Results 1 - 10 of 25,969

Solving Constrained Multi-Objective Problems by Objective Space Analysis

by Gideon Avigad, C. A. Coello Coello
"... In this paper a new approach to solve constrained multi-objective problems by way of evolutionary multi-objective optimization is introduced. In contrast to former evolutionary approaches, which amalgamate objective space dominance relations with feasibility of solutions considered in the design spa ..."
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In this paper a new approach to solve constrained multi-objective problems by way of evolutionary multi-objective optimization is introduced. In contrast to former evolutionary approaches, which amalgamate objective space dominance relations with feasibility of solutions considered in the design

A Fast and Elitist Multi-Objective Genetic Algorithm: NSGA-II

by Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, T. Meyarivan , 2000
"... Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) O(MN computational complexity (where M is the number of objectives and N is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing param ..."
Abstract - Cited by 1815 (60 self) - Add to MetaCart
to solve constrained multi-objective problems eciently. Simulation results of the constrained NSGA-II on a number of test problems, including a five-objective, seven-constraint non-linear problem, are compared with another constrained multi-objective optimizer and much better performance of NSGA

An efficient multi-objective evolutionary algorithm: OMOEA-II

by Sanyou Zeng, Yuping Chen - Third International Conference on Evolutionary Multi-Criterion Optimization (EMO 2005), volume 3410 of Lecture Notes in Computer Science , 2005
"... Abstract- A new algorithm is proposed to solve constrained multi-objective problems in this paper. The constraints of the MOPs are taken account of in determining Pareto dominance. As a result, the feasibility of solutions is not an issue. At the same time, it takes advantage of both the orthogonal ..."
Abstract - Cited by 13 (1 self) - Add to MetaCart
Abstract- A new algorithm is proposed to solve constrained multi-objective problems in this paper. The constraints of the MOPs are taken account of in determining Pareto dominance. As a result, the feasibility of solutions is not an issue. At the same time, it takes advantage of both the orthogonal

A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimization: NSGA-II

by Kalyanmoy Deb, Samir Agrawal, Amrit Pratap, T Meyarivan , 2000
"... Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have been mainly criticized for their (i) -4 computational complexity (where is the number of objectives and is the population size), (ii) non-elitism approach, and (iii) the need for specifying a sharing ..."
Abstract - Cited by 662 (15 self) - Add to MetaCart
to find much better spread of solutions in all problems compared to PAES---another elitist multi-objective EA which pays special attention towards creating a diverse Pareto-optimal front. Because of NSGA-II's low computational requirements, elitist approach, and parameter-less sharing approach

A Limited Memory Algorithm for Bound Constrained Optimization

by Richard H. Byrd, Peihuang Lu, Jorge Nocedal, Ciyou Zhu - SIAM JOURNAL ON SCIENTIFIC COMPUTING , 1994
"... An algorithm for solving large nonlinear optimization problems with simple bounds is described. It is based ..."
Abstract - Cited by 572 (9 self) - Add to MetaCart
An algorithm for solving large nonlinear optimization problems with simple bounds is described. It is based

SNOPT: An SQP Algorithm For Large-Scale Constrained Optimization

by Philip E. Gill, Walter Murray, Michael A. Saunders , 2002
"... Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first deriv ..."
Abstract - Cited by 597 (24 self) - Add to MetaCart
Sequential quadratic programming (SQP) methods have proved highly effective for solving constrained optimization problems with smooth nonlinear functions in the objective and constraints. Here we consider problems with general inequality constraints (linear and nonlinear). We assume that first

Constrained model predictive control: Stability and optimality

by D. Q. Mayne, J. B. Rawlings, C. V. Rao, P. O. M. Scokaert - AUTOMATICA , 2000
"... Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon open-loop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence and t ..."
Abstract - Cited by 738 (16 self) - Add to MetaCart
Model predictive control is a form of control in which the current control action is obtained by solving, at each sampling instant, a finite horizon open-loop optimal control problem, using the current state of the plant as the initial state; the optimization yields an optimal control sequence

Constrained K-means Clustering with Background Knowledge

by Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan Schroedl - In ICML , 2001
"... Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be pro tably modi- ed ..."
Abstract - Cited by 488 (9 self) - Add to MetaCart
Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be pro tably modi- ed

Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks

by Ros Tassiulas, Anthony Ephremides - IEEE Transactions on Automatic Control , 1992
"... Abstruct-The stability of a queueing network with interdependent servers is considered. The dependency of servers is described by the definition of their subsets that can be activated simultaneously. Multihop packet radio networks (PRN’s) provide a motivation for the consideration of this system. We ..."
Abstract - Cited by 949 (19 self) - Add to MetaCart
. We study the problem of scheduling the server activation under the constraints imposed by the dependency among them. The performance criterion of a scheduling policy m is its throughput that is characterized by its stability region C,, that is, the set of vectors of arrival rates for which the system

Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems

by Mário A. T. Figueiredo, Robert D. Nowak, Stephen J. Wright - IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING , 2007
"... Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a spa ..."
Abstract - Cited by 539 (17 self) - Add to MetaCart
Many problems in signal processing and statistical inference involve finding sparse solutions to under-determined, or ill-conditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a
Results 1 - 10 of 25,969