检索结果-内蒙古大学图书馆

8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Han, Lingzhi Liu, Hai-Lin Gu, Fangqing Guangdong Univ Technol Sch Appl Math Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538692769

Evolutionary Multi-objective optimization (EMO) Algorithms have been successfully applied in many practical problems. However, their performance deteriorated seriously for the problems with many objectives, i.e. many-objective optimization problems (MaOPs). In some practical applications, there exist some redundant objectives in an MaOP. Reducing the number of objectives of the optimization problem is one of the effective ways to solve the MaOPs with redundant objectives. It can improve the search efficiency of EMO algorithms, and reduce the computational cost. In this paper, we propose a new objective reduction algorithm. A criterion based on the number of non-dominated solution paired is presented to measure the conflict degree between objectives. Furthermore, we develop a effective objective reduction algorithm using feature selection technique. We compared the proposed algorithm with the LPCA, NLMVUP-CA and delta-MOSS algorithm in some benchmark problems, and the results show the effectiveness of the proposed algorithm.

关键词： multiobjective optimization problem objective reduction feature selection conflict measure

来源：评论

学校读者我要写书评

暂无评论

Mathematical and computational models for nanostructured coating using electron beam-physical vapor deposition (EB-PVD)

Mathematical and computational models for nanostructured coa...

引用

作者： Baek, Seungyup PennState University Libraries

学位级别：Doctor of Philosophy

Nanostructured coatings offer substantial improvement over conventional coatings in physical and mechanical properties such as hardness, strength, toughness and thermal conductivity. There are numerous applications where nanostructured coatings have been found to beneficial including bio-mechanical implants, semiconductor thin films, propulsion, and power generation. In aircraft and power generation turbines, nanostructured thermal barrier coating (TBC) can significantly reduce fuel costs. There has been an acute need for a science base, such as dynamic models of nanostructured coating processes. Modeling the processes presents significant challenges because the physical variables are highly coupled and interact in a nonlinear manner. Real-time control of this process poses additional challenges because of the high vacuum, high temperature, and generally hostile environment for making in-situ measurements, which further motivates the need for good process *** research proposes unified models for controlling nanostructured coating process using electron beam physical vapor deposition (EB-PVD). Specifically, the following three dynamical models have been developed:*** Dynamics Model – combines models of various component dynamics in the EB-PVD machine such as cathode heating, ingot melting, and *** Process Model – mathematically characterizes the physical relationship between process parameters and the micro/nanostructure of the coating. *** Kinematics Model – geometric models that are computationally efficient for modeling the kinematics of substrate and manipulator *** of the main contributions of this work is the use of partial differential equations for the deposition process model to characterize the evolution of the coated surface. This has been used to characterize surface evolution at the macro-level (1 micrometer), at the meso-level (50 nanometers), and with limited fidelity at the nano-level (1 nanometer). Th

关键词： multiobjective optimization problem Simulation Level set method Nanostructured coating EB-PVD Feedback control

来源：评论

学校读者我要写书评

暂无评论

An improved multiobjective evolutionary algorithm based on decomposition approach and its application in antenna array beam pattern synthesis

引用

INTERNATIONAL JOURNAL OF NUMERICAL MODELLING-ELECTRONIC NETWORKS DEVICES AND FIELDS 2022年第1期35卷 e2935-e2935页

作者： Liang, Shuang Fang, Zhiyi Li, Guanxiao Zhao, Yaqing Liu, Xuejie Sun, Geng Jilin Univ Coll Comp Sci & Technol Jilin Jilin Peoples R China Jilin Univ Minist Educ Key Lab Symbol Computat & Knowledge Engn Jilin Jilin Peoples R China

Antenna arrays can enhance the performance and reduce the overhead of the wireless communication systems. However, the beam pattern synthesis of antenna arrays are difficult problems since the optimization properties are usually trade-offs that affect each other. In this paper, we formulate a multiobjective beam pattern optimization problem (MBPOP) to simultaneously reduce the maximum sidelobe level (SLL) and achieve the nulls of the antenna array beam pattern. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) is a general and effective algorithm to solve the MOPs. However, it may be easy to lose population diversity and converge to local optimum. To overcome the issues above, we propose an improved MOEA/D (IMOEA/D) to deal with the formulated MBPOP. IMOEA/D introduces the normal distribution crossover operator (NDX), Levy flight strategy and Euclidean distance-based solution selection mechanism to enhance the performance of conventional MOEA/D to make it more suitable to solve the formulated MBPOP. Experiments are conducted and the results indicate that the proposed IMOEA/D has a better performance in terms of the convergence rate and population diversity compared to other algorithms for solving the formulated MBPOP.

关键词： antenna array beam pattern evolutionary computation MOEA D multiobjective optimization problem

来源：评论

学校读者我要写书评

暂无评论

DIntrusion Detection in WSN with an Improved NSA Based on the DE-CMOP

引用

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2017年第11期11卷 5574-5591页

作者： Guo, Weipeng Chen, Yonghong Cai, Yiqiao Wang, Tian Tian, Hui Huaqiao Univ Coll Comp Sci & Technol Xiamen Peoples R China Univ Cincinnati Dept Elect Engn & Comp Syst Cincinnati OH 45221 USA

Inspired by the idea of Artificial Immune System, many researches of wireless sensor network (WSN) intrusion detection is based on the artificial intelligent system (AIS). However, a large number of generated detectors, black hole, overlap problem of NSA have impeded further used in WSN. In order to improve the anomaly detection performance for WSN, detector generation mechanism need to be improved. Therefore, in this paper, a Differential Evolution Constraint Multi-objective optimization problem based Negative Selection Algorithm (DE-CMOP based NSA) is proposed to optimize the distribution and effectiveness of the detector. By combining the constraint handling and multi-objective optimization technique, the algorithm is able to generate the detector set with maximized coverage of non-self space and minimized overlap among detectors. By employing differential evolution, the algorithm can reduce the black hole effectively. The experiment results show that our proposed scheme provides improved NSA algorithm in-terms, the detectors generated by the DE-CMOP based NSA more uniform with less overlap and minimum black hole, thus effectively improves the intrusion detection performance. At the same time, the new algorithm reduces the number of detectors which reduces the complexity of detection phase. Thus, this makes it suitable for intrusion detection in WSN.

关键词： WSN Intrusion detection Negative Selection Algorithm multiobjective optimization problem Differential Evolution

来源：评论

学校读者我要写书评

暂无评论

multiobjective PARTICLE SWARM optimization BASED ON DIMENSIONAL UPDATE

引用

INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS 2013年第3期22卷 1350015-1350015页

作者： Xu, Heming Wang, Yinglin Xu, Xin Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China

For multiobjective particle swarm optimization (MOPSO), two particles may be incomparable, i.e., not dominated by each other. The personal best and the global best for the particle become less optimal, thus the convergence becomes slow. Even worse, an archive of a limited size can not cover the entire region dominated by the Pareto front, the uncovered region can contain unidentifiable nondominated solutions that are not optimal, and thus the precision the algorithm achieves encounters a plateau. Therefore we propose dimensional update, i.e., evaluating the particle's fitness after updating each variable of its position. Separate consideration of the impact of each variable decreases the occurrence of incomparable relations, thus improves the performance. Experimental results validate the efficiency of our algorithm.

关键词： multiobjective optimization problem particle swarm optimization dimensional update personal best archive learning

来源：评论

学校读者我要写书评

暂无评论

Development of a Comparative Multiple Criteria Framework for Ranking Pareto Optimal Solutions of a multiobjective Reservoir Operation problem

引用

JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING 2016年第7期142卷 1-12页

作者： Bozorg-Haddad, Omid Azarnivand, Ali Hosseini-Moghari, Seyed-Mohammad Loaiciga, Hugo A. Univ Tehran Fac Agr Engn & Technol Dept Irrigat & Reclamat Engn Coll Agr & Nat Resources Tehran *** Iran Univ Calif Santa Barbara Dept Geog Santa Barbara CA 93016 USA

Real-world, multiobjective, reservoir optimization problems (MOOPs) usually have conflicting objective functions. multiobjective evolutionary algorithms (MOEAs) have been applied to solve multiobjective optimization problems. Given that MOOP solutions cannot minimize or maximize all objectives simultaneously, it is difficult to determine Pareto optimal solutions (POS). This paper presents a comparative multimethodological framework that implements five multicriteria decision-making methods (MCDMs) to determine POS for a single-reservoir system with three objective functions. The Borda technique was successfully employed to rank the MCDMs' solutions. The reservoir system's release, storage, and generated hydropower were optimized based on the best-ranked MCDMs' solutions. This paper's methodology introduces practical computational framework for making robust decisions in complex reservoir operation problems and demonstrates its applicability.

关键词： multiobjective optimization problem Multicriteria decision making-Pareto frontier Reservoir operation Nondominated sorted genetic algorithm-II (NSGA-II)

来源：评论

学校读者我要写书评

暂无评论

A knee-guided differential evolution algorithm for unmanned aerial vehicle path planning in disaster management

引用

APPLIED SOFT COMPUTING 2021年 98卷 106857-106857页

作者： Yu, Xiaobing Li, Chenliang Yen, Gary G. Nanjing Univ Informat Sci & Technol Sch Management Sci & Engn Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Key Lab Meteorol Disaster KLME Minist Educ Nanjing 210044 Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteoro Nanjing 210044 Peoples R China Oklahoma State Univ Sch Elect & Comp Engn Stillwater OK 74078 USA

Unmanned aerial vehicles are instrumental in monitoring and analyzing information and searching for people in disaster relief scenarios. In this paper, path planning is constructed as a multiobjective optimization problem with constraints in a three-dimensional terrain disaster scenario. The objective functions involve the distance and risk of the path, which are calculated based on Bezier theory. The constraints include the turning angle and flight altitude. To solve this problem in an effective and efficient manner, a differential evolution algorithm that is based solely on the knee point is proposed, in which the knee solution would guide the search direction of the algorithm. According to the minimal Manhattan distance approach, the algorithm can quickly identify an optimal solution to generating a smooth path for decision-makers. Experimental results have confirmed the superiority of the proposed algorithm, and the rankings of the minimal Manhattan distance approach are consistent with multicriteria decision-making methods. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Path planning multiobjective optimization problem Knee solution Differential evolution

来源：评论

学校读者我要写书评

暂无评论

Heuristic-Based Approach for Near-Optimal Response to Water Distribution Network Failures in Near Real Time

引用

JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT 2022年第8期148卷 04022039-04022039页

作者： Nikoloudi, Eirini Romano, Michele Memon, Fayyaz Ali Kapelan, Zoran Univ Exeter Ctr Water Syst N Pk Rd Exeter EX4 4QJ Devon England United Utilities Grp PLC Lingley Green Ave Warrington WA5 3LP Cheshire England Delft Univ Technol Fac Civil Engn & Geosci Stevinweg 1 NL-2628 CN Delft Netherlands

This paper proposes a new method to identify the near-optimal response to failures in water distribution networks in near real time. The response method is formulated as a two-objective optimization problem with objectives being the minimization of failure impacts and related operational costs. The new heuristics-based method is developed and used to solve this optimization problem. The method comprises three steps. In the first step, the initial list of available interventions is identified offline. In the second step (online), the narrowed-down list of interventions considered in the optimization is identified. Finally, in the last step (online), a novel heuristic algorithm is applied to identify near-optimal solutions in near real time. The new optimization method was validated and demonstrated in two case studies, a semireal case study based on a C-Town network and an assumed failure event (pipe burst), and a real UK case study involving a complex real pipe network and event caused by shutting down the Water Treatment Works. The Pareto front of response interventions identified by the new heuristics method approximates well the non-dominated sorting genetic algorithm II Pareto front in both cases with the largest differences measured in terms of end-impacts (between relevant solutions for the same cost) being 4% and 9%, respectively. In addition, the new heuristics method is able to identify near-optimal response solutions in a computationally fast manner (15 min and 1 h for the two cases). Therefore, the heuristics method can be used in near real time in real-life situations.

关键词： Near real-time optimization Near real-time response Water distribution network failures Heuristic method multiobjective optimization problem

来源：评论

学校读者我要写书评

暂无评论

A regularity property-driven evolutionary algorithm for multiobjective optimization

引用

SWARM AND EVOLUTIONARY COMPUTATION 2023年 78卷

作者： Gao, Xiangzhou Zhang, Hu Song, Shenmin Harbin Inst Technol Ctr Control Theory & Guidance Technol Harbin 150001 Peoples R China Beijing Electromech Engn Inst Sci & Technol Complex Syst Control & Intelligent A Beijing 100074 Peoples R China

When most existing multiobjective evolutionary algorithms tackle continuous multiobjective optimization problems, they pay more attention to the population distribution in the objective space and neglect the potential of high-quality solutions in the decision space. In fact, it has been demonstrated that a good approximation of both Pareto optimal set (PS) and Pareto front (PF) is capable of facilitating decision making, especially when preferences are not clearly defined by the decision-maker. However, since different problems may have different internal structures, achieving trade-offs between exploration and exploitation while accelerating convergence toward the PS and PF remains challenging. To address this issue, we propose an evolutionary algorithm that explicitly exploits the regularity properties of the multiobjective optimization problem in the decision space and the objective space. A feedback loop can be formed directly between two spaces, which aims to approximate the PS and the PF by approximating the PS manifold and the PF manifold, respectively. In addition, the uniform distribution of population is guaranteed by two mutually reinforcing diversity maintenance mechanisms. Our experimental results on a variety of benchmark problems and real -world problems demonstrate that the proposed method performs remarkable on problems with regularities but suffers from some limitations when solving some real-world problems.

关键词： multiobjective optimization problem Regularity property Manifold structure Recombination operator Diversity maintenance

来源：评论

学校读者我要写书评

暂无评论

Chaotic self-adaptive interior search algorithm to solve combined economic emission dispatch problems with security constraints

引用

INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2019年第8期29卷

作者： Rajagopalan, Arul Kasinathan, Padmanathan Nagarajan, Karthik Ramachandaramurthy, Vigna K. Sengoden, Velusami Alavandar, Srinivasan Vellore Inst Technol Sch Elect Engn Chennai Campus Chennai Tamil Nadu India Agni Coll Technol Dept Elect & Elect Engn Chennai Tamil Nadu India Hindustan Inst Technol & Sci Dept Elect & Elect Engn Chennai Tamil Nadu India Univ Tenaga Nas Inst Power Engn Dept Elect Power Engn Kajang Malaysia Periyar Maniammai Univ Dept Elect & Elect Engn Thanjavur India

The main goal behind the combined economic emission dispatch (CEED) is to reduce the costs incurred upon fuel and emission for the generating units available without any intention to violate the generator and security constraints. Hence, the CEED must be handled after considering two challenging goals such as the costs involved with emission and fuel. In this paper, chaotic self-adaptive interior search algorithm (CSAISA) was proposed to solve the CEED problems, considering the nonlinear behavior of generators in terms of valve point effects, prohibited operating zones, and security constraints. The proposed algorithm was tested for its effectiveness using 11-generating units (without security), IEEE-30 bus system, and IEEE-118 bus system with security constraints. The results of the proposed CSAISA were compared with interior search algorithm (ISA), harmony search algorithm (HSA), differential evolution (DE), particle swarm optimization (PSO), and genetic algorithm (GA). To conclude, the proposed CSAISA outperformed all other algorithms in terms of convergence speed, implementation time, and solution quality, which was tested using performance metrics.

关键词： chaotic self-adaptive interior search algorithm combined economic emission dispatch interior search algorithm multiobjective optimization problem performance metrics security constraints

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：