Paper Info
Title
Joint operations algorithm for large-scale global optimization.
Abstract
Large-scale global optimization (LSGO) is a very important but thorny task in optimization domain, which widely exists in management and engineering problems. In order to strengthen the effectiveness of meta-heuristic algorithms when handling LSGO problems, we propose a novel meta-heuristic algorithm, which is inspired by the joint operations strategy of multiple military units and called joint operations algorithm (JOA). The overall framework of the proposed algorithm involves three main operations: offensive, defensive and regroup operations. In JOA, offensive operations and defensive operations are used to balance the exploration ability and exploitation ability, and regroup operations is applied to alleviate the problem of premature convergence. To evaluate the performance of the proposed algorithm, we compare JOA with six excellent meta-heuristic algorithms on twenty LSGO benchmark functions of IEEE CEC 2010 special session and four real-life problems. The experimental results show that JOA performs steadily, and it has the best overall performance among the seven compared algorithms. (C) 2015 Elsevier B.V. All rights reserved.
Year
DOI
Venue
2016
10.1016/j.asoc.2015.10.047
Applied Soft Computing
Keywords
DocType
Volume
Joint operations algorithm,Meta-heuristic algorithms,Evolutionary algorithms,Swarm-based algorithms,Large-scale global optimization
Journal
38
ISSN
Citations 
PageRank 
1568-4946
10
0.46
References 
Authors
44
3
Name
Order
Citations
PageRank
Gaoji Sun1100.80
Ruiqing Zhao2100.46
Yanfei Lan321815.92