Publicação

Parallel strategies for Direct Multisearch

Ver documento

Detalhes bibliográficos
Resumo:Direct multisearch (DMS) is a derivative-free optimization class of algorithms, suited for computing approximations to the complete Pareto front of a given multiobjective optimization problem. In DMS class, constraints are addressed with an extreme barrier approach, only evaluating feasible points. It has a well-supported convergence analysis and simple implementations present a good numerical performance, both in academic test sets and in real applications. Recently, this numerical performance was improved with the definition of a search step based on the minimization of quadratic polynomial models, corresponding to the algorithm BoostDMS. In this work, we propose and numerically evaluate strategies to improve the performance of BoostDMS, mainly through parallelization applied to the search and to the poll steps. The final parallelized version not only considerably decreases the computational time required for solving a multiobjective optimization problem, but also increases the quality of the computed approximation to the Pareto front. Extensive numerical results will be reported in an academic test set and in a chemical engineering application.
Autores principais:Tavares, S.
Outros Autores:Brás, C. P.; Custódio, A. L.; Duarte, V.; Medeiros, P.
Assunto:Derivative-free optimization Direct search methods Multiobjective optimization Parallel algorithms Applied Mathematics
Ano:2023
País:Portugal
Tipo de documento:artigo
Tipo de acesso:acesso aberto
Instituição associada:Universidade Nova de Lisboa
Idioma:inglês
Origem:Repositório Institucional da UNL

Registos relacionados

Registos relacionados