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Benchmarking multiobjective optimizers 2.0

Dimo Brockhoff, Tea Tušar
2021 Proceedings of the Genetic and Evolutionary Computation Conference Companion  
Mastertitelformat bearbeiten  Choose some algorithms  Choose some test functions  Discuss the results Isn't Benchmarking Trivial?  ...  Many-objective test problems to visually examine the behavior of multiobjective evolution in a decision space.  ...  points have a ratio of zero  Visualize dominance ratios in logarithmic scale From a grid point, follow the path in the direction of this gradient  Visualize the length of the path to the local optimum  ... 
doi:10.1145/3449726.3461421 fatcat:2nsfe43mgzhcjdg4ihct4ewxva

GECCO 2022 tutorial on benchmarking multiobjective optimizers 2.0

Dimo Brockhoff, Tea Tušar
2022 Proceedings of the Genetic and Evolutionary Computation Conference Companion  
Many-objective test problems to visually examine the behavior of multiobjective evolution in a decision space. In Parallel [Köppen et al. 2005] M. Köppen, R.  ...  Visual examination of the behavior of EMO many-objective test problem suite.  ... 
doi:10.1145/3520304.3533635 fatcat:lkna6purcve6bmv3indbgci2xy

An Optimization Framework of Multiobjective Artificial Bee Colony Algorithm Based on the MOEA Framework

Jiuyuan Huo, Liqun Liu
2018 Computational Intelligence and Neuroscience  
The framework was tested on the Walking Fish Group test suite, and a many-objective water resource planning problem was utilized for verification and application.  ...  The artificial bee colony (ABC) algorithm has become one of the popular optimization metaheuristics and has been proven to perform better than many state-of-the-art algorithms for dealing with complex  ...  Acknowledgments is work was supported by the National Nature Science Foundation of China (Grant nos. 61462058 and 61862038).  ... 
doi:10.1155/2018/5865168 fatcat:a673rtxwfre35bu24vj4nkkove

Evolutionary Multiparty Distance Minimization [article]

Zeneng She, Wenjian Luo, Xin Lin, Yatong Chang, Yuhui Shi
2022 arXiv   pre-print
In the field of evolutionary multiobjective optimization, the decision maker (DM) concerns conflicting objectives.  ...  Multiparty multiobjective optimization problems (MPMOPs) are proposed to depict the MOP with multiple decision makers involved, where each party concerns about certain some objectives of all.  ...  Acknowledgment This study is supported by the National Natural Science Foundation of China (Grant No. 61573327), and the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001).  ... 
arXiv:2207.13390v1 fatcat:6y5kwewcezbczhkhubyjbqj6hq

Scalability in Multiobjective Optimization (Dagstuhl Seminar 20031)

Carlos M. Fonseca, Kathrin Klamroth, Günter Rudolph, Margaret M. Wiecek
2020 Dagstuhl Reports  
The seminar focused on three main aspects of scalability in multiobjective optimization (MO) and their interplay, namely (1) MO with many objective functions, (2) MO with many decision makers, and (3)  ...  The continuing goal of this series is to strengthen the links between the Evolutionary Multiobjective Optimization (EMO) and the Multiple Criteria Decision Making (MCDM) communities, two of the largest  ...  This research is related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, of the University of Jyvaskyla.  ... 
doi:10.4230/dagrep.10.1.52 dblp:journals/dagstuhl-reports/FonsecaKRW20 fatcat:aicmczzbtjftdkjbry2m5nulge

Multiobjective evolutionary algorithms: A survey of the state of the art

Aimin Zhou, Bo-Yang Qu, Hui Li, Shi-Zheng Zhao, Ponnuthurai Nagaratnam Suganthan, Qingfu Zhang
2011 Swarm and Evolutionary Computation  
By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run.  ...  A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions.  ...  Acknowledgements This work is partly supported by National Basic Research Program of China (No. 2011CB707104) and National Science Foundation of China (No. 61005050).  ... 
doi:10.1016/j.swevo.2011.03.001 fatcat:jfcghitjp5ap5he4d3ackhhjsu

Enabling Dominance Resistance in Visualisable Distance-Based Many-Objective Problems

Jonathan E. Fieldsend
2016 Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO '16 Companion  
Journal of General Microbiology, Many-Objective Test Problem for Visually Examining 17(1):201–226, 1957. Diversity Maintenance Behavior in a Decision Space.  ...  In Parallel Problem Solving Many-Objective and Many-Variable Test Problems for from Nature, pages 832–842. Springer, 2004. Visual Examination of Multiobjective Search.  ... 
doi:10.1145/2908961.2935616 dblp:conf/gecco/Fieldsend16 fatcat:nfwr6w4ulfh3tbmxbcedgm57ya

An Empirical Study of Cluster-Based MOEA/D Bare Bones PSO for Data Clustering

Daphne Teck Ching Lai, Yuji Sato
2021 Algorithms  
Recently, researchers proposed such techniques to find better solutions in the objective space to solve engineering problems.  ...  This is an added layer of search control in the objective space.  ...  The approach generated a reduced Pareto set in many-objective optimization (MaOO) problems to solve its scaling problem.  ... 
doi:10.3390/a14110338 fatcat:ekyhsmgpq5hohiqopstvhq6ouy

Multiobjective hBOA, clustering, and scalability

Martin Pelikan, Kumara Sastry, David E. Goldberg
2005 Proceedings of the 2005 conference on Genetic and evolutionary computation - GECCO '05  
The algorithm mohBOA is shown to scale up well on a number of problems on which standard multiobjective evolutionary algorithms perform poorly.  ...  (NSGA-II) and clustering in the objective space.  ...  The U.S. Government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation thereon.  ... 
doi:10.1145/1068009.1068122 dblp:conf/gecco/PelikanSG05 fatcat:pmfsfargbnaifm2p3dlqiganuu

Multiobjective hBOA, Clustering, and Scalability [article]

Martin Pelikan, Kumara Sastry, David E. Goldberg
2005 arXiv   pre-print
The algorithm mohBOA is shown to scale up well on a number of problems on which standard multiobjective evolutionary algorithms perform poorly.  ...  The algorithm mohBOA differs from the multiobjective variants of BOA and hBOA proposed in the past by including clustering in the objective space and allocating an approximately equally sized portion of  ...  The U.S. Government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation thereon.  ... 
arXiv:cs/0502034v1 fatcat:sryvmj3ylfc4vj46vafb5pgcia

A new competitive multiverse optimization technique for solving single‐objective and multiobjective problems

Ilyas Benmessahel, Kun Xie, Mouna Chellal
2020 Engineering Reports  
The performance of our proposed method in single-objective optimization is demonstrated on a large number of mathematical problems of standard benchmark.  ...  The results obtained confirm the superior overall performance of CMVO in terms of the quality of obtained solutions, computational efficiency, and convergence speed relative to many of the state-of-the-art  ...  A multiobjective problem is often mathematically formed in terms of a set of solutions that contain Pareto optimal front instead of a single optimal one in a single-objective problem.  ... 
doi:10.1002/eng2.12124 fatcat:eg6hepznvjcdzcg5raj7facmqe

Many-Objective Optimization of a Hybrid Car Controller [chapter]

Tobias Rodemann, Kaname Narukawa, Michael Fischer, Mohammed Awada
2015 Lecture Notes in Computer Science  
However, prior work basically targets a minimization of fuel consumption. In this work we present a many-objective evolutionary optimization that considers up to 7 objectives in parallel.  ...  to solving dynamic optimisation problems This paper studies the idea of separating the explored and unexplored regions in the search space to improve change detection and optima tracking.  ...  PowerSurge: A Serious Game on Power Transmission Networks Recently, the interest on multiobjective optimization problems with a large number of decision variables has grown since many significant real  ... 
doi:10.1007/978-3-319-16549-3_48 fatcat:yvlv6xnggnf3jn7vdnnwih6chy

Neuroevolution for solving multiobjective knapsack problems

Roman Denysiuk, António Gaspar-Cunha, Alexandre C.B. Delbem
2019 Expert systems with applications  
The main contribution of this study resides in developing a solution encoding and genotype-phenotype mapping for EMOAs to solve MOKPs.  ...  The multiobjective knapsack problem (MOKP) is an important combinatorial problem that arises in various applications, including resource allocation, computer science and finance.  ...  Acknowledgments This work was supported by the Portuguese "Fundação para a Ciência e Tecnologia" under grant PEst-C/CTM/LA0025/2013 (Projecto Estratégico -LA 25 -2013 -2014 -Strategic Project -LA 25 -  ... 
doi:10.1016/j.eswa.2018.09.004 fatcat:yzhdpcjxqrc53looo7nrpwuqty

Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms

A.M. Brintrup, J. Ramsden, H. Takagi, A. Tiwari
2008 IEEE Transactions on Evolutionary Computation  
Three different algorithms based on the framework are developed, and tested with an ergonomic chair design problem.  ...  The framework is rooted in multiobjective optimization, genetic algorithms, and interactive user evaluation.  ...  The basis of multiobjective optimization comes from the need to achieve compromise decision-making in a problem of many conflicting objectives.  ... 
doi:10.1109/tevc.2007.904343 fatcat:jpxri7ypxvbqhm7cuyxboyiepu

Variants of Differential Evolution for Multi-Objective Optimization

Karin Zielinski, Rainer Laur
2007 2007 IEEE Symposium on Computational Intelligence in Multi-Criteria Decision-Making  
In this work four variants of Differential Evolution are examined that differ in the selection scheme and in the assignment of crowding distance.  ...  In multi-objective optimization not only fast convergence is important, but it is also necessary to keep enough diversity so that the whole Pareto-optimal front can be found.  ...  In this work the performance of the DE variants is evaluated in dependence on the number of generations to allow a more complete assessment of their behavior, based on several well known bi-objective test  ... 
doi:10.1109/mcdm.2007.369422 dblp:conf/cimcdm/ZielinskiL07 fatcat:m6ljdiizxvekppsjjix6gbgxle
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