Adaptive Parallel Genetic Algorithm adjusts the genetic parameters and operators dynamically during the iterations of
evolution in order to accelerate the convergence and avoid the premature. By using the concept of coarse-grained
parallelization, the population is divided into a few large subpopulations. These subpopulations evolve independently
and concurrently on different processors. After a predefined period of time, some selected individuals are exchanged via
a migration process. In this paper, a parallel multi-population adaptive genetic algorithm is proposed by adjusting the
size of sub-population. The sub-population size is dynamically varied based on the fitness of the best individual of that
sub-population compared with the mean fitness of the total population. The relevant migration strategy including
synchronous and asynchronous migration is also put forward to avoid the work load imbalance in parallel genetic
algorithm. Then, the convergence analysis based on schema theory is given to certify the efficiency of the Sub-
Populations size adjustment in the algorithm.
Fuzzy C-Means clustering is one of the most perfective and widely used algorithms based on objective function for
unsupervised classification. Considering the spatial relationship of pixels when it is used in remote sensing imagery,
Neighbor-based FCM algorithm is put forward with the method of modifying the value of fuzzy membership degrees
with the neighbor information during the clustering iterations. We use dominant class, if it can be determined in a fixed
neighbor region, or the weighted parameters based on the distance of neighbors to perfect the membership degrees of
central pixel. Then parallel implement for the algorithm is also proposed by taking account into the communication
complexity and the spatial relationship for image partition. In the end, the experimental data indicate the efficiency of the
algorithm in decreasing the amount of clustering iterations and increasing the classified precision; the parallel algorithm
also achieves the satisfied linear speedup.
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