The particle swarm optimization (PSO) technique is a powerful stochastic evolutionary algorithm that can be used to find the global optimum solution in a complex search space. However, it has been observed that there is a great variation in its performance due to the dimensionality of the problem and the location of the global optimum with respect to the boundaries of the search space. The present paper attempts to resolve this problem by proposing a simple hybrid "damping" boundary condition that combines the characteristics offered by the existing "absorbing" and "reflecting" boundaries. Simulation results on microwave image reconstruction have shown that with the proposed "damping" boundary condition, a much robust and consistent optimization performance can be obtained for PSO regardless of the dimensionality and location of the global optimum solution.

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Particle swarm optimization algorithm (PSOA), which maintains a population of particles, where each particle represents a potential solution to an optimization problem, is a population-based stochastic search process. This study intends to integrate PSOA with K-means to cluster data. It is shown that PSOA can be employed to find the centroids of a user-specified number of clusters. The proposed PSOA is evaluated using four data sets, and compared to the performance of some other PSOA-based methods and K-means method. Computational results show that the proposed method has much potential. A real-world problem for order clustering also illustrates that the proposed method is quite promising.