A connectionist machine for genetic hillclimbing
A connectionist machine for genetic hillclimbing
Computational intelligence PC tools
Computational intelligence PC tools
Swarm intelligence
Journal of Global Optimization
Recent approaches to global optimization problems through Particle Swarm Optimization
Natural Computing: an international journal
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
The particle swarm optimization algorithm: convergence analysis and parameter selection
Information Processing Letters
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
Cooperative models of particle swarm optimizers
Cooperative models of particle swarm optimizers
Tiny GAs for image processing applications
IEEE Computational Intelligence Magazine
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
A Cooperative approach to particle swarm optimization
IEEE Transactions on Evolutionary Computation
Parallel cooperative micro-particle swarm optimization: A master-slave model
Applied Soft Computing
A simple adaptive algorithm for numerical optimization
MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II
An adaptive single-point algorithm for global numerical optimization
Expert Systems with Applications: An International Journal
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Cooperative approaches have proved to be very useful in evolutionary computation due to their ability to solve efficiently high-dimensional complex problems through the cooperation of low-dimensional subpopulations. On the other hand, Micro-evolutionary approaches employ very small populations of just a few individuals to provide solutions rapidly. However, the small population size renders them prone to search stagnation. This paper introduces Cooperative Micro-Particle Swarm Optimization, which employs cooperative low-dimensional and low-cardinality subswarms to concurrently adapt different subcomponents of high-dimensional optimization problems. The algorithm is applied on high-dimensional instances of five widely used test problems with very promising results. Comparisons with the standard Particle Swarm Optimization algorithm are also reported and discussed.