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Cooperative Co-Evolution With Differential Grouping for Large Scale Optimization
TLDR
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.
Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale
TLDR
A suite of benchmark functions for large-scale numerical optimization of metaheuristic optimization algorithms and a systematic evaluation platform is provided for comparing the scalability of different EAs.
Cooperatively Coevolving Particle Swarms for Large Scale Optimization
  • Xiaodong Li, X. Yao
  • Mathematics, Computer Science
    IEEE Transactions on Evolutionary Computation
  • 1 April 2012
TLDR
The experimental results and analysis suggest that CCPSO2 is a highly competitive optimization algorithm for solving large-scale and complex multimodal optimization problems.
A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization
TLDR
This paper introduces a modified PSO, Non-dominated Sorting Particle Swarm Optimizer (NSPSO), for better multiobjective optimization by making a better use of particles' personal bests and offspring for more effective nondomination comparisons.
Niching Without Niching Parameters: Particle Swarm Optimization Using a Ring Topology
  • Xiaodong Li
  • Mathematics, Computer Science
    IEEE Transactions on Evolutionary Computation
  • 1 February 2010
TLDR
Experimental results suggest that PSO algorithms using the ring topology are able to provide superior and more consistent performance over some existing PSO niching algorithms that require nICHing parameters.
Locating and tracking multiple dynamic optima by a particle swarm model using speciation
This paper proposes an improved particle swarm optimizer using the notion of species to determine its neighborhood best values for solving multimodal optimization problems and for tracking multiple
Benchmark Functions for CEC'2013 Special Session and Competition on Niching Methods for Multimodal Function Optimization'
TLDR
It is believed it is now time to adopt a unifying framework for evaluating niching methods, so that further advance in this area can be made with ease.
Efficient differential evolution using speciation for multimodal function optimization
  • Xiaodong Li
  • Mathematics, Computer Science
    GECCO '05
  • 25 June 2005
In this paper differential evolution is extended by using the notion of speciation for solving multimodal optimization problems. The proposed species-based DE (SDE) is able to locate multiple global
Adaptively Choosing Neighbourhood Bests Using Species in a Particle Swarm Optimizer for Multimodal Function Optimization
This paper proposes an improved particle swarm optimizer using the notion of species to determine its neighbourhood best values, for solving multimodal optimization problems. In the proposed species-
Cooperative Co-evolution with delta grouping for large scale non-separable function optimization
TLDR
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.
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