Results 221 to 230 of about 50,463 (272)
Some of the next articles are maybe not open access.
Evolutionary multiobjective optimization
WIREs Data Mining and Knowledge Discovery, 2011AbstractThis paper presents a very short introduction to multiobjective evolutionary algorithms, including their basic concepts and their main components. The discussion focuses on algorthmic design and, therefore, the issues discussed include selection mechanisms, diversity maintenance mechanisms, and elitism in a multi‐objective context.
openaire +2 more sources
Evolutionary multiobjective optimization
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation, 2007Many real-world search and optimization problems are naturally posed as non-linear programming problems having multiple conflicting objectives.Due to lack of suitable solution techniques, such problems are usually artificially converted into a single-objective problem and solved.
Eckart Zitzler, Kalyanmoy Deb
openaire +1 more source
Evolutionary multiobjective optimization and multiobjective fuzzy system design
Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology - CSTST '08, 2008Evolutionary multiobjective optimization (EMO) is one of the most active research areas in evolutionary computation. EMO algorithms have been successfully used in various application areas. Among them are multiobjective design of neural networks and fuzzy systems. Especially, fuzzy system design has often been discussed as multiobjective problems. This
openaire +1 more source
Multiobjective Prices of Stability and Anarchy for Multiobjective Games
2014We generalize the prices of stability and anarchy to multiobjective games. In the singleobjective case, the loss of overall efficiency induced by selfish behaviors is a deeply studied subject. While the price of anarchy bounds above the loss of efficiency of equilibria, the price of stability bounds it below.
openaire +1 more source
Multiobjective data clustering
Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004., 2004Conventional clustering algorithms utilize a single criterion that may not conform to the diverse shapes of the underlying clusters. We offer a new clustering approach that uses multiple clustering objective functions simultaneously. The proposed multiobjective clustering is a two-step process.
Martin H. C. Law +2 more
openaire +2 more sources
IEEE Transactions on Evolutionary Computation, 2018
This paper presents a new particle swarm optimizer for solving multimodal multiobjective optimization problems which may have more than one Pareto-optimal solution corresponding to the same objective function value.
Caitong Yue, B. Qu, Jing J. Liang
semanticscholar +1 more source
This paper presents a new particle swarm optimizer for solving multimodal multiobjective optimization problems which may have more than one Pareto-optimal solution corresponding to the same objective function value.
Caitong Yue, B. Qu, Jing J. Liang
semanticscholar +1 more source
DUALITY OF MULTIOBJECTIVE PROBLEMS
Mathematics of the USSR-Sbornik, 1988The duality of multicriteria problems is studied by means of the conjugate many-valued mappings. This class of mappings is analysed in detail in a special part of the paper. The results are used for the analysis of the interconnection of the corresponding Pareto sets and its properties.
openaire +1 more source
Multiobjective query optimization
Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, 2001The optimization of queries in distributed database systems is known to be subject to delicate trade-offs. For example, the Mariposa database system allows users to specify a desired delay-cost tradeoff (that is, to supply a decreasing function u(d), specifying how much the user is willing to pay in order to receive the query results within time d ...
Christos H. Papadimitriou +1 more
openaire +1 more source
IEEE Transactions on Evolutionary Computation, 2019
Constrained multiobjective optimization problems (CMOPs) are frequently encountered in real-world applications, which usually involve constraints in both the decision and objective spaces.
Zhi-Zhong Liu, Yong Wang
semanticscholar +1 more source
Constrained multiobjective optimization problems (CMOPs) are frequently encountered in real-world applications, which usually involve constraints in both the decision and objective spaces.
Zhi-Zhong Liu, Yong Wang
semanticscholar +1 more source
A novel elitist multiobjective optimization algorithm: Multiobjective extremal optimization
European Journal of Operational Research, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Min-Rong Chen, Yong-Zai Lu
openaire +1 more source