检索结果-内蒙古大学图书馆

A review of clonal selection algorithm and its applications

ARTIFICIAL INTELLIGENCE REVIEW 2011年第2期36卷 117-138页

作者： Ulutas, Berna Haktanirlar Kulturel-Konak, Sadan Eskisehir Osmangazi Univ Dept Ind Engn TR-26480 Eskisehir Turkey Penn State Berks Management Informat Syst Reading PA 19610 USA

Recently, clonal selection theory in the immune system has received the attention of researchers and given them inspiration to create algorithms that evolve candidate solutions by means of selection, cloning, and mutation procedures. Moreover, diversity in the population is enabled by means of the receptor editing process. The Clonal Selection Algorithm (CSA) in its canonical form and its various versions are used to solve different types of problems and are reported to perform better compared with other heuristics (i.e., genetic algorithms, neural networks, etc.) in some cases, such as function optimization and pattern recognition. Although the studies related with CSA are increasingly popular, according to our best knowledge, there is no study summarizing the basic features of these algorithms, hybrid algorithms, and the application areas of these algorithms all in one paper. Therefore, this study aims to summarize the powerful characteristics and general review of CSA. In addition, CSA based hybrid algorithms are reviewed, and open research areas are discussed for further research.

关键词： Artificial immune systems Clonal selection algorithms Review hybrid algorithms

来源：评论

学校读者我要写书评

暂无评论

Ant algorithms and simulated annealing for multicriteria dynamic programming

引用

COMPUTERS & OPERATIONS RESEARCH 2009年第2期36卷 433-441页

作者： Sitarz, Sebastian Silesian Univ Inst Math PL-40007 Katowice Poland

The paper presents a comparison of ant algorithms and simulated annealing as well as their applications in multicriteria discrete dynamic programming. The considered dynamic process consists of finite states and decision variables. In order to describe the effectiveness of multicriteria algorithms, four measures of the quality of the nondominated set approximations are used. (C) 2007 Elsevier Ltd. All rights reserved.

关键词： Multicriteria programming Dynamic programming Ant colony optimization Simulated annealing hybrid algorithms

来源：评论

学校读者我要写书评

暂无评论

Opposition-Based hybrid Strategy for Particle Swarm Optimization in Noisy Environments

引用

IEEE ACCESS 2018年 6卷 21888-21900页

作者： Kang, Qi Xiong, Caifei Zhou, Mengchu Meng, Lingpeng Tongji Univ Sch Elect & Informat Engn Dept Control Sci & Engn Shanghai 201804 Peoples R China New Jersey Inst Technol Helen & John C Hartmann Dept Elect & Comp Engn Newark NJ 07102 USA King Abdulaziz Univ Renewable Energy Res Grp Jeddah 21589 Saudi Arabia Shanghai Maritime Univ China Inst FTZ Supply Chain Shanghai 201306 Peoples R China

Particle swarm optimization (PSO) is a population-based algorithm designed to tackle various optimization problems. However, its performance deteriorates significantly when optimization problems are subjected to noise. PSO is strongly influenced by its previous best particles and global best one, which may lead to premature convergence and fall into local optima. This also holds true for various PSO variants dealing with optimization problems in noisy environments. Opposition-based learning (OBL) is well-known for its ability to increase population diversity. In this paper, we propose hybrid PSO algorithms that introduce OBL into PSO variants for improving the latter's performance. The proposed hybrid algorithms employ probabilistic OBL for a swarm. In contrast to other integrations of PSO and OBL, we select the top fittest particles from the current swarm and its opposite swarm to improve the entire swarm's fitness. Experiments on 20 benchmark functions subject to different levels of noise show that the proposed hybrid PSO algorithms outperform their counterpart PSO variants as well as composite differential evolution in most cases.

关键词： Particle swarm optimization opposition-based learning noisy environments hybrid algorithms

来源：评论

学校读者我要写书评

暂无评论

Identifying Market Behaviours Using European Stock Index Time Series by a hybrid Segmentation Algorithm

引用

NEURAL PROCESSING LETTERS 2017年第3期46卷 767-790页

作者： Duran-Rosal, Antonio M. de la Paz-Marin, Monica Gutierrez, Pedro A. Hervas-Martinez, Cesar Univ Cordoba Dept Comp Sci & Numer Anal Rabanales CampusAlbert Einstein Bldg E-14071 Cordoba Spain

The discovery of useful patterns embodied in a time series is of fundamental relevance in many real applications. Repetitive structures and common type of segments can also provide very useful information of patterns in financial time series. In this paper, we introduce a time series segmentation and characterization methodology combining a hybrid genetic algorithm and a clustering technique to automatically group common patterns from this kind of financial time series and address the problem of identifying stock market prices trends. This hybrid genetic algorithm includes a local search method aimed to improve the quality of the final solution. The local search algorithm is based on maximizing a likelihood ratio, assuming normality for the series and the subseries in which the original one is segmented. To do so, we select two stock market index time series: IBEX35 Spanish index (closing prices) and a weighted average time series of the IBEX35 (Spanish), BEL20 (Belgian), CAC40 (French) and DAX (German) indexes. These are processed to obtain segments that are mapped into a five dimensional space composed of five statistical measures, with the purpose of grouping them according to their statistical properties. Experimental results show that it is possible to discover homogeneous patterns in both time series.

关键词： Time series segmentation hybrid algorithms Clustering European stock market indexes

来源：评论

学校读者我要写书评

暂无评论

A memetic algorithm for the cost-oriented robotic assembly line balancing problem

引用

COMPUTERS & OPERATIONS RESEARCH 2018年 99卷 249-261页

作者： Pereira, Jordi Ritt, Marcus Vasquez, Oscar C. Univ Adolfo Ibanez Fac Engn & Sci Av Padre Hurtado 750Off C216 Vina Del Mar Chile Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil Univ Santiago Chile Ind Engn Dept Santiago Chile

In order to minimize costs, manufacturing companies have been relying on assembly lines for the mass production of commodity goods. Among other issues, the successful operation of an assembly line requires balancing work among the stations of the line in order to maximize its efficiency, a problem known in the literature as the assembly line balancing problem, ALBP. In this work, we consider an ALBP in which task assignment and equipment decisions are jointly considered, a problem that has been denoted as the robotic ALBP. Moreover, we focus on the case in which equipment has different costs, leading to a cost-oriented formulation. In order to solve the problem, which we denote as the cost-oriented robotic assembly line balancing problem, cRALBP, a hybrid metaheuristic is proposed. The metaheuristic embeds results obtained for two special cases of the problem within a genetic algorithm in order to obtain a memetic algorithm, applicable to the general problem. An extensive computational experiment shows the advantages of the hybrid approach and how each of the components of the algorithm contributes to the overall ability of the method to obtain good solutions. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Line balancing Cost-oriented line balancing Robotic assembly line hybrid algorithms

来源：评论

学校读者我要写书评

暂无评论

A novel quantum inspired cuckoo search for knapsack problems

引用

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2011年第5期3卷 297-305页

作者： Layeb, Abdesslem Mentouri Univ Constantine Dept Comp Sci MISC Lab Constantine 25017 Algeria

This paper presents a new inspired algorithm called quantum inspired cuckoo search algorithm (QICSA). This one is a new framework relying on quantum computing principles and cuckoo search algorithm. The contribution consists in defining an appropriate representation scheme in the cuckoo search algorithm that allows applying successfully on combinatorial optimisation problems some quantum computing principles like qubit representation, superposition of states, measurement, and interference. This hybridisation between quantum inspired computing and bioinspircd computing has led to an efficient hybrid framework which achieves better balance between exploration and exploitation capabilities of the search process. Experiments on knapsack problems show the effectiveness of the proposed framework and its ability to achieve good quality solutions.

关键词： cuckoo search algorithm quantum computing QC hybrid algorithms

来源：评论

学校读者我要写书评

暂无评论

A hybrid genetic algorithm for multi-objective product plan selection problem with ASP and ALB

引用

EXPERT SYSTEMS WITH APPLICATIONS 2012年第5期39卷 5440-5450页

作者： Wang, H. S. Che, Z. H. Chiang, C. J. Natl Taipei Univ Technol Dept Ind Engn & Management Taipei 106 Taiwan

Facing current environment full of a variety of small quantity customized requests, enterprises must provide diversified products for speedy and effective responses to customers' requests. Among multiple plans of product, both assembly sequence planning (ASP) and assembly line balance (ALB) must be taken into consideration for the selection of optimal product plan because assembly sequence and assembly line balance have significant impact on production efficiency. Considering different setup times among different assembly tasks, this issue is an NP-hard problem which cannot be easily solved by general method. In this study the multi-objective optimization mathematical model for the selection of product plan integrating ASP and ALB has been established. Introduced cases will be solved by the established model connecting to database statistics. The results show that the proposed Guided-modified weighted Pareto-based multi-objective genetic algorithm (G-WPMOGA) can effectively solve this difficult problem. The results of comparison among three different kinds of hybrid algorithms show that in terms of the issues of ASP and ALB for multiple plans, G-WPMOGA shows better problem-solving capability for four-objective optimization. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Multiple plans Assembly sequence planning Assembly line balance Multi-objective optimization hybrid algorithms

来源：评论

学校读者我要写书评

暂无评论

A Multiobjective Evolutionary-Simplex hybrid Approach for the Optimization of Differential Equation Models of Gene Networks

引用

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION 2008年第5期12卷 572-590页

作者： Koduru, Praveen Dong, Zhanshan Das, Sanjoy Welch, Stephen M. Roe, Judith L. Charbit, Erika Kansas State Univ Dept Elect & Comp Engn Manhattan KS 66502 USA Pioneer HiBred Int Inc Johnston IA 50131 USA Kansas State Univ Dept Agron Manhattan KS 66502 USA

This paper describes genetic and hybrid approaches for multiobjective optimization using a numerical measure called fuzzy dominance. Fuzzy dominance is used when implementing tournament selection within the genetic algorithm (GA). In the hybrid version, it is also used to carry out a Nelder-Mead simplex-based local search. The proposed GA is shown to perform better than NSGA-II and SPEA-2 on standard benchmarks, as well as for the optimization of a genetic model for flowering time control in rice. Adding the local search achieves faster convergence, an important feature in computationally intensive optimization of gene networks. The hybrid version also compares well with ParEGO on a few other benchmarks. The proposed hybrid algorithm is then applied to estimate the parameters of an elaborate gene network model of flowering time control in Arabidopsis. Overall solution quality is quite good by biological standards. Tradeoffs are discussed between accuracy in gene activity levels versus in the plant traits that they influence. These tradeoffs suggest that data mining the Pareto front may be useful in bioinformatics.

关键词： Biological system modeling fuzzy dominance genetic algorithms (GAs) genomics hybrid algorithms multiobjective optimization simplex

来源：评论

学校读者我要写书评

暂无评论

Efficient Task Offloading Strategy for Energy-Constrained Edge Computing Environments: A hybrid Optimization Approach

引用

IEEE ACCESS 2024年 12卷 85089-85102页

作者： Alsadie, Deafallah Umm Al Qura Univ Coll Comp Dept Comp Sci & Artificial Intelligence Makkah City 21961 Saudi Arabia

Edge Computing (EC) has emerged as a pivotal paradigm, offering solutions to address the challenges posed by latency-sensitive applications and to enhance overall network performance. In EC environments, efficient task offloading is crucial for minimizing latency and energy consumption while maximizing resource utilization. In this paper, we propose a hybrid task offloading approach (hybridTO) integrating Grey Wolf Optimizer and Particle Swarm Optimization. Our approach aims to optimize energy consumption and fulfil latency constraints in EC environments by taking into account various factors such as capacity constraints, proximity constraints, and latency requirements. Leveraging the collaborative capabilities inherent in EC servers, hybridTO offers a comprehensive solution to the task offloading problem. Through extensive simulations, we evaluate the performance of hybridTO against baseline approaches, demonstrating its superiority regarding energy usage, offloading utility and response delay, especially under conditions of limited resources. These results underscore the effectiveness of hybridTO as a promising solution for energy-efficient task offloading in EC environments, offering valuable insights for further research and development in this field.

关键词： Edge computing task offloading energy efficiency optimization hybrid algorithms

来源：评论

学校读者我要写书评

暂无评论

New Genetic algorithms Based Approaches to Continuous p-Median Problem

引用

NETWORKS & SPATIAL ECONOMICS 2011年第1期11卷 83-99页

作者： Neema, M. N. Maniruzzaman, K. M. Ohgai, A. Toyohashi Univ Technol Architecture & Civil Eng Div Dept Environm & Life Eng Aichi 4418580 Japan Bangladesh Univ Engn & Technol Dept Urban & Reg Planning Dhaka 1000 Bangladesh

We proposed new genetic algorithms (GAs) to address well-known p-median problem in continuous space. Two GA approaches with different replacement procedures are developed to solve this problem. To make the approaches more efficient in finding near-optimal solution two hybrid algorithms are developed combining the new GAs and a traditional local search heuristic. The performance of the newly developed models is compared to that of the traditional alternating location-allocation heuristics by numerical simulation and it is found that the models are effective in finding optimum facility locations.

关键词： Genetic algorithms p-Median problem Alternating location-allocation heuristic hybrid algorithms Continuous space

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：