4,760 research outputs found
Adaptive Ranking Based Constraint Handling for Explicitly Constrained Black-Box Optimization
A novel explicit constraint handling technique for the covariance matrix
adaptation evolution strategy (CMA-ES) is proposed. The proposed constraint
handling exhibits two invariance properties. One is the invariance to arbitrary
element-wise increasing transformation of the objective and constraint
functions. The other is the invariance to arbitrary affine transformation of
the search space. The proposed technique virtually transforms a constrained
optimization problem into an unconstrained optimization problem by considering
an adaptive weighted sum of the ranking of the objective function values and
the ranking of the constraint violations that are measured by the Mahalanobis
distance between each candidate solution to its projection onto the boundary of
the constraints. Simulation results are presented and show that the CMA-ES with
the proposed constraint handling exhibits the affine invariance and performs
similarly to the CMA-ES on unconstrained counterparts.Comment: 9 page
Multiobjective optimization of electromagnetic structures based on self-organizing migration
Práce se zabĂ˝vá popisem novĂ©ho stochastickĂ©ho vĂcekriteriálnĂho optimalizaÄŤnĂho algoritmu MOSOMA (Multiobjective Self-Organizing Migrating Algorithm). Je zde ukázáno, Ĺľe algoritmus je schopen Ĺ™ešit nejrĹŻznÄ›jšà typy optimalizaÄŤnĂch Ăşloh (s jakĂ˝mkoli poÄŤtem kritĂ©riĂ, s i bez omezujĂcĂch podmĂnek, se spojitĂ˝m i diskrĂ©tnĂm stavovĂ˝m prostorem). VĂ˝sledky algoritmu jsou srovnány s dalšĂmi běžnÄ› pouĹľĂvanĂ˝mi metodami pro vĂcekriteriálnĂ optimalizaci na velkĂ© sadÄ› testovacĂch Ăşloh. Uvedli jsme novou techniku pro vĂ˝poÄŤet metriky rozprostĹ™enĂ (spread) zaloĹľenĂ© na hledánĂ minimálnĂ kostry grafu (Minimum Spanning Tree) pro problĂ©my majĂcĂ vĂce neĹľ dvÄ› kritĂ©ria. DoporuÄŤenĂ© hodnoty pro parametry Ĺ™ĂdĂcĂ bÄ›h algoritmu byly urÄŤeny na základÄ› vĂ˝sledkĹŻ jejich citlivostnĂ analĂ˝zy. Algoritmus MOSOMA je dále ĂşspěšnÄ› pouĹľit pro Ĺ™ešenĂ rĹŻznĂ˝ch návrhovĂ˝ch Ăşloh z oblasti elektromagnetismu (návrh Yagi-Uda antĂ©ny a dielektrickĂ˝ch filtrĹŻ, adaptivnĂ Ĺ™ĂzenĂ vyzaĹ™ovanĂ©ho svazku v ÄŤasovĂ© oblasti…).This thesis describes a novel stochastic multi-objective optimization algorithm called MOSOMA (Multi-Objective Self-Organizing Migrating Algorithm). It is shown that MOSOMA is able to solve various types of multi-objective optimization problems (with any number of objectives, unconstrained or constrained problems, with continuous or discrete decision space). The efficiency of MOSOMA is compared with other commonly used optimization techniques on a large suite of test problems. The new procedure based on finding of minimum spanning tree for computing the spread metric for problems with more than two objectives is proposed. Recommended values of parameters controlling the run of MOSOMA are derived according to their sensitivity analysis. The ability of MOSOMA to solve real-life problems from electromagnetics is shown in a few examples (Yagi-Uda and dielectric filters design, adaptive beam forming in time domain…).
Experimental Comparisons of Derivative Free Optimization Algorithms
In this paper, the performances of the quasi-Newton BFGS algorithm, the
NEWUOA derivative free optimizer, the Covariance Matrix Adaptation Evolution
Strategy (CMA-ES), the Differential Evolution (DE) algorithm and Particle Swarm
Optimizers (PSO) are compared experimentally on benchmark functions reflecting
important challenges encountered in real-world optimization problems.
Dependence of the performances in the conditioning of the problem and
rotational invariance of the algorithms are in particular investigated.Comment: 8th International Symposium on Experimental Algorithms, Dortmund :
Germany (2009
DEoptim: An R Package for Global Optimization by Differential Evolution
This article describes the R package DEoptim, which implements the differential evolution algorithm for global optimization of a real-valued function of a real-valued parameter vector. The implementation of differential evolution in DEoptim interfaces with C code for efficiency. The utility of the package is illustrated by case studies in fitting a Parratt model for X-ray reflectometry data and a Markov-switching generalized autoregressive conditional heteroskedasticity model for the returns of the Swiss Market Index.
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