8 research outputs found
srMO-BO-3GP: A sequential regularized multi-objective constrained Bayesian optimization for design applications
Bayesian optimization (BO) is an efficient and flexible global optimization
framework that is applicable to a very wide range of engineering applications.
To leverage the capability of the classical BO, many extensions, including
multi-objective, multi-fidelity, parallelization, latent-variable model, have
been proposed to improve the limitation of the classical BO framework. In this
work, we propose a novel multi-objective (MO) extension, called srMO-BO-3GP, to
solve the MO optimization problems in a sequential setting. Three different
Gaussian processes (GPs) are stacked together, where each of the GP is assigned
with a different task: the first GP is used to approximate the single-objective
function, the second GP is used to learn the unknown constraints, and the third
GP is used to learn the uncertain Pareto frontier. At each iteration, a MO
augmented Tchebycheff function converting MO to single-objective is adopted and
extended with a regularized ridge term, where the regularization is introduced
to smoothen the single-objective function. Finally, we couple the third GP
along with the classical BO framework to promote the richness and diversity of
the Pareto frontier by the exploitation and exploration acquisition function.
The proposed framework is demonstrated using several numerical benchmark
functions, as well as a thermomechanical finite element model for flip-chip
package design optimization
Dominance-Based Multiobjective Simulated Annealing
Copyright © 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Simulated annealing is a provably convergent optimizer for single-objective problems. Previously proposed multiobjective extensions have mostly taken the form of a single-objective simulated annealer optimizing a composite function of the objectives. We propose a multiobjective simulated annealer utilizing the relative dominance of a solution as the system energy for optimization, eliminating problems associated with composite objective functions. We also propose a method for choosing perturbation scalings promoting search both towards and across the Pareto front. We illustrate the simulated annealer's performance on a suite of standard test problems and provide comparisons with another multiobjective simulated annealer and the NSGA-II genetic algorithm. The new simulated annealer is shown to promote rapid convergence to the true Pareto front with a good coverage of solutions across it comparing favorably with the other algorithms. An application of the simulated annealer to an industrial problem, the optimization of a code-division-multiple access (CDMA) mobile telecommunications network's air interface, is presented and the simulated annealer is shown to generate nondominated solutions with an even and dense coverage that outperforms single objective genetic algorithm optimizers