文献类型：期刊文献

英文题名：A Hybrid Cultural Harmony Search Algorithm for Constrained Optimization Problem of Diesel Blending

作者：Gao, Min[1];Zhu, Yanfei[2];Cao, Cuiwen[1];Zhu, Yanfeng[3]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai 200237, Peoples R China;[2]Shanghai Normal Univ, Coll Informat Mech & Elect Engn, Shanghai 200234, Peoples R China;[3]Tianjin Univ Sci & Technol, Coll Elect Informat & Automat, Tianjin 300457, Peoples R China

年份：2020

卷号：8

起止页码：6673

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20200408078679);WOS:【SCI-EXPANDED(收录号:WOS:000524687200004)】；

基金：This work was supported in part by the Natural Science Foundation of Shanghai under Grant 18ZR1428000, and in part by the National Natural Science Foundation of China under Grant 61673175 and Grant 61573144.

语种：英文

外文关键词：Nonlinear diesel blending; variable domain reduction; simplex improved cultural harmony search algorithm; constrained optimization

摘要：This paper studies the constrained optimization problem for nonlinear diesel blending. A new hybrid algorithm called cultural harmony search algorithm is presented to solve the proposed optimization problem, which uses cultural knowledge in the belief space of the cultural algorithm to guide the evolving and searching process of the harmony search algorithm. Then, an improved harmony improvisation in the population space of cultural algorithm is developed for new harmony generation to enrich the population diversity. Moreover, in order to accelerate convergence, the domain of decision variables is scaled down by a simplex method at the beginning of the algorithm, and a simplex improved cultural harmony search algorithm is provided. Finally, benchmark functions and the results of application in nonlinear diesel blending of a real-world refinery show the feasibility and effectiveness of the proposed algorithms. The contrasted experiments show that our proposed hybrid algorithm is better than other hybrid algorithms, especially in diesel blending optimization problem.