Diversity Of Particle Swarm Research Articles

Analysis method of competitive advantage of new industrial innovation alliance based on contraction factor particle swarm optimization (PSO)

To improve effectiveness of competitive advantage analysis algorithm of emerging industry innovation union, a kind of competitive advantage analysis method of emerging industry innovation union based on constriction factor particle swarm optimization (PSO) is proposed. Firstly, competitive advantage evaluation model of emerging industry innovation union is constructed aimed at uncertain influence factor existing in evaluation to strategic emerging industry; secondly, particle swarm optimization is introduced, and to avoid premature convergence problem existing in particle swarm optimization and realize rapid convergence of particle to global optimal solution, constriction factor and two operators, i.e. “attraction” and “diffusion”, are introduced in this paper so that diversity of particle swarm is kept and better convergence rate is possessed. Finally, through empirical analysis to strategic emerging industry evaluation of an area, feasibility and rationality of the method are verified.

Campus Network Management Scheme of Wireless Network User Data Mining

To improve the coverage rate of wireless sensor network node in the campus and prolong network lifetime, an optimization method for network node coverage based on escape particle swarm algorithm has been proposed on the basis of analyzing defects of basic particle swarm optimization algorithm. Firstly, with network coverage rate as optimization objective, escape particle swarm algorithm is adopted for solving and genetic algorithm crossover mechanism is introduced during the solving process to maintain the diversity of particle swarm and prevent “precocity”. In addition, momentum algorithm is adopted to smooth particle searching path, accelerate convergence rate and finally performance of the algorithm is tested through simulation experiment.

The Plan for Distribution Network Maintenance Based on Psobc

With respect to the state maintenance of the distribution network facilities, an optimization model which considered the risk of facilities failure and facilities maintenance costs of distribution network maintenance was given. The grid security constraints , maintenance relational constraints and resource constraints and so on were considered in this model in order to gain the maximum payoff. In the psobc, bacterial attraction and repulsion behavior were introduced into the basic pso. By means of controlling the particle swarm diversity, the global and local search capabilities could be well balanced. The practicality and effect of the model and algorithm were verified through calculation and analysis.

Evolution of heterogeneous ensembles through dynamic particle swarm optimization for video-based face recognition

In many real-world applications, pattern recognition systems are designed a priori using limited and imbalanced data acquired from complex changing environments. Since new reference data often becomes available during operations, performance could be maintained or improved by adapting these systems through supervised incremental learning. To avoid knowledge corruption and sustain a high level of accuracy over time, an adaptive multiclassifier system (AMCS) may integrate information from diverse classifiers that are guided by a population-based evolutionary optimization algorithm. In this paper, an incremental learning strategy based on dynamic particle swarm optimization (DPSO) is proposed to evolve heterogeneous ensembles of classifiers (where each classifier corresponds to a particle) in response to new reference samples. This new strategy is applied to video-based face recognition, using an AMCS that consists of a pool of fuzzy ARTMAP (FAM) neural networks for classification of facial regions, and a niching version of DPSO that optimizes all FAM parameters such that the classification rate is maximized. Given that diversity within a dynamic particle swarm is correlated with diversity within a corresponding pool of base classifiers, DPSO properties are exploited to generate and evolve diversified pools of FAM classifiers, and to efficiently select ensembles among the pools based on accuracy and particle swarm diversity. Performance of the proposed strategy is assessed in terms of classification rate and resource requirements under different incremental learning scenarios, where new reference data is extracted from real-world video streams. Simulation results indicate the DPSO strategy provides an efficient way to evolve ensembles of FAM networks in an AMCS. Maintaining particle diversity in the optimization space yields a level of accuracy that is comparable to AMCS using reference ensemble-based and batch learning techniques, but requires significantly lower computational complexity than assessing diversity among classifiers in the feature or decision spaces.