Premium
A probability distribution estimation based method for dynamic optimization
Author(s) -
Xiao Jie,
Huang Yinlun,
Lou Helen H.
Publication year - 2007
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.11209
Subject(s) - mathematical optimization , optimization problem , metaheuristic , computer science , continuous optimization , nonlinear system , heuristic , population , ant colony optimization algorithms , probability distribution , process (computing) , engineering optimization , multi swarm optimization , mathematics , statistics , physics , demography , quantum mechanics , sociology , operating system
Engineering optimization of a highly nonlinear complex system is always a challenge methodologically and computationally. This is especially true when multistage dynamic optimization is involved. While significant progress has been made in rigorous deterministic algorithms for dynamic optimization, meta‐heuristic‐based optimization may offer an attractive alternative. This paper introduces a general mathematical framework, called the Population‐based Probability Distribution Estimation (PPDE) method, for tackling constrained multistage complex process dynamic optimization problems. Solution identification is accomplished through probability distribution estimation based search in a continuous space, where special solution migration and penalty assignment techniques are integrated. Besides an optimal parameter estimation problem for a reactor system, an automotive coating curing optimization problem is also investigated, where the PPDE successfully minimizes oven energy consumption under various process/product constraints. Optimization results demonstrate superiorities of the method over the Ant Colony System (ACS) based dynamic optimization method. © 2007 American Institute of Chemical Engineers AIChE J, 2007