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

共 283 条记录，以下是21-30 订阅

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Hybrid Dynamic Resampling for Guided evolutionary multi-objective optimization 8

Hybrid Dynamic Resampling for Guided Evolutionary Multi-Obje...

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8th International Conference on evolutionary multi-Criterion optimization (EMO)

作者： Siegmund, Florian Ng, Amos H. C. Deb, Kalyanmoy Univ Skovde Virtual Syst Res Ctr Skovde Sweden Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

ISBN: (纸本)9783319159348;9783319159331

In Guided evolutionary multi-objective optimization the goal is to find a diverse, but locally focused non-dominated front in a decision maker's area of interest, as close as possible to the true Pareto-front. The optimization can focus its efforts towards the preferred area and achieve a better result [7,9,13,17]. The modeled and simulated systems are often stochastic and a common method to handle the objective noise is Resampling. The given preference information allows to define better resampling strategies which further improve the optimization result. In this paper, resampling strategies are proposed that base the sampling allocation on multiple factors, and thereby combine multiple resampling strategies proposed by the authors in [15]. These factors are, for example, the Pareto-rank of a solution and its distance to the decision maker's area of interest. The proposed hybrid Dynamic Resampling Strategy DR2 is evaluated on the Reference point-guided NSGA-II optimization algorithm (R-NSGA-II) [9].

关键词： evolutionary multi-objective optimization Guided search Reference point Dynamic resampling Budget allocation

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Efficient Design of Cosine-Modulated Filter Banks Using evolutionary multi-objective optimization

Efficient Design of Cosine-Modulated Filter Banks Using Evol...

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3rd International Conference on Swarm, evolutionary and Memetic Computing (SEMCCO)

作者： Nasir, Md. Sengupta, Soumyadip Das, Swagatam Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 India Indian Stat Inst Elect & Commun Sci Unit Kolkata India

ISBN: (纸本)9783642353796;9783642353802

We propose a novel and efficient way to design maximally decimated FIR cosine modulated filter banks, in which each analysis and synthesis filter has linear phase. We consider a class of near-perfect reconstruction CMFBs with the linear phase prototype filter, which structurally eliminates the amplitude overall distortion. The prototype filter design problem is then formulated into a multi-objective optimization problem (MOP), which aims at maximizing stop-band attenuation and minimizing reconstruction error simultaneously. We have modeled the design problem as a constrained multi-objective optimization problem which is efficiently solved by using a recently proposed algorithm MOEA/DFD. Experiment shows that the performance of MOEA/DFD exceeds that of MOEA/D and NSGA-II.

关键词： multi-rate filter Cosine modulated filter bank Prototype filter evolutionary multi-objective optimization

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Enhancing the Convergence Ability of evolutionary multi-objective optimization Algorithms with Momentum

Enhancing the Convergence Ability of Evolutionary Multi-obje...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Chen, Longcan Pang, Lie Meng Zhang, Qingfu Ishibuchi, Hisao Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

ISBN: (纸本)9798400704949

To improve the convergence ability of evolutionary multi-objective optimization algorithms (EMOAs), various strategies have been proposed. One effective strategy is to use good momentum from the previous generations to create new solutions. However, the definition of good momentum has not been carefully studied. In this paper, we propose five different definitions of good momentum for EMOAs. Then, we explain their integration into popular EMOAs such as NSGA-II, MOEA/D, and SMS-EMOA. Through computational experiments, we demonstrate that the use of an appropriate definition of good momentum greatly accelerates the convergence of EMOAs on both artificial test problems and real-world problems, particularly on large-scale problems.

关键词： evolutionary multi-objective optimization Large-scale multi-objective optimization Solution generation strategy

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Effects of Discretization of Decision and objective Spaces on the Performance of evolutionary multi-objective optimization Algorithms

Effects of Discretization of Decision and Objective Spaces o...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Chen, Weiyu Ishibuchi, Hisao Shang, Ke Southern Univ Sci & Technol SUSTech Dept Comp Sci & Engn Shenzhen Key Lab Computat Intelligence Univ Key Lab Evolving Intelligent Syst Guangdong Shenzhen Peoples R China

ISBN: (纸本)9781728124858

Recently, the discretization of decision and objective spaces has been discussed in the literature. In some studies, it is shown that the decision space discretization improves the performance of evolutionary multi-objective optimization (EMO) algorithms on continuous multi-objective test problems. In other studies, it is shown that the objective space discretization improves the performance on combinatorial multi-objective problems. However, the effect of the simultaneous discretization of both spaces has not been examined in the literature. In this paper, we examine the effects of the decision space discretization, objective space discretization and simultaneous discretization on the performance of NSGA-II through computational experiments on the DTLZ and WFG problems. Using various settings about the number of decision variables and the number of objectives, our experiments are performed on four types of problems: standard problems, large-scale problems, many-objective problems, and large-scale many-objective problems. We show that the decision space discretization has a positive effect for large-scale problems and the objective space discretization has a positive effect for many-objective problems. We also show the discretization of both spaces is useful for large-scale many-objective problems.

关键词： evolutionary multi-objective optimization multi-objective problems many-objective problems large-scale problems decision space discretization objective space discretization

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Research of evolutionary multi-objective optimization Algorithm Model Based on AIS

Research of Evolutionary Multi-objective Optimization Algori...

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International Conference on Materials Engineering and Mechanical Automation (MEMA 2013)

作者： Li, Ming-song Wang, Na Duan, Zhuo-hua ShaoGuan Univ Shaoguan Guangdong Peoples R China

ISBN: (纸本)9783037859018

Problem of multi-objective optimization based on Artificial Immune System (AIS) is an important research area of current evolutionary computing. Starting from the intelligent information processing mechanism of immune theory and the immune system itself, by researching the calculating model of immune evolution and Based on the biological mechanism of immune system, a general algorithm frame for solving optimization problem is proposed. The researching content has good theoretical value and practicability, also provides base for further research of specific type of problems.

关键词： Artificial Immune System evolutionary multi-objective optimization Algorithm Model

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Do We Really Need to Use Constraint Violation in Constrained evolutionary multi-objective optimization? 17th

Do We Really Need to Use Constraint Violation in Constrained...

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17th International Conference on Parallel Problem Solving from Nature (PPSN)

作者： Li, Shuang Li, Ke Li, Wei Harbin Inst Technol Control & Simulat Ctr Harbin Peoples R China Univ Exeter Dept Comp Sci Exeter EX4 5DS Devon England

ISBN: (纸本)9783031147210;9783031147203

Constraint violation has been a building block to design evolutionary multi-objective optimization algorithms for solving constrained multi-objective optimization problems. However, it is not uncommon that the constraint violation is hardly approachable in real-world blackbox optimization scenarios. It is unclear that whether the existing constrained evolutionary multi-objective optimization algorithms, whose environmental selection mechanism are built upon the constraint violation, can still work or not when the formulations of the constraint functions are unknown. Bearing this consideration in mind, this paper picks up four widely used constrained evolutionary multi-objective optimization algorithms as the baseline and develop the corresponding variants that replace the constraint violation by a crisp value. From our experiments on both synthetic and real-world benchmark test problems, we find that the performance of the selected algorithms have not been significantly influenced when the constraint violation is not used to guide the environmental selection. The supplementary material of this paper can be found in https://***/23dtdne8.

关键词： Constrained multi-objective optimization Constraint handling techniques evolutionary multi-objective optimization

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Modified Distance-based Subset Selection for evolutionary multi-objective optimization Algorithms

Modified Distance-based Subset Selection for Evolutionary Mu...

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IEEE Congress on evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Chen, Weiyu Ishibuchi, Hisao Shang, Ke Southern Univ Sci & Technol Guangdong Prov Key Lab Brain Inspired Intelligent Dept Comp Sci & Engn Shenzhen 518055 Peoples R China

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169293

evolutionary algorithms have been widely used to solve multi-objective optimization problems. Usually, the final population of an evolutionary algorithm is used as the output of multi-objective optimization. However, a current new trend is to select a pre-specified number of solutions from an unbounded external archive (UEA) as the final output of multi-objective optimization. Some subset selection methods have been proposed in the literature such as hypervolume-based and IGD-based selection. Recently, a distance-based subset selection (DSS) method was proposed for efficient subset selection from a large external archive. Whereas DSS efficiently finds a set of uniformly distributed solutions, it has some difficulties in the handling of solutions in the UEA as we demonstrate in this paper. To improve the performance of the DSS method, we propose a modified DSS method based on the IGD(+) distance instead of the Euclidean distance. Experimental results on various benchmark problems show that the modified DSS method performs better than or equal to the original DSS method on most test problems.

关键词： evolutionary multi-objective optimization distance-based solution subset selection unbounded external archive

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A Comparative Study of Dynamic Resampling Strategies for Guided evolutionary multi-objective optimization

A Comparative Study of Dynamic Resampling Strategies for Gui...

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IEEE Congress on evolutionary Computation

作者： Siegmund, Florian Ng, Amos H. C. Deb, Kalyanmoy Univ Skovde Virtual Syst Res Ctr S-54128 Skovde Sweden Indian Inst Technol Dept Mech Engn Kanpur 208016 Uttar Pradesh India

ISBN: (纸本)9781479904549;9781479904532

In evolutionary multi-objective optimization many solutions have to be evaluated to provide the decision maker with a diverse choice of solutions along the Pareto-front, in particular for high-dimensional optimization problems. In Simulation-based optimization the modeled systems are complex and require long simulation times. In addition the evaluated systems are often stochastic and reliable quality assessment of system configurations by resampling requires many simulation runs. As a countermeasure for the required high number of simulation runs caused by multiple optimization objectives the optimization can be focused on interesting parts of the Pareto-front, as it is done by the Reference point-guided NSGA-II algorithm (R-NSGA-II) [9]. The number of evaluations needed for the resampling of solutions can be reduced by intelligent resampling algorithms that allocate just as much sampling budget needed in different situations during the optimization run. In this paper we propose and compare resampling algorithms that support the R-NSGA-II algorithm on optimization problems with stochastic evaluation functions.

关键词： evolutionary multi-objective optimization simulation-based optimization guided search reference point decision support stochastic systems dynamic resampling

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Hybrid Dynamic Resampling Algorithms for evolutionary multi-objective optimization of Invariant-Noise Problems 19th

Hybrid Dynamic Resampling Algorithms for Evolutionary Multi-...

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19th European Conference on the Applications of evolutionary Computation (EvoApplications)

作者： Siegmund, Florian Ng, Amos H. C. Deb, Kalyanmoy Univ Skovde Sch Engn Sci Skovde Sweden Michigan State Univ Dept Elect & Comp Engn E Lansing MI 48824 USA

ISBN: (纸本)9783319311531

In Simulation-based evolutionary multi-objective optimization (EMO) the available time for optimization usually is limited. Since many real-world optimization problems are stochastic models, the optimization algorithm has to employ a noise compensation technique for the objective values. This article analyzes Dynamic Resampling algorithms for handling the objective noise. Dynamic Resampling improves the objective value accuracy by spending more time to evaluate the solutions multiple times, which tightens the optimization time limit even more. This circumstance can be used to design Dynamic Resampling algorithms with a better sampling allocation strategy that uses the time limit. In our previous work, we investigated Time-based Hybrid Resampling algorithms for Preference-based EMO. In this article, we extend our studies to general EMO which aims to find a converged and diverse set of alternative solutions along the whole Pareto-front of the problem. We focus on problems with an invariant noise level, i.e. a flat noise landscape.

关键词： evolutionary multi-objective optimization Simulationbased optimization Noise Dynamic resampling Budget allocation Hybrid

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Preferred Region Based evolutionary multi-objective optimization Using Parallel Coordinates Interface 3

Preferred Region Based Evolutionary Multi-Objective Optimiza...

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3rd International Symposium on Computational and Business Intelligence (ISCBI)

作者： Sato, Hiroyuki Tomita, Kouhei Miyakawa, Minami Univ Electrocommun Grad Sch Informat & Engn Sci 1-5-1 Chofugaoka Chofu Tokyo 182 Japan

ISBN: (纸本)9781467385015

This work proposes a novel preference based evolutionary multi and many-objective optimization approach to search a specific region of the Pareto front. First, to know the overview of the entire Pareto front, the proposed approach roughly approximates it by using a representative MOEA/D with uniformly distributed weight vectors. Then, the obtained solutions are plotted on the parallel coordinates user interface (UI). In the proposed approach, the decision maker's preference can be specified as a region in the objective space while the conventional approaches use a single preference point in the objective space. It has an advantage when the decision maker has poor knowledge about the target problem. Next, the proposed approach rearranges the weight vectors to determine the search directions in the objective space inside the preferred region and performs MOEA/D with the rearranged weight vectors. The parallel coordinates UI is particularly suited to rearrange weight vectors and compatible with MOEA/D. Experimental results using DLTZ2 problems with 2-6 objectives show the proposed approach improves the approximation performance of the specific region of Pareto front.

关键词： evolutionary multi-objective optimization preference based search interactive optimization

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