info:eu-repo/semantics/article
Parallel hyper-heuristics for process engineering optimization
Fecha
2021-10Registro en:
Oteiza, Paola Patricia; Ardenghi, Juan Ignacio; Brignole, Nélida Beatriz; Parallel hyper-heuristics for process engineering optimization; Pergamon-Elsevier Science Ltd; Computers and Chemical Engineering; 153; 1074; 10-2021; 1-13
0098-1354
CONICET Digital
CONICET
Autor
Oteiza, Paola Patricia
Ardenghi, Juan Ignacio
Brignole, Nélida Beatriz
Resumen
This paper presents the general framework of a parallel cooperative hyper-heuristic optimizer (PCHO) to solve systems of nonlinear algebraic equations with equality and inequality constraints. The algorithm comprises the classical metaheuristics called Genetic Algorithms, Simulated Annealing and Particle Swarm Optimization, whose parameters are adaptively chosen during the executions. A Master-Worker architecture was designed and implemented, where the Master processor ranks the solution candidates informed by the metaheuristics and immediately communicates the most promising candidate to update all Workers. Algorithmic performance was tested with general models, most of them corresponding to PSE process systems. The results confirmed the efficiency of the proposed approach since both online parameter retuning and parallel processing sped up the search.