An approach for initializing the random adaptive grouping algorithm for solving large-scale global optimization problems

International Workshop "Advanced Technologies in Material Science, Mechanical and Automation Engineering – MIP: Engineering – 2019"

材料科学;机械制造;原子能学

Vakhnin, A.^1 ; Sopov, E.^1

Reshetnev Siberian State University of Science and Technology 31, Krasnoyarsky Rabochy Av., Krasnoyarsk

660037, Russia^1

关键词: Cooperative co-evolution;    Golden section search;    Heuristic search algorithms;    High dimensionality;    Hybrid heuristic algorithms;    Large scale global optimizations;    Real-world optimization;    Self-adaptive differential evolutions;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/537/4/042006/pdf
DOI  :  10.1088/1757-899X/537/4/042006

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Many real-world optimization problems deal with high dimensionality and are known as large-scale global optimization (LSGO) problems. LSGO problems, which have many optima and are not separable, can be very challenging for many heuristic search algorithms. In this study, we have proposed a novel two-stage hybrid heuristic algorithm, which incorporates the coordinate descent algorithm with the golden-section search (CDGSS) and the random adaptive grouping for cooperative coevolution of the Self-adaptive Differential Evolution with Neighborhood Search (DECC-RAG) algorithm. At the first stage, the proposed algorithm roughly scans the search space for a better initial population for the DECC-RAG algorithm. At the second stage, the algorithm uses the DECC-RAG framework for solving the given LSGO problem. We have evaluated the proposed approach (DECC-RAG1.1) with 15 most difficult LSGO problems from the IEEE CEC'2013 benchmark set. The experimental results show that DECC-RAG1.1 outperforms the standard DECC-RAG and some the state-of-the-art LSGO algorithms.