particularly in scenarios where they are placed in their optimal positions, which is crucial for accommodating anticipated variations in user densities and workload fluctuations expected in the near future. This research forecasting of workload in 5G networks by leveraging the Temporal Hierarchical Attention Mechanism Network (THAMNET) model for accurate Internet traffic forecasting. Addressing this gap, the Max-Min Fairness Allocation Scheme (MMF-AS) algorithm is implemented to ensure a balanced workload distribution. This approach facilitates fair allocation of Base Stations (BSs) to ESs, taking into account factors such as workload, distance, and connectivity. A novel approach is introduced by integrating the THAMNET forecasting model with the Particle Swarm Optimization (PSO) and MMF-AS algorithms. The proposed method considers predicted future traffic demands, and connectivity between ESs and BSs, and aims to achieve balanced workload distribution, higher utilisation rates, minimised latency, and reduced energy consumption. Experimental results demonstrate equitable workload distribution 25.95%, utilisation rate 17.57%, and notable reductions in latency 47.61% and energy consumption 32.28% compared to the non-forecasting comparison.

关键词： Edge server placement (ESP) evolutionary computation Mobile edge computing (MEC) 5G Energy consumption

来源：评论

学校读者我要写书评

暂无评论

evolutionary Multimodal Multiobjective Optimization for Traveling Salesman Problems

引用

IEEE TRANSACTIONS ON evolutionary computation 2024年第2期28卷 516-530页

作者： Liu, Yiping Xu, Liting Han, Yuyan Zeng, Xiangxiang Yen, Gary G. Ishibuchi, Hisao Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Liaocheng Univ Sch Comp Sci Liaocheng 252059 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

Multimodal multiobjective optimization problems (MMOPs) are commonly seen in real-world applications. Many evolutionary algorithms have been proposed to solve continuous MMOPs. However, little effort has been made to solve combinatorial (or discrete) MMOPs. Searching for equivalent Pareto-optimal solutions in the discrete decision space is challenging. Moreover, the true Pareto-optimal solutions of a combinatorial MMOP are usually difficult to know, which has limited the development of its optimizer. In this article, we first propose a test problem generator for multimodal multiobjective traveling salesman problems (MMTSPs). It can readily generate MMTSPs with known Pareto-optimal solutions. Then, we propose a novel evolutionary algorithm to solve MMTSPs. In our proposed algorithm, we develop two new edge assembly crossover operators, which are specialized in searching for superior solutions to MMTSPs. Moreover, the proposed algorithm uses a new environmental selection operator to maintain a good balance between the objective space diversity and decision space diversity. We compare our algorithm with five state-of-the-art designs. Experimental results convincingly show that our algorithm is powerful in solving MMTSPs.

关键词： Pareto optimization Optimization evolutionary computation Generators Traveling salesman problems Search problems Approximation algorithms Combinatorial optimization evolutionary multimodal multiobjective optimization test problems traveling salesman problem (TSP)

来源：评论

学校读者我要写书评

暂无评论

Bi-Trajectory Hybrid Search to Solve Bottleneck-Minimized Colored Traveling Salesman Problems

引用

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第1期21卷 895-905页

作者： Zhou, Yangming Xu, Wenqiang Zhou, Mengchu Fu, Zhang-Hua Shanghai Jiao Tong Univ Data Driven Management Decis Making Lab Shanghai 200030 Peoples R China Shanghai Jiao Tong Univ Sino US Global Logist Inst Antai Coll Econ & Management Shanghai 200030 Peoples R China East China Univ Sci & Technol Dept Comp Sci & Engn Shanghai 200237 Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Chinese Univ Hong Kong Shenzhen Inst Robot & Intelligent Mfg Shenzhen 518172 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518172 Peoples R China

A bottleneck-minimized colored traveling salesman problem is an important variant of colored traveling salesman problems. It is useful in handling the planning problems with partially overlapped workspace such as the scheduling transportation resources for timely delivery of goods. In this work, we propose an efficient bi-trajectory hybrid search method for it. The proposed method integrates a route-based crossover operator to generate promising offspring solutions, a multi-neighborhood simulated annealing to perform local optimization, and a stagnation-detect-escape mechanism to help the search escape from local optima. We also propose a bidirectional adjacency solution representation method to encode a solution, which extends the traditional adjacency representation method by additionally employing an array to represent its reverse counterpart. Extensive evaluations on two sets of 58 widely used benchmark instances demonstrate that the proposed method significantly outperforms state-of-the-art algorithms. Investigations on key algorithm modules are performed to confirm the novelty and effectiveness of the proposed ideas and strategies. Finally, we verify the generalization of the proposed method via its use to solve a colored traveling salesman problem with its aim to balance workload among salesmen. Note to Practitioners-This work is motivated by the problems of scheduling transportation resources for timely delivery of goods. It proposes an effective bi-trajectory hybrid search to tackle bottleneck-minimized colored traveling salesman problem. The proposed approach performs bi-trajectory hybrid evolutionary search by maintaining only two individuals during the whole search process. Extensive numerical experiments and comparisons show that our proposed algorithm significantly outperforms state-of-the-art algorithms and can help decision-makers in designing best-routing solutions.

关键词： Traveling salesman problem bottleneck metaheuristic evolutionary computation memetic search

来源：评论

学校读者我要写书评

暂无评论

Automatic Software Tailoring for Optimal Performance

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING

引用

IEEE TRANSACTIONS ON SUSTAINABLE COMPUTING 2024年第3期9卷 464-481页

作者： Aragon-Jurado, Jose Miguel de la Torre, Juan Carlos Ruiz, Patricia Galindo, Pedro L. Zomaya, Albert Y. Dorronsoro, Bernabe Univ Cadiz Cadiz 11003 Spain Univ Cadiz Intelligent Syst Grp Cadiz 11003 Spain Univ Sydney Ctr Distributed & High Performance Comp Camperdown NSW 2050 Australia Univ Sydney Camperdown NSW 2050 Australia

Efficient green software solutions require being aware of the characteristics of both the software and the hardware where it is executed. Separately optimizing them leads to inefficient results, and there is a need for a perfect synergy between software and hardware for optimal outcomes. We present a novel combinatorial optimization problem for the minimization of the software execution time on a specific hardware, taking into account the existing uncertainty in the system. A solution to the problem is a sequence of LLVM code transformations, and a cellular genetic algorithm is used to find it. Assuming that hardware does not change, reducing the software runtime typically leads to a greener version with lower consumption. To cope with the uncertainty, two novel approaches relying on bootstrap method to compute confident intervals of the software runtime at negligible cost are proposed and compared to three other techniques and -O3 Clang compilation flag over four hardware architectures. Results show how the proposed approach effectively copes with the uncertainty, providing more robust solutions with respect to the compared methods. The execution time of the raw program is reduced from 28.1% to up to 63.2%, outperforming -O3 flag by 13.9% to 26.3%, for the different architectures.

关键词： Runtime Optimization Codes Uncertainty Semantics Performance evaluation Computer architecture evolutionary computation execution time LLVM software performance source code transformation uncertainty

来源：评论

学校读者我要写书评

暂无评论

Runtime Analysis of Competitive Co-evolutionary Algorithms for Maximin Optimisation of a Bilinear Function

引用

ALGORITHMICA 2024年第7期86卷 2352-2392页

作者： Lehre, Per Kristian Univ Birmingham Sch Comp Sci Birmingham England

Co-evolutionary algorithms have a wide range of applications, such as in hardware design, evolution of strategies for board games, and patching software bugs. However, these algorithms are poorly understood and applications are often limited by pathological behaviour, such as loss of gradient, relative over-generalisation, and mediocre objective stasis. It is an open challenge to develop a theory that can predict when co-evolutionary algorithms find solutions efficiently and reliable. This paper provides a first step in developing runtime analysis for population-based competitive co-evolutionary algorithms. We provide a mathematical framework for describing and reasoning about the performance of co-evolutionary processes. To illustrate the framework, we introduce a population-based co-evolutionary algorithm called PDCoEA, and prove that it obtains a solution to a bilinear maximin optimisation problem in expected polynomial time. Finally, we describe settings where PDCoEA needs exponential time with overwhelmingly high probability to obtain a solution.

关键词： Co-evolutionary algorithms Runtime analysis evolutionary computation Game theory

来源：评论

学校读者我要写书评

暂无评论

Optimization of Public Transport Networks by Considering Alternative Positions for Network Stations

引用

IEEE LATIN AMERICA TRANSACTIONS 2024年第6期22卷 468-474页

作者： Montarroyos, Ingo P. P. Araujo, Danilo R. B. Univ Fed Rural Pernambuco Recife PE Brazil

Netheradays planning of metropolitan areas considers to improve quality of life of their inhabitants and urbanmobility is one of the main concerns. Studies point out that investments in public transportation and other modes are aimedat the overall improvement of mobility. However, there is a gap in proper tools for optimizing public transport *** fact, network optimization is an NP-Hard problem and there are usually many conflicting objectives that need to beoptimized simultaneously. This paper proposes the use of manyobjective evolutionary algorithms to address the problem ofpublic transport networks optimization, focusing on metropolitan bus lines. The proposal consists in optimizing the position of bus stops and consequently obtaining new routes that pass through these stops in order to minimize the average travel time, the time spent between origin / destination and the variance of distance between the stops. To evaluate our proposal, a simulator was used to simulate the behavior of different passenger profiles in an urban area and the results were compared between the lines obtained by the optimization process and existing bus lines in the city of Sao Paulo. According to our results, optimized bus routes have mean travel time 22% less than the existing route and the time spent between origin/destination has decreased up to 18%.

关键词： Optimization Web services Sorting Satellite images Photography Urban areas Genetic algorithms Public Transport Networks Multiobjective Optimization evolutionary computation

来源：评论

学校读者我要写书评

暂无评论

Damping-Assisted evolutionary Swarm Intelligence for Industrial IoT Task Scheduling in Cloud Computing

引用

IEEE INTERNET OF THINGS JOURNAL 2024年第1期11卷 1698-1710页

作者： Gad, Ahmed G. Houssein, Essam H. Zhou, MengChu Suganthan, Ponnuthurai Nagaratnam Wazery, Yaser M. Kafrelsheikh Univ Fac Computers & Informat Kafrelsheikh 33516 Egypt Minia Univ Fac Computers & Informat Al Minya 61519 Egypt Macau Univ Sci & Technol Macao Inst Syst Engn Macau Peoples R China Macau Univ Sci & Technol Collaborat Lab Intelligent Sci & Syst Macau Peoples R China New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Qatar Univ KINDI Ctr Comp Res Coll Engn Doha Qatar

Advancements in the Industrial Internet of Things (IIoT) have yielded massive volumes of data, taxing the capabilities of cloud computing infrastructure. Allocating limited computing resources to numerous incoming requests is crucial for cloud computing and referred to as a task-scheduling-in-cloud-computing (TSCC) problem. In order to ameliorate the performance of a particle swarm optimizer (PSO) and broaden its application to TSCC, this article introduces an opposition-based simulated annealing particle swarm optimizer (OSAPSO) to address PSO's premature convergence issue, particularly when tackling high-dimensional complex problems like TSCC. OSAPSO is a novel combination of opposition-based learning (OBL), evolution strategy, simulated annealing (SA), and swarm intelligence. At its initial stage, a swarm is formed at random by using OBL to guarantee its diversity with a light computational burden. A multiway tournament selection approach is then utilized to pick parents to produce a new offspring swarm by using two novel evolutionary operators, namely, damping-based mutation and inversion-scrambling-based crossover. OSAPSO is given a powerful exploration capacity by adopting the survivor probabilistic selection of SA, which accepts subpar solutions with a certain probability. Finally, PSO itself kicks in, making a good tradeoff between solution diversity and convergence speed of OSAPSO. Due to the nonconvex discontinuous nature of TSCC, OSAPSO is modified to clone it into a discrete optimization problem. Within a heterogeneous cloud computing environment, OSAPSO and eight well-regarded competitors are examined on a set of multiscale IIoT heterogeneous task groups. In terms of power consumption, monetary cost, service makespan, and system throughput, experimental results reveal that OSAPSO beats its peers in IIoT task scheduling of cloud systems.

关键词： Cloud task scheduling evolutionary computation Industrial Internet of Things (IIoT) particle swarm optimizer (PSO) power consumption simulated annealing (SA) swarm intelligence (SI) system throughput

来源：评论

学校读者我要写书评

暂无评论

Evolving interpretable neural modularity in free-form multilayer perceptrons through connection costs

引用

NEURAL COMPUTING & APPLICATIONS 2024年第3期36卷 1459-1476页

作者： van der Merwe, Andreas Werle Vandenheever, David Stellenbosch Univ Biomed Engn Res Grp Stellenbosch South Africa Mississippi State Univ Agr & Biol Engn Dept Neural Engn Res Div Mississippi State MS 39762 USA

Interpretability fosters trust when humans and artificial intelligence (AI) systems interact, and its value for neural networks in particular cannot be overstated. In this paper, we analyse the emergence and value of neural modularity as it relates to interpretability. We compare the modularity evolved through connectivity constraints in terms of network Q-scores and examine the interpretable qualities of the resultant networks with functional subset regression. The connectivity constraints compared here include those proposed by previous research as well as several novel variations formulated to express neuron input competition and connections per neuron variance. Networks were evolved using HyperNEAT on a free-form substrate. The results indicate that the connection costs successfully promote the evolution of neural modularity across a variety of tasks and show that the novel connection cost variations are competitive with previously explored connection costs. The interpretability assessment shows that while the evolved networks' interpretable qualities are task dependent, two of the compared connection costs deliver statistically different functional module overlap distributions. However, recovered subnetwork module accuracies remain low, highlighting the key points for future research.

关键词： Modularity Interpretability evolutionary computation Neuroevolution Fitness functions Connectivity constraints Multilayer perceptrons Free-form General substrates q-scores Graph metrics

来源：评论

学校读者我要写书评

暂无评论

Solution Transfer in evolutionary Optimization: An Empirical Study on Sequential Transfer

引用

IEEE TRANSACTIONS ON evolutionary computation 2024年第6期28卷 1776-1793页

作者： Xue, Xiaoming Yang, Cuie Feng, Liang Zhang, Kai Song, Linqi Tan, Kay Chen City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China City Univ Hong Kong Shenzhen Res Inst Shenzhen 518057 Peoples R China Northeastern Univ State Key Lab Synthet Automat Proc Ind Shenyang 110819 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Qingdao Univ Technol Civil Engn Sch Qingdao 266520 Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

Knowledge transfer from optimized problems has emerged as a promising technique for enhancing evolutionary search. However, most studies in this domain primarily concentrate on devising knowledge transfer mechanisms for specific problem domains, often lacking the examination of the fundamental aspects of knowledge transfer, i.e., what, when, and how to transfer across diverse scenarios. This not only restricts the generality of these algorithms but also hinders their practical applicability. In light of this, this article: 1) reviews a vast array of techniques associated with the crucial aspects of solution transfer and 2) conducts a series of experiments to explore the underlying transfer mechanisms that enhance the evolutionary search. In particular, we first define solution transferability in the context of evolutionary search, which provides a new perspective in understanding what, when, and how to transfer in enhancing evolutionary search. Next, through comprehensive experiments, we find that the approximation and evaluation of solution transferability are of great importance in designing what, when, and how to transfer toward enhanced evolutionary search. Furthermore, our empirical study also discusses the counterintuitive performance improvements unrelated to the search experience of source tasks. The source code for reproducing our experiments is available at https://***/XmingHsueh/ STO-EC.

关键词： Task analysis Optimization Knowledge transfer Timing Measurement evolutionary computation Urban areas How to transfer transfer optimization transferability what to transfer when to transfer

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：