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Search Results: 1 - 10 of 16255 matches for " Non-dominated set "
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Multi-Objective Optimization Problems with Arena Principle and NSGA-II
Wang Dong-Feng,Xu Feng
Information Technology Journal , 2010,
Abstract: Existing test problems for multi-objective optimization are mainly criticized for high computational complexity. In this study, we introduce a new non- dominated sorting algorithm based on Pareto optimal solutions which alleviates the problem of high computational complexity in NSGA-II. We use the Arena Principle in NSGA-II to retain the non-dominated solutions found during the evolutionary process. The main goal of this work is to keep the fast convergence exhibited by Arena Principle in global optimization when extending this heuristic to multi-objective optimization. The algorithm’s computational complexity is O(rmN). We adopt two standard test functions and simulation results show that the Arena Principle is able to find more useful and better spread of solutions.
- , 2017, DOI: 10.11992/tis.201706013
Abstract: 提出一种基于膜优化理论的多目标优化算法,该算法受膜计算的启发,结合膜结构、多重集和反应规则来求解多目标优化问题。为了增强算法的适应能力,采用了遗传算法中的交叉与变异机制,同时在膜中引入外部档案集,并采用非支配排序和拥挤距离方法对外部档案集进行更新操作来提高搜索解的多样性。仿真实验采用标准的KUR和ZDT系列多目标问题对所提出的算法进行测试,通过该算法得出的非支配解集能够较好地逼近真实的Pareto前沿,说明所提算法在求解多目标优化问题上具有可行性和有效性。
In this paper, we propose a multi-objective optimization algorithm based on the theory of membrane optimization. Inspired by membrane computing, this algorithm combines membrane structure, multiple sets, and reaction rules to solve multi-objective optimization problems. We employ the crossover and mutation mechanism in this genetic algorithm to enhance its adaptability. We also introduce an external archive set into the membrane and design a non-dominated sorting and crowding distance method to improve the diversity of the global search solution and thereby update the introduced archive. We used multi-objective problems including KUR and ZDT to evaluate the performance of our proposed algorithm. Our results show that the non-dominated solution set derived from the proposed algorithm can better approach the real Pareto front, which confirms that the proposed algorithm is feasible and effective in solving multi-objective optimization problems
Non-dominated sorting differential evolution algorithm for multi-objective optimal PMU placement

Peng Chunhu,SUN Hui-juan,GUO Jian-feng,

控制理论与应用 , 2009,
Abstract: For a power grid to be completely observable when employing a minimal number of placed phasor measurement units(PMU) to achieve the highest reliability of the N-1 measurements, we propose a new hybrid algorithm to optimize this PMU multi-objective placement problem. In this algorithm, the Pareto non-dominated sorting mechanism is integrated with the differential evolution algorithm; meanwhile the individual crowding mechanism and the mutation strategy are improved to cope with the premature convergence and the search bias. Moreover, fuzzy set theory is employed to extract the best compromise non-dominated solution. Both the Pareto-optimal solution and the desired Pareto front can be rapidly found by the proposed algorithm. This is demonstrated by the results in the application to the IEEE 39-bus systems.
Fast method of constructing multi-objective Pareto non-dominated set:election principle

YANG Ping,ZHENG Jin-hu,LI Mi-qing,LUO Biao,

计算机应用研究 , 2009,
Abstract: The multi-objective optimization problem based on pareto is a important research direction of the evolutionary algorithm, and how to improve the efficiency of constructing the Pareto non-dominated set is a key to the algorithm.This paper proposed a quick method of constructing multi-objective pareto non-dominated set through observing the election phenomenon and understanding the mutual character of multi-objective individual, namely the election principle (EP), analyzed that its computational complexity was O(rmN),proved the EP works correctly. Because the number m of actual non-dominated individual is smaller than the population size N,compared with familiar methods the EP has a high efficiency and proves it through experiment finally.
Multi-Objective Optimization for Active Disturbance Rejection Control for the ALSTOM Benchmark Problem  [PDF]
Chun’e Huang, Zhongli Liu
International Journal of Clean Coal and Energy (IJCCE) , 2015, DOI: 10.4236/ijcce.2015.43006
Abstract: Based on a thing that it is difficult to choose the parameters of active disturbance rejection control for the non-linear ALSTOM gasifier, multi-objective optimization algorithm is applied in the choose of parameters. Simulation results show that performance tests in load change and coal quality change achieve better dynamic responses and larger scales of rejecting coal quality disturbances. The study provides an alternative to choose parameters for other control schemes of the ALSTOM gasifier.
Data envelopment analysis
Quanling Wei
Chinese Science Bulletin , 2001, DOI: 10.1007/BF03183382
Abstract: This review introduces the history and present status of data envelopment analysis (DEA) research, particularly the evaluation process. And extensions of some DEA models are also described. It is pointed out that mathematics, economics and management science are the main forces in the DEA development, optimization provides the fundamental method for the DEA research, and the wide range of applications enforces the rapid development of DEA.
Modified NSGA-II for a Bi-Objective Job Sequencing Problem  [PDF]
Susmita Bandyopadhyay
Intelligent Information Management (IIM) , 2012, DOI: 10.4236/iim.2012.46036
Abstract: This paper proposes a better modified version of a well-known Multi-Objective Evolutionary Algorithm (MOEA) known as Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The proposed algorithm contains a new mutation algorithm and has been applied on a bi-objective job sequencing problem. The objectives are the minimization of total weighted tardiness and the minimization of the deterioration cost. The results of the proposed algorithm have been compared with those of original NSGA-II. The comparison of the results shows that the modified NSGA-II performs better than the original NSGA-II.
A Multiobjective Optimization Method for Designing M-Channel NPR Cosine Modulated Filter Bank for Image Compression  [PDF]
Anamika Jain, Aditya Goel
Engineering (ENG) , 2015, DOI: 10.4236/eng.2015.72008
Abstract: This paper proposes a method to design multichannel cosine modulated filter bank for image compression using multiobjective optimization technique. The design problem is a combination of stopband residual energy, least square error of the overall transfer function of the filter bank, coding gain with dc leakage free condition as constraint. The proposed algorithm uses Non-dominated Sorting Genetic Algorithm (NSGA) to minimize the mutually contradictory objective function by minimizing filter tap weights of prototype filter. The algorithm solves this problem by searching solutions that achieve the best compromise between the different objectives criteria. The performance of this algorithm is evaluated in terms of coding gain and peak signal to noise ratio (PSNR). Simulation results on different images are included to illustrate the effectiveness of the proposed algorithm for image compression application.
Emergency strategy operation optimization for atmosphere environment control system in space station

- , 2017, DOI: 10.13700/j.bh.1001-5965.2016.0664
Abstract: 摘要 空间站大气环控系统(ECS)由多个相互耦合的子系统组成,主要控制舱室气体成分和环境参数,对保障航天员生命安全具有重要意义。该系统正常运行严重依赖于供电系统的工作稳定性,因此长期在轨运行要求ECS应具有适应供电不足的应急运行能力。针对可能面临的供电不足情况,开展了大气ECS应急运行策略优化研究。为了研究出多约束多目标优化问题,首先建立了大气ECS物质、能量和功耗模型,并提出了非再生物资使用时长评估函数。其次以非再生物资使用时长最大和电能需求最小为目标函数,以子系统可调的运行参数为优化参数,在舱室五大环境参数的约束下,采用快速非支配排序遗传算法-Ⅱ(NSGA-Ⅱ)获得了ECS Pareto最优解集,进而获得了Pareto最优前沿(POF)。由于多目标函数具有相同重要性,最终可从POF上获得了大气ECS应急运行策略。优化研究结果表明:该方法能够确定不足电能情况下各子系统的应急电能最优分配方案,从而确定出应急时的子系统最优重构运行方案,以保证最大系统使用时长和最小电能需求的要求。
Abstract:Environment control system (ECS) in a space station is an essential system for the astronaut life safety. Composed of a number of coupling subsystems, the ECS functions as the controller of the cabin air condition and the environmental parameters, which are essential parts to the astronauts' life safety. As its subsystems are power-consuming, the system's regular operation highly relies on the stability of the power supply system and the ECS should have the ability to reconfigure operation strategy in some emergency conditions. In this paper, the emergency strategy optimization method of ECS is studied under potential insufficient power supply condition. To study this issue, we establish basic ECS mathematical models involving its substances, energy as well as consumption, and the non-regenerative substance lifetime conception, which represents the remaining amount of non-regenerative life support substance. A multi-objective optimization method is developed to search the ECS emergency strategy. The maximum non-regenerative substance lifetime and the minimum power consumption are chosen as the optimization objective functions. Some adjustable key variables are chosen as the optimal variables, which represent the way to reconfigure the operation strategy. With the constraints of 5 main environmental parameters, the non-dominated sorting genetic algorithm-Ⅱ (NSGA-Ⅱ) is adopted to obtain the Pareto optimal solution set and the Pareto optimal frontier (POF). The results of optimization research show that the presented optimal method can obtain the optimal emergency electric energy allocation strategy for subsystems under different insufficient power supply conditions, and the optimal reconfigured operation strategy can meet the maximum system lifetime and minimum system supply energy requirement.
Optimization of axial compressor stage using NSGA-II technique
G. Chaitanya,J. Suresh Kumar,K Srinivas
Journal of Engineering and Applied Sciences , 2010,
Abstract: Efficiency and Stage Weight [Inlet stage specific Area] are two important design issues which demand specific attention in the design of aero space compressors. In this paper these two objectives were optimized using elitist multi objective genetic algorithm, otherwise known as NSGA-II (Non dominated sorted Genetic Algorithm-II) which was developed by Kalyan Moy Deb [2002]. Lingen Chen and Fengrui Sun (2005) implemented optimum design of a subsonic axial flow compressor stage using mean line prediction method and taking 12 design variables and three objective functions. In the present approach two objective functions were formulated taking 5 design variables into account. The results showing optimal front for the two objectives problem is presented and the sensitivity analysis results of influencing design variables are shown.
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