Self-adaptive differential evolution applied to combustion engine calibration

作     者：Fachin, Jose Marcio Reynoso-Meza, Gilberto Mariani, Viviana Cocco Coelho, Leandro dos Santos 

作者机构：Univ Fed Parana Dept Elect Engn Elect Engn Grad Program PPGEE Curitiba Parana Brazil Pontificia Univ Catolica Parana Ind & Syst Engn Grad Program PPGEPS Curitiba Parana Brazil Pontificia Univ Catolica Parana Dept Mech Engn Curitiba Parana Brazil 

出 版 物：《SOFT COMPUTING》 (Soft Comput.)

年 卷 期：2021年第25卷第1期

页      面：109-135页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：National Council of Scientific and Technologic Development of Brazil-CNPq [307958/2019-1-PQ, 307966/2019-4-PQ, 405101/2016-3-Univ, 404659/2016-0-Univ] PRONEX Fundacao Araucaria [042/2018] 

主　　题：Optimization algorithm Differential evolution algorithm Hybrid approach Unconstrained optimization 

摘      要：In this paper, a new population-based stochastic optimization algorithm called Hybrid Self-Adaptive Differential Evolution (HSADE) is proposed. The algorithm addresses unconstrained global optimization problems, exploring and combining the best features of some Differential Evolution (DE), obtaining a good balance between exploration and exploitation. These approaches are important for increasing the accuracy and efficiency of a population-based stochastic algorithm and for adapting the control parameter values during the optimization process. Further, they are crucial for increasing the convergence speed and reducing the risk of search stagnation. To verify the performance of the HSADE, 25 benchmark functions were tested presenting an optimal performance when compared to some state-of-the-art DEs algorithms. Furthermore, an experimental problem in an automotive sector, related to automatic internal combustion engine calibration, was used adjusting 300 decision variables. The HSADE achieved a reliable calibration, reducing the time required to perform approximately 90% when comparing to other optimization algorithms.