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Abstract: In recent years, many different variants of the particle swarm optimizer (PSO) for solving optimization problems have been proposed. However, PSO has an inherent drawback in handling constrained problems, mainly because of its complexity and dependency on parameters. Furthermore, one PSO variant may perform well for some test problems but not obtain good results for others. In this paper, our purpose is to develop a new PSO algorithm that can efficiently solve a variety of constrained optimization problems. It considers a mix of different PSO variants each of which evolves with a different number of individuals from the current population. In each generation, the algorithm assigns more individuals to the better-performing variants and fewer to the worse-performing ones. Also, a new PSO variant is developed for use in the proposed algorithm to maintain a better balance between its local and global PSO versions. A new methodology for adapting PSO parameters is presented and the proposed self-adaptive PSO algorithm tested and analyzed on two sets of test problems, namely the CEC2006 and CEC2010 constrained optimization problems. Based on the results, the proposed algorithm shows significantly better performance than the same global and local PSO variants as well as other-state-of-the-art algorithms. Although, based on our analysis, it cannot guarantee an optimal solution for any unknown problem, it is expected to be able to solve a wide variety of practical problems. [Copyright &y& Elsevier]

Highlights: [•] This paper presents a new multiobjective swarm hybrid learning algorithm, MPSON. [•] We also propose MGAN to comparison with MPSON based on performance measures. [•] The aim of our algorithms is to obtain simple and accurate RBF networks simultaneously. [•] We validate the algorithms on datasets with different features, samples and classes. [•] Results show MPSON is the best algorithm with the accuracy-complexity balance for RBF network design. [Copyright &y& Elsevier]

Abstract: Both fast and accurate block-matching algorithms are critical to efficient compression of video frames using motion estimation and compensation. While the particle swarm optimization approach holds the promise of alleviating the local optima problem suffered typically by existing very fast block matching methods, motion estimation algorithms based on particle swarm optimization in the literature appear to be either much slower than some leading fast block-matching methods for a given accuracy of motion estimation, or less accurate for a given computational complexity. In this paper, we show that the conventional particle swarm optimization approach, which was originally designed to solve general optimization problems where fast convergence of the algorithm might not be a primary concern, could be modified appropriately so that it could provide accurate motion estimation with very low computational cost in the specific context of video motion estimation. To this end, we proposed a new block matching algorithm based on a set of strategies adapted from the standard particle swarm optimization approach. Extensive simulations showed that the proposed method could achieve significant improvements over leading fast block matching methods including the diamond search and the cross-diamond search methods, in terms of both estimation accuracy and computational cost. In particular, the proposed method based on particle swarm optimization is not only much faster, but also remarkably more accurate (about 2dB higher in terms of the Peak Signal-to-Noise-Ratio) than the competing methods on video sequences with large motion. [Copyright &y& Elsevier]

Abstract: Multimodal optimization is still one of the most challenging tasks for evolutionary computation. In recent years, many evolutionary multi-modal optimization algorithms have been developed. All these algorithms must tackle two issues in order to successfully solve a multi-modal problem: how to identify multiple global/local optima and how to maintain the identified optima till the end of the search. For most of the multi-modal optimization algorithms, the fine-local search capabilities are not effective. If the required accuracy is high, these algorithms fail to find the desired optima even after converging near them. To overcome this problem, this paper integrates a novel local search technique with some existing PSO based multimodal optimization algorithms to enhance their local search ability. The algorithms are tested on 14 commonly used multi-modal optimization problems and the experimental results suggest that the proposed technique not only increases the probability of finding both global and local optima but also reduces the average number of function evaluations. [Copyright &y& Elsevier]