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The particel swarm: Explosion, stability, and convergence in a multidimensional complex space
 IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTION
"... The particle swarm is an algorithm for finding optimal regions of complex search spaces through interaction of individuals in a population of particles. Though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately explained ..."
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Cited by 862 (10 self)
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The particle swarm is an algorithm for finding optimal regions of complex search spaces through interaction of individuals in a population of particles. Though the algorithm, which is based on a metaphor of social interaction, has been shown to perform well, researchers have not adequately
Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems
 IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
, 2007
"... Many problems in signal processing and statistical inference involve finding sparse solutions to underdetermined, or illconditioned, 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 ..."
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Cited by 539 (17 self)
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Many problems in signal processing and statistical inference involve finding sparse solutions to underdetermined, or illconditioned, linear systems of equations. A standard approach consists in minimizing an objective function which includes a quadratic (squared ℓ2) error term combined with a
WaitFree Synchronization
 ACM Transactions on Programming Languages and Systems
, 1993
"... A waitfree implementation of a concurrent data object is one that guarantees that any process can complete any operation in a finite number of steps, regardless of the execution speeds of the other processes. The problem of constructing a waitfree implementation of one data object from another lie ..."
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Cited by 849 (28 self)
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A waitfree implementation of a concurrent data object is one that guarantees that any process can complete any operation in a finite number of steps, regardless of the execution speeds of the other processes. The problem of constructing a waitfree implementation of one data object from another
Iterative point matching for registration of freeform curves and surfaces
, 1994
"... A heuristic method has been developed for registering two sets of 3D curves obtained by using an edgebased stereo system, or two dense 3D maps obtained by using a correlationbased stereo system. Geometric matching in general is a difficult unsolved problem in computer vision. Fortunately, in ma ..."
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Cited by 663 (8 self)
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, in many practical applications, some a priori knowledge exists which considerably simplifies the problem. In visual navigation, for example, the motion between successive positions is usually approximately known. From this initial estimate, our algorithm computes observer motion with very good precision
Maxmargin Markov networks
, 2003
"... In typical classification tasks, we seek a function which assigns a label to a single object. Kernelbased approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from the ..."
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Cited by 602 (15 self)
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In typical classification tasks, we seek a function which assigns a label to a single object. Kernelbased approaches, such as support vector machines (SVMs), which maximize the margin of confidence of the classifier, are the method of choice for many such tasks. Their popularity stems both from
On the Effect of Selection and Archiving Operators in ManyObjective Particle Swarm Optimisation
"... The particle swarm optimisation (PSO) heuristic has been used for a number of years now to perform multiobjective optimisation, however its performance on manyobjective optimisation (problems with four or more competing objectives) has been less well examined. Manyobjective optimisation is wel ..."
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The particle swarm optimisation (PSO) heuristic has been used for a number of years now to perform multiobjective optimisation, however its performance on manyobjective optimisation (problems with four or more competing objectives) has been less well examined. Manyobjective opti
Multiobjective Evolutionary Algorithms: Analyzing the StateoftheArt
, 2000
"... Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mideighties in an attempt to stochastically solve problems of this generic class. During the past decade, ..."
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Cited by 435 (7 self)
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Solving optimization problems with multiple (often conflicting) objectives is, generally, a very difficult goal. Evolutionary algorithms (EAs) were initially extended and applied during the mideighties in an attempt to stochastically solve problems of this generic class. During the past decade
Multiswarm that learns ∗ by
"... Abstract: This paper studies particle swarm optimization approach enriched by two versions of an extension aimed at gathering information during the optimization process. Application of these extensions, called memory mechanisms, increases computational cost, but it is spent to a benefit by incorpor ..."
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Cited by 1 (0 self)
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by incorporating the knowledge about the problem into the algorithm and this way improving its search abilities. The first mechanism is based on the idea of storing explicit solutions while the second one applies onepass clustering algorithm to build clusters containing search experiences. The main disadvantage
A New Evolutionary Decision Theory for ManyObjective Optimization Problems
"... Abstract. In this paper the authors point out that the Pareto Optimality is unfair, unreasonable and imperfect for Manyobjective Optimization Problems (MOPs) underlying the hypothesis that all objectives have equal importance. The key contribution of this paper is the discovery of the new definitio ..."
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the hypothesis that all objectives in the problem have equal importance. Two new evolutionary algorithms are given, where ε dominance is used as a selection strategy with the winning score as an elite strategy for searchoptimal solutions. Two benchmark problems are designed for testing the new concepts of manyobjective
Results 1  10
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915,062