检索结果-内蒙古大学图书馆

multi-objective optimization problem WITH BOUNDED PARAMETERS

RAIRO-OPERATIONS RESEARCH 2014年第4期48卷 545-558页

作者： Bhurjee, Ajay Kumar Panda, Geetanjali Indian Inst Technol Dept Math Kharagpur 721302 W Bengal India

In this paper, we propose a nonlinear multi-objective optimization problem whose parameters in the objective functions and constraints vary in between some lower and upper bounds. Existence of the efficient solution of this model is studied and gradient based as well as gradient free optimality conditions are derived. The theoretical developments are illustrated through numerical examples.

关键词： multi-objective optimization problem efficient solution optimality condition interval valued convex function

来源：评论

学校读者我要写书评

暂无评论

Analysis on multi-objective optimization problem Techniques

Analysis on Multi-objective Optimization Problem Techniques

引用

Springer International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS)

作者： Jaiswal, Aditi Maulana Azad Natl Inst Technol Comp Sci & Engn Dept Bhopal 462003 Madhya Pradesh India

ISBN: (纸本)9789811319518;9789811319501

In past few years, Web-based application and services are growing rapidly and this growing demands needs different Quality of Services (QoS) requirements for efficient use of such web-based services. The purpose behind utilizing these application resources could be tarnished if the fundamental communication network does not fulfill the QoS requirements. However, different applications have distinct QoS necessities as each application have different priorities. The main concern is to come across such solution which will optimize the network not in the terms of minimum number of hops but in terms of Qos parameters of network, relies upon application running over that network. This issue comes under multi-objective optimization problem (MOOP) and Genetic Algorithm (GA) is one of the techniques which can possibly control numerous parameters all together, and hence GA is applied to solve MOOP, which can enhance the QoS. This paper surveys the various MOOP techniques and then gives the best solution among them.

关键词： multi-objective optimization problem Evolutionary algorithms Quality of service (qos) Genetic algorithm (ga)

来源：评论

学校读者我要写书评

暂无评论

Hybrid QPSO-NNIA2 Algorithm for multi-objective optimization problem

引用

INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE 2019年第8期33卷

作者： Zhang, Lan Northwestern Polytech Univ Dept Appl Math Xian Shaanxi Peoples R China Xian Aeronaut Polytech Inst Basic Dept Xian Shaanxi Peoples R China

To improve the convergence and distribution of a multi-objective optimization algorithm, a hybrid multi-objective optimization algorithm, based on the quantum particle swarm optimization (QPSO) algorithm and adaptive ranks clone and neighbor list-based immune algorithm (NNIA2), is proposed. The contribution of this work is threefold. First, the vicinity distance was used instead of the crowding distance to update the archived optimal solutions in the QPSO algorithm. The archived optimal solutions are updated and maintained by using the dynamic vicinity distance based m-nearest neighbor list in the QPSO algorithm. Secondly, an adaptive dynamic threshold of unfitness function for constraint handling is introduced in the process. It is related to the evolution algebra and the feasible solution. Thirdly, a new metric called the distribution metric is proposed to depict the diversity and distribution of the Pareto optimal. In order to verify the validity and feasibility of the QPSO-NNIA2 algorithm, we compare it with the QPSO, NNIA2, NSGA-II, MOEA/D, and SPEA2 algorithms in solving unconstrained and constrained multi-objective problems. The simulation results show that the QPSO-NNIA2 algorithm achieves superior convergence and superior performance by three metrics compared to other algorithms.

关键词： multi-objective optimization problem Pareto optimal solution vicinity distance Quantum particle swarm optimization algorithm particle swarm optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A swarm exploring neural dynamics method for solving convex multi-objective optimization problem

引用

NEUROCOMPUTING 2024年 601卷

作者： Zhang, Zhijun Yu, Haomin Ren, Xiaohui Luo, Yamei South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China Inst Super Robot Huangpu Guangzhou 510555 Peoples R China Nanchang Univ Jiangxi Thousand Talents Plan Nanchang 330031 Peoples R China Jishou Univ Coll Comp Sci & Engn Jishou 416000 Peoples R China Guangdong Artifcial intelligence & Digital Econ La Pazhou Lab Guangzhou 510335 Peoples R China Shanxi Univ Technol Sch Mech Engn Shaanx Prov Key Lab Ind Automat Hanzhong 723001 Peoples R China Hunan Univ Finance & Econ Sch Informat Technol & Management Changsha 410100 Peoples R China Guangdong Univ Petrochem Technol Inst Artificial Intelligence & Automat Sch Automat Sci & Engn Maoming 525000 Peoples R China South China Univ Technol Key Lib Autonomous Syst & Network Control Minist Educ Guangzhou 510641 Peoples R China

multi-objective optimization problem (MOP) plays an increasingly important role in finance and engineering. In order to obtain more accurate and evenly distributed target solution set to a multi-objective programming, a novel swarm exploring neural dynamics (SEND) method is proposed, analyzed and applied in this paper. Specifically, a scalarization approach is firstly applied to transform the MOP into a group of subproblems. Secondly, each subproblem is solved by a varying parameter recurrent neural network (VP-RNN). By solving these problems, a group of Pareto optimal solutions are obtained. Thirdly, a population evolution weight optimization algorithm is used to diversify the solution set to obtain evenly distributed solutions. Simulation results demonstrate that the proposed SEND method can obtain a more accurate and evenly distributed solution set than some previous methods and the convergence rate is faster than the state-of-art methods, such as collaborative neurodynamic approach (CNA).

关键词： Swarm exploring neural dynamics multi-objective optimization problem Pareto optimal solutions

来源：评论

学校读者我要写书评

暂无评论

The multi-objective optimization problems of Markovian Jump Systems Closed by General Controllers: A Mode-Separation Approach

引用

IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 13359-13372页

作者： Wang, Guoliang Lyu, Yaqiang Liaoning Petrochem Univ Sch Informat & Control Engn Fushun 113001 Liaoning Peoples R China

This paper studies the multi-objective optimization problems (MOPs) of Markovian jump systems (MJSs) closed by general controllers. Firstly, the linear quadratic regulator (LQR) problem of MJSs closed by a general controller with sampled or synchronous modes is studied and estimated by a mode-separation approach, whose traditional algebraic Riccati equations (AREs) are removed. Meanwhile, two traditional situations about mode-dependent and -independent controllers are contained as special ones. Secondly, an MOP deeply depending on mode separations is proposed and solved by the non-dominated sorting whale optimization algorithm (NSWOA) such that the minimum value of LQR problem and the maximum expectation value of summed mode dwell times in the same mode separation are simultaneously optimized. Particularly, in order to further improve the estimation effect, a single-objective optimization problem (SOP) is presented and computed by applying the deep deterministic policy gradient (DDPG) technique, whose optimal controller in addition to its best mode separation is given in detail. Thirdly, similarly but more generally, another controller with its mode and state both sampled is constructed to realize the LQR problem, whose effects are better than some existing methods and can also be improved by solving some optimization problems. Finally, a simulation is used to verify the effectiveness and superiority of the proposed methods. Note to Practitioners-This paper addresses the LQR problem for MJSs, commonly used in automation and control with uncertainty and frequent mode switching. Traditional LQR methods rely on mode-dependent controllers and AREs, leading to high computational costs and real-time demands, especially in industrial settings. To overcome these challenges, this paper proposes a general controller design and an MOP, replacing AREs with a mode-separation strategy, simplifying the process and reducing costs. A controller with both mode and state sampling is a

关键词： Control systems optimization Automation Switches Real-time systems Synchronization Sliding mode control Event detection Training Regulators Markovian jump systems multi-objective optimization problem mode-separation approach linear quadratic regulator sampled control

来源：评论

学校读者我要写书评

暂无评论

Solving convex multi-objective optimization problems using a projection neural network framework

引用

Soft Computing 2025年 1-24页

作者： Jahangiri, Mohammadreza Nazemi, Alireza Faculty of Mathematical Sciences Shahrood University of Technology Shahrood Iran

In this paper, a projection neural network model for solving convex multi-objective optimization problem (CMOP) is considered. The CMOP is first converted into an equivalent convex nonlinear single-objective programming problem by the mean of the weighted sum method, where the Pareto optimal solutions (POS) are calculated by diversifying values of weights. A neural network model is then constructed for solving the obtained convex problem. It is shown that the presented neural network model is stable in the sense of Lyapunov and is globally convergent. Simulation results are given to illustrate the global convergence and performance of our proposed model. Both theoretical and numerical approaches are studied. It is illustrated that the numerical results are in good agreement with the theoretical arguments. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Convergence Convex non-linear programming problem multi-objective optimization problem Neural networks Pareto optimal solution Stability

来源：评论

学校读者我要写书评

暂无评论

A Competitive Mechanism multi-objective Particle Swarm optimization Algorithm and Its Application to Signalized Traffic problem

引用

CYBERNETICS AND SYSTEMS 2020年第1期52卷 73-104页

作者： Yuen, Man-Chung Ng, Sin-Chun Leung, Man-Fai Open Univ Hong Kong Sch Sci & Technol Hong Kong Peoples R China

In this paper, a modified Competitive Mechanism multi-objective Particle Swarm optimization (MCMOPSO) algorithm is presented for multi-objective optimization. The algorithm consists of an improved leader selection scheme called multi-competition leader selection. Under this scheme, particles move to the winner among the elite particles for the social cognitive by comparing the nearest angle or the farthest angle of several randomly selected elite particles. Besides, as the inertia weight plays an important role in controlling the previous velocity of each particle, the competitive mechanism is applied to the inertia weight in order to investigate for the most suitable balance between the exploration and exploitation abilities of the algorithm during the search process. The experimental results show that the proposed algorithm outperforms four other popular multi-objective particle swarm optimization algorithms most of the time on thirty-seven benchmarks in terms of inverted generational distance. Furthermore, the proposed algorithm is applied to the signalized traffic problem to optimize the effective green time of each phase, and the proposed algorithm performs better than other MOPSO algorithms for the traffic problem in terms of hypervolume.

关键词： Evolutionary algorithms multi-objective optimization problem particle swarm optimization

来源：评论

学校读者我要写书评

暂无评论

A novel machine learning-driven optimization methodology for faster and more efficient design space exploration in high-level synthesis

引用

MICROPROCESSORS AND MICROSYSTEMS 2025年 114卷

作者： Celik, Esra Dal, Deniz Ataturk Univ Fac Engn Dept Comp Engn TR-25240 Erzurum Turkiye

The optimization of digital circuits is a critical factor in determining the competitiveness of modern electronic systems, particularly in terms of area, performance, and power consumption. High-Level Synthesis (HLS) plays a pivotal role in this optimization process, enabling designers to define system requirements at a higher level of abstraction and providing opportunities to analyze and optimize digital circuits against various metrics prior to production. However, the design constraints inherent in the HLS process often lead to multi-objective optimization problems, which significantly complicate the exploration process. This complexity necessitates the development of novel synthesis methodologies enabling faster and more efficient design space exploration. In response to this need, within the scope of this study, we introduced an innovative and hybrid HLS methodology that combines metaheuristic and machine learning approaches. In this respect, two distinct synthesis tools were developed. The first tool, implemented in C++, utilizes the Simulated Annealing (SA) metaheuristic with a novel three-part solution representation. This representation, a key contribution of our study, aims to minimize the weighted sum of latency and area constraints for Data Flow Graph (DFG) designs. While effective, this approach resulted in extended execution times due to computationally intensive design variables. To address the performance bottleneck identified in the standard cost function evaluation, we developed a second tool that integrates machine learning with the traditional SA. This hybrid approach combines C++ and Python, incorporating a Support Vector Regression (SVR) model to estimate solution costs more efficiently, significantly reducing execution times. Our study also presents the detailed analyses of the experimental results conducted on seven benchmarks with varying node counts. The three-part solution representation in the traditional SA approach demonstrated up to a

关键词： Digital circuit synthesis Electronic design automation EDA Computer-aided design CAD High-level synthesis HLS Design space exploration multi-objective optimization problem MOOP optimization Metaheuristic Simulated annealing Machine learning Machine learning-based simulated annealing MLSA Support vector regression Latency Area Weighted scalarization

来源：评论

学校读者我要写书评

暂无评论

A Comprehensive Analysis on Enhancing multi-objective Evolutionary Algorithms Using Chaotic Dynamics and Dominant Relationship-Based Search Strategies

引用

IEEE ACCESS 2025年 13卷 33455-33470页

作者： Wang, Zitong Pei, Yan Li, Jianqiang Univ Aizu Grad Sch Comp Sci & Engn Fukushima 9658580 Japan Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

In optimization and decision-making, multi-objective optimization has emerged as a pivotal challenge. Over the past three decades, the concerted efforts of scholars and practitioners across various disciplines have significantly advanced the study and implementation of multi-objective Evolutionary Algorithms (MOEAs). MOEAs stand at the forefront of multi-objective decision-making methodologies, marking a vibrant area of inquiry within evolutionary computation. This body of work categorizes MOEAs into three distinct streams: Decomposition-based MOEA algorithms, Dominant relationship-based MOEA algorithms, and Evaluation index-based MOEA algorithms. Focusing specifically on dominance-based MOEAs, this study integrates them with chaotic evolution (CE) strategies to enhance the efficacy of multi-objective optimization processes. Through comparative analysis against traditional algorithms, the newly proposed chaotic MOEA demonstrates superior optimization performance, thereby setting a robust groundwork for the continuous evolution and application of MOEAs.

关键词： optimization Heuristic algorithms Chaos Evolutionary computation Search problems Space exploration Linear programming Evolutionary multi-objective optimization multi-objective optimization problem chaotic evolution multi-objective chaotic evolution algorithm search strategy optimization

来源：评论

学校读者我要写书评

暂无评论

Tradeoff Analysis of Unintentional Interference and Communication Rate of UAV

引用

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2025年第4期74卷 5941-5953页

作者： Lu, Yongguang Wang, Kehao Liu, Pei Chen, Lin Shin, Hyundong Quek, Tony Q. S. Wuhan Univ Technol Sch Informat Engn Wuhan 430070 Peoples R China Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510006 Peoples R China Kyung Hee Univ Dept Elect Engn Yongin 17104 South Korea Singapore Univ Technol & Design Informat Syst Technol & DesignPillar Singapore 487372 Singapore

With the development of unmanned aerial vehicle (UAV) technology, using UAVs as aerial communication platforms to provide additional communication services is a promising technology. However, due to the scarcity of spectrum resources, UAV will inevitably causes unintentional interference to primary users (PUs) when transmitting data to secondary users (SUs). Aiming at this problem, with the help of the multi-objective optimization problem (MOOP) framework, this paper studies the trade-off between UAV's communication rate and UAV's unintentional interference by optimizing UAV's trajectory, UAV's transmission power, and SU's scheduling. Specifically, we consider a cognitive UAV communication scenario that a single UAV needs to transmit data to multiple SUs in a shared spectrum scenario. During this process, if the existing PUs receive unintentional interference from the UAV does not exceed a given threshold which is also called interference temperature (IT), it will be regarded as a normal communication state. Then, a MOOP is established that includes maximizing the minimum UAV-SU communication rate and maximizing the times that PUs are in normal communication states. Finally, with the help of $\epsilon -constraint$ method and stochastic submodular optimization algorithm, we propose an iterative algorithm to obtain the approximate Pareto front. The tradeoff between UAV's communication rate and UAV's unintentional interference is analyzed through numerical simulation.

关键词： UAV cognitive communication UAV uninten-tional interference trajectory optimization communication design communication design multi-objective optimization problem multi-objective optimization problem multi-objective optimization problem

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

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

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：