Individual-Based Cooperative Coevolution Local Search for Large Scale Optimization

  • Can LiuEmail author
  • Bin Li
Conference paper
Part of the Proceedings in Adaptation, Learning and Optimization book series (PALO, volume 1)


Decomposition methodology has been well studied and widely applied to Large Scale Global Optimization (LSGO). Cooperative Coevolution (CC) is an effective decomposition strategy and has made remarkable achievements on tackling LSGO problems. In recent studies, the role of Individual-based Local Search (ILS) has arose more and more attention, especially under the framework of Memetic Algorithms (MAs). In this paper, we investigate the validity and performance of incorporating Cooperative Coevolution strategy into Individual-based Local Search. For this purpose, a Solis and Wets’ algorithm with Cooperative Coevolution (SWCC) is presented, and a comparison is made between SWCC and SW via experiments on the LSGO test suite issued in CEC’2013. Then, SWCC is embedded into Simulated Annealing algorithm (SA) and Memetic framework to investigate its effectiveness as local search operator. Experiment results show the effectiveness of SWCC on fully-separable LSGO problems and poor performance on fully non-separable problems.


Cooperative Coevolution Individual-based Local Search Solis and Wets’ algorithm Simulated Annealing algorithm Memetic Algorithms 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Nature Inspire Computation and Applications Lab (NICAL)University of Science and Technology of ChinaHefeiChina
  2. 2.USTC-Birmingham Research Institute of Intelligent Computation and Applications (UBRI)University of Science and Technology of ChinaHefeiChina

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