Results 221 to 230 of about 141,498 (264)

Crossover for Cardinality Constrained Optimization

ACM Transactions on Evolutionary Learning and Optimization, 2022
To understand better how and why crossover can benefit constrained optimization, we consider pseudo-Boolean functions with an upper bound B on the number of 1-bits allowed in the length- n bit string (i.e., a cardinality constraint).
Tobias Friedrich 0001, Timo Kötzing, Aishwarya Radhakrishnan, Leon Schiller, Martin Schirneck, Georg Tennigkeit, Simon Wietheger   +6 more
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Linearly constrained optimization

Computing, 1989
For linearly constrained optimization problems an algorithm is presented which is based on conjugate gradients. Theoretical considerations are not given. Some numerical tests demonstrate a good behaviour of the algorithm.
G. M. Ostrovsky, M. G. Ostrovsky, T. A. Berezinsky   +2 more
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PESO+for Constrained Optimization

2006 IEEE International Conference on Evolutionary Computation, 2006
We introduce the PESO+algorithm (Particle Evolutionary Swarm Optimization Plus) for the solution of single objective constrained optimization problems. A novel feature introduced by PESO+is an external archive to store and retrieve “ tolerant” particles found at past tolerance values.
Angel Eduardo Muñoz-Zavala, Arturo Hernández Aguirre, Enrique Raúl Villa Diharce, Salvador Botello Rionda   +3 more
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Butterfly Constrained Optimizer for Constrained Optimization Problems

2018
An extension of the new optimization algorithm, butterfly optimizer (BO) for the constrained optimization problem is discussed in this paper. This version of BO is called butterfly constrained optimizer (BCO) which mimics the mate-locating behaviors of male butterfly and his behavior toward sunspots.
Abhishek Kumar, Tarun Maini, Rakesh Kumar Misra, Devender Singh   +3 more
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On Constrained Discontinuous Optimization

1998
In this paper we extend the results of Ermoliev, Norkin and Wets [8] and Ermoliev and Norkin [7] to the case of constrained discontinuous optimization problems. General optimality conditions for problems with nonconvex feasible sets are obtained. Easily implementable random search technique is proposed.
Ermoliev, Y.M., Norkin, V.I.
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Constrained optimal estimation and control

Automatica, 1997
The classical theories of the linear quadratic regulator and of the linear Gaussian estimator define the full gain matrix completely. However, many control and estimation problems would benefit from prescribing a different structure to the gain matrix. Typical is the case of the output feedback control, object of a wide literature since the 1970s.
ARDUINI, Carlo, CURTI, Fabio
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On optimality conditions for cone-constrained optimization

Proceedings of the 41st IEEE Conference on Decision and Control, 2002., 2003
We consider feasible sets given by conic constraints, where the cone defining the constraints is convex with nonempty interior. We study the case where the feasible set is not assumed to be regular in the classical sense of Robinson and obtain a constructive description of the tangent cone under a certain new second-order regularity condition.
Alexey F. Izmailov, Mikhail V. Solodov
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Improved constrained ordinal optimization for simulation-based constrained optimization

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009
The performance optimization of many man-made systems belong to simulation-based constrained optimization (SBCO), where the evaluation of both the performance and the constraint have no closed form expression and are based on simulation. The simulation-based estimate of both the performance and the feasibility are usually time-consuming and noisy.
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Optimal Design and Constrained Quasiconvexity

SIAM Journal on Mathematical Analysis, 2000
The author considers an optimal design problem of the form \[ \text{Minimize }I(g) = \int_\Omega W(x,g(x),w(x), \nabla w(x)) dx, \] with \(g(x) \in \{a,b\}\), \({1\over |\Omega|} \int_\Omega g = \lambda a + (1 - \lambda)b\), \(\lambda \in(0,1)\) is fixed, \(w\in H^1_0(\Omega)\) is the solution of -div\((g \nabla w)=f\) for a given \(f \in H^{-1}(\Omega)
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Particle Swarm Optimizer for constrained optimization

2013 IEEE Congress on Evolutionary Computation, 2013
Recently, Particle Swarm Optimizer (PSO) has become a popular tool for solving constrained optimization problems. However, there is no guarantee that PSO will perform consistently well for all problems and will not be trapped in local optima. In this paper, a PSO algorithm is introduced that uses two new mechanisms, the first one to maintain a better ...
Samir M. Mohamed Elsayed, Ruhul A. Sarker, Efrén Mezura-Montes   +2 more
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