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检索条件"主题词=multi-objective optimization problem"

共 192 条记录，以下是121-130 订阅

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Solving multi-Stage Weapon Target Assignment problems by C-TAEA 39

Solving Multi-Stage Weapon Target Assignment Problems by C-T...

引用

39th Chinese Control Conference (CCC)

作者： Zou, Shiqi Shi, Xiaoping Guo, Rui Lin, Xiaohan Harbin Inst Technol Control & Simulat Ctr Harbin 150080 Peoples R China

ISBN: (纸本)9789881563903

multi-stage weapon target assignment (MSWTA) problem is usually treated as a classical constrained multi-objective optimization problem, which tends to balance the attack efficiency and economic loss for weapons after adding engagement resource constraints. In the context of multi-stage confrontations, the MSWTA becomes more complex and challenging to solve. To handle this constrained problem, this paper develops the advantageous two-archive evolutionary algorithm (C-TAEA) to combine the density estimation mechanism with a fast non-dominated sorting method to obtain solutions to this problem efficiently, while maintaining the convergence-oriented archive (CA) to speed up the generation of solutions and the diversity-oriented archive (DA) to enhance the diversity of populations. Extensive experiments are conducted to compare the proposed C-TAEA with the traditional NSGA-II according to the average of IGD and HV values. The numerical simulation results demonstrate that our method intuitively shows the performance improvement after adding the feasibility and can find the approximate Pareto Front more effectively.

关键词： Weapon Target Assignment (WTA) multi-objective optimization problem Two-Archive C-TAEA NSGA-II Density Estimation

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A computational intelligence approach to system-of-systems architecting incorporating multi-objective optimization 1

A computational intelligence approach to system-of-systems a...

引用

Conference on Systems Engineering Research

作者： Curry, David M. Dagli, Cihan H. Missouri Univ Sci & Technol Dept Engn Management & Syst Engn Rolla MO 65409 USA

A computational intelligence approach to system-of-systems architecting is developed using multi-objective optimization. Such an approach yields a set of optimal solutions (the Pareto set) which has both advantages and disadvantages. The primary benefit is that a set of solutions provides a picture of the optimal solution space that a single solution cannot. The primary difficulty is making use of a potentially infinite set of solutions. Therefore, a significant part of this approach is the development of a method to model the solution set with a finite number of points allowing the architect to intelligently choose a subset of optimal solutions based on criteria outside of the given objectives. The approach developed incorporates a meta-architecture, multi-objective genetic algorithm, and a corner search to identify points useful for modeling the solution space. This approach is then applied to a network centric warfare problem seeking the optimum selection of twenty systems. Finally, using the same problem, it is compared to a hybrid approach using single-objective optimization with a fuzzy logic assessor to demonstrate the advantage of multi-objective optimization. (C) 2015 The Authors. Published by Elsevier B.V.

关键词： System-of-Systems SoS Architecting Genetic Algorithm GA multi-objective optimization problem MOP multi-objective Evolutionary Algorithm MOEA Meta-Architecture

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A new multi-objective evolutionary algorithm for solving high complex multi-objective problems 06

A new multi-objective evolutionary algorithm for solving hig...

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8th Annual Genetic and Evolutionary Computation Conference

作者： Li, Kangshun Yue, Xuezhi Kang, Lishan Chen, Zhangxin Jiangxi Univ Sci & Technol Sch Informat Engn Ganzhou 341000 Peoples R China Jiangxi Normal Univ Prov Key Lab High Performance Comp Technol Nanchang 330022 Jiangxi Peoples R China Wuhan Univ State Key Lab Software Engn Wuhan 430072 Hubei Peoples R China Southern Methodist Univ Ctr Sci Computat Dallas TX 75275 USA Southern Methodist Univ Dept Math Dallas TX 75275 USA

ISBN: (纸本)9781595931863

In this paper, a new multi-objective evolutionary algorithm for solving high complex multi-objective problems is presented based on the rule of energy minimizing and the law of entropy increasing of particle systems in phase space, Through the experiments it proves that this algorithm can quickly obtains the Pareto solutions with high precision and uniform distribution. And the results of the experiments show that this algorithm can avoid the premature phenomenon of problems better than the traditional evolutionary algorithm because it can drive all the individuals to participate in the evolving operation in each generation.

关键词： evolutionary algorithm multi-objective optimization problem free energy entropy Pareto front

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A problem-specific Constrained multi-objective Evolutionary Algorithm 3

A Problem-specific Constrained Multi-objective Evolutionary ...

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3rd International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

作者： Dong, Jun-jie Li, He-cheng Huang, Jing Qinghai Normal Univ Sch Math & Stat Xining Qinghai Peoples R China

ISBN: (纸本)9781538684818

multi-objective optimization problem is a kind of problem optimizing simultaneously several conflicting objectives and keeping a balance between the diversity and the convergence of solutions. In this paper, some novel techniques are designed to improve the efficiency of multi-objective evolutionary algorithms. Firstly, fitness in keeping with feasibility is designed to choose the individual with good feasibility as much as possible;secondly, a specific sub-function is separated from a series of objectives, which is applied to provide an approximate search direction and speed the convergence of the algorithm. Thirdly, differential evolution is used to make the population search to the optimal solutions;then, the crowding degree scheme, as in NSGA-II, is used to select potential promising solutions in the process of iterations such that Pareto front is uniform as much as possible. Finally, a novel multi-objective evolutionary algorithm is presented by embedding these schemes. The simulation illustrates the effectives of the proposed algorithm.

关键词： multi-objective optimization problem evolutionary algorithm problem information non-dominated solution

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An Improved multi-objective optimization Algorithm Based on Reinforcement Learning 13th

An Improved Multi-objective Optimization Algorithm Based on ...

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13th International Conference on Swarm Intelligence (ICSI)

作者： Liu, Jun Zhou, Yi Qiu, Yimin Li, Zhongfeng Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan 430081 Peoples R China Yingkou Inst Technol Sch Elect Engn Yingkou 115014 Peoples R China

ISBN: (纸本)9783031096778;9783031096761

multi-objective optimization (MOP) has been widely applied in various applications such as engineering and economics. MOP is an important practical optimization problem, and finding approaches to better solve it is of both practical and theoretical significance. The core of solving an MOP is to find the global optimal solution set efficiently and accurately. The current MOP algorithm has premature convergence or poor population diversity, and the solution set obtained falls easily into the local optimal or clustering phenomenon. In this study, an MOP algorithm based on the non-dominated sorting genetic algorithm based on reinforcement learning (RL-NSGA-II) is proposed;the algorithm is adopted based on the prediction, forecasting, Monte Carlo method, which is based on the action of population genetic information, and environment interaction information and Markov decision process in mathematical modeling. This is because using a non-dominated solution contains valuable information, which can be used to guide the evolution direction of the population and search the optimal solution set more accurately. The proposed RL-NSGA-II algorithm was evaluated on the ZDT and DTLZ test sets, and the experimental results verified the effectiveness of the proposed algorithm in solving MOPs.

关键词： Reinforcement learning NSGA-II Monte Carlo method multi-objective optimization problem

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Ant Colony optimization Algorithm In Occupational Skill Testing Management System

Ant Colony Optimization Algorithm In Occupational Skill Test...

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2016 IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference(IMCEC 2016)

作者： Peng Yinghui Tianjin University of Technology and Education

ISBN: (纸本)9781509001668

This project for vocational skills certification examination in different professions,different types and different levels has developed vocational skills certification exam management *** Ant colony optimization(ACO)algorithm intelligently make skills identification papers has solved the multi-objective problem;so determine vocational skills certification examination test of the different professions,different types,different levels of difficulty with normal distribution,meet different groups requirements of vocational skills certification examination.

关键词： Occupational Skill Testing Auto make test multi-objective optimization problem

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Hardware Software Partitioning using Immune Algorithm Based on Pareto

Hardware Software Partitioning using Immune Algorithm Based ...

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International Conference on Artificial Intelligence and Computational Intelligence

作者： Liu, Yang Li, Qing Cheng Tianjin Normal Univ Coll Comp & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9780769538167

With the development of embedded system and microelectronics technology, the Hardware/Software Co-Design Methodology of SoC has taken an important role in designing of embedded applications. Hardware/Software partitioning is one of the most significant part of Hardware/Software co-design of embedded systems. Hardware/Software partitioning is essentially multi objective optimization problem, namely, how to obtain an optimal hardware/software combination based on satisfying system function requirements and constraints. This paper presents an Immune Algorithm based on the Pareto concept of multi-objective optimization problems. Compared with other algorithm, it can provide an effective tool for measuring the performance of different objective functions, and improve the designing efficiency. In the last, the result was compared with those of using the single-objective optimization on different task graphs. Experimental results show the algorithm can achieve the global optimal solution of the HW/SW partitioning problem based on meet system constraints.

关键词： Hardware/Software partitioning Immune Algorithm Pareto-optimal multi-objective optimization problem Hardware/Software Co-Design Methodology

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Comparison of Evolutionary multi-objective optimization Algorithms Using Imitation Game 22

Comparison of Evolutionary Multi-Objective Optimization Algo...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Sato, Yuji Murakawa, Yoshihisa Hosei Univ Fac Comp & Informat Sci Koganei Tokyo Japan

ISBN: (纸本)9781450392686

In this paper, we consider the artificial bird song imitation game as dynamic two objective optimization problem and propose it as the benchmark problem that includes a wide variety of transitions. We have evaluated the speed of evolution towards the edge of chaos using NSGA-II and SPEA2 which are representative evolutionary multi-objective optimization algorithms based on Pareto dominance. As a result, we show that our proposed method is effective, and NSGA-II is better than SPEA2 in convergence speed. We also show that the solution tends to converge around the median of the edge of chaos and the chaos region, as compared with the conventional imitation game using the single objective evolutionary algorithm.

关键词： multi-objective optimization problem NSGA-II SPEA2 Benchmark problem. Imitation Game

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IMPROVED PARETO OPTIMUM PASSING USING VARIED KICKING SPEED IN SOCCER GAMES 3

IMPROVED PARETO OPTIMUM PASSING USING VARIED KICKING SPEED I...

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3rd Asian Conference on Intelligent Games and Simulation / 3rd Asian Simulation Technology Conference

作者： Tanjapatkul, Nattawit Kotrajaras, Vishnu Chulalongkorn Univ Fac Engn Dept Comp Engn Payathai Rd Bangkok 10330 Thailand

ISBN: (纸本)9789077381601

Ball passing is very important in soccer games. A good pass to a teammate can create a scoring chance, which is critical for winning the game. This paper presents the improvement of ball passing for Artificial Intelligence (AI) in soccer games that uses pareto optimum passing. We have developed our own soccer simulation using Unity3D game engine. An approachfor finding suitable values for risk parameters on each position on the pitch is proposed. We also introduce the algorithm for calculating a suitable kicking force in order to reduce risk value of positions on the pitch, allowing previously too risky positions to be included in the passing decision algorithm. The results show that AI using the proposed algorithm has better scoring chances and wins more matches than the same AI that does not recalculate the kicking force. This proves our methodology can increase the performance of ball passing for AI in soccer games.

关键词： Soccer Games Pareto Optimality multi-objective optimization problem Artificial Intelligence

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Air to Air Communications Based on UAV-enabled Virtual Antenna Arrays: A multi-objective optimization Approach

Air to Air Communications Based on UAV-enabled Virtual Anten...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Shi, Xiao Wang, Aimin Sun, Geng Li, Jiahui Zheng, Xiaoya Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)9781665442664

Due to the flexibility and high line-of-sight (LoS) probability, unmanned aerial vehicles (UAVs) can play an important role in 5G/6G networks. In this work, we study a novel air-to-air (A2A) communication scheme, in which two UAV swarms perform two UAV-enabled virtual antenna arrays (UVAAs) for exchanging data by using collaborative beamforming (CB). In order to improve the transmission efficiency and save the energy consumptions of the UAVs, we formulate an A2A communication multi-objective optimization problem (A2ACMOP) to simultaneously enhance the duplex transmission rates and reduce the total energy consumptions of the UAV swarms by deploying the UAVs and adjusting their excitation current weights. Due to the complexity and NP-hardness of the formulated A2ACMOP, we propose an improved non-dominated sorting genetic algorithm-III (INSGA-III) with opposition-based learning solution initialization and hybrid solution update operators to solve the problem. Simulation results verify that the proposed INSGA-III can effectively solve the formulated A2ACMOP and it has better performance than some other benchmark strategies.

关键词： UAV communications collaborative beamforming multi-objective optimization problem evolutionary algorithm

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