Corpus ID: 32373309

Competitive Island Cooperative Coevolution for Real Parameter Global Optimization

  title={Competitive Island Cooperative Coevolution for Real Parameter Global Optimization},
  author={K. Bali},
Cooperative Coevolution (CC) is an evolutionary algorithm that features the divide-andconquer paradigm as an efficient technique for solving global optimization problems. A major difficulty associated with CC is the choice of a good decomposition strategy, especially when applied to problems that possess interacting decision variables. Identifying an efficient problem decomposition scheme is vital such that the interacting variables are captured and grouped together into separate subcomponents… Expand


Multi-Island Competitive Cooperative Coevolution for Real Parameter Global Optimization
Results from the experimental analysis show that competition and collaboration of several different island can yield solutions with a quality better than the two-island competition algorithm (CICC) on most complex multi-modal problems. Expand
Competitive two-island cooperative coevolution for real parameter global optimisation
A method is utilized that enforces competition in coevolution whereby different problem decomposition schemes are implemented as islands that compete and collaborate with each other and achieves promising results. Expand
Scaling up Multi-island Competitive Cooperative Coevolution for Real Parameter Global Optimisation
An analysis of the MICCC algorithm and also extends it to more than five islands that incorporate arbitrary (non-uniform) problem decomposition strategies as additional islands in MICCC and monitors how each different problem decompose strategy contributes towards the global fitness over different stages of optimisation. Expand
A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization
This paper proposes a new coevolutionary paradigm that hybridizes competitive and cooperative mechanisms observed in nature to solve multiobjective optimization problems and to track the Pareto front in a dynamic environment. Expand
A cooperative coevolutionary algorithm with Correlation based Adaptive Variable Partitioning
  • T. Ray, X. Yao
  • Mathematics, Computer Science
  • 2009 IEEE Congress on Evolutionary Computation
  • 2009
Insight is provided into why CCEA in its basic form is not suitable for nonseparable problems and a Cooperative Coevolutionary Algorithm with Correlation based Adaptive Variable Partitioning (CCEA-AVP) is introduced to deal with such problems. Expand
Competitive Island-Based Cooperative Coevolution for Efficient Optimization of Large-Scale Fully-Separable Continuous Functions
The experimental results in this paper suggest that competition and collaboration of suboptimal decomposition strategies of a fully-separable problem can generate better solutions than the standard cooperative coevolution with standalone decomposition Strategies. Expand
Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization
An automatic decomposition strategy called differential grouping is proposed that can uncover the underlying interaction structure of the decision variables and form subcomponents such that the interdependence between them is kept to a minimum and greatly improve the solution quality on large-scale global optimization problems. Expand
Large scale evolutionary optimization using cooperative coevolution
A new cooperative coevolution framework that is capable of optimizing large scale nonseparable problems with a random grouping scheme and adaptive weighting and a novel differential evolution algorithm is adopted. Expand
Multilevel cooperative coevolution for large scale optimization
  • Zhenyu Yang, K. Tang, X. Yao
  • Computer Science
  • 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)
  • 2008
The motivation is to improve the previous work on grouping based cooperative coevolution (EACC-G), which has a hard-to-determine parameter, group size, in tackling problem decomposition. Expand
Cooperative Co-evolution with delta grouping for large scale non-separable function optimization
Delta method measures the averaged difference in a certain variable across the entire population and uses it for identifying interacting variables and shows that this new technique is more effective than the existing random grouping method. Expand