Building on the research in positive psychology, the current study examines the impact of employee savouring on building perceived job performance and tested the extent to which an individual positive affectivity moderated the relationship of savouring and job performance. Using survey responses from 357 salespeople in six Taiwanese insurance companies, the results of hierarchical regression analyses show that savouring is positively related to perceived job performance. In addition, the relationship between savouring and perceived job performance was moderated by employees' positive affectivity. The implications of these results and the limitations of the research are discussed.

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Particle swarm optimization (PSO) has been proposed as an alternative to traditional evolutionary algorithms. Yet, more efficient strategies are still needed to control the trade-off between exploitation and exploration in the search process for solving complex tasks with high dimensional and multimodal objective functions. In this work, the authors propose a new PSO approach to overcome the search difficulties. Their approach first predicts the landscape type of a function for initial search settings, and then focuses on two search strategies for multimodal functions. One is a two-swarm cooperative strategy that controls search region and integrates partial and full dimension PSO search. The other strategy is to control the velocity of the particles in an adaptive way, according to how they move in the space. To evaluate the proposed approach, extensive experiments have been conducted and comparisons to several popular PSO variants have been made. Our experiments prove that the proposed approach can have better performance than others in most of the test cases.

Particle swarm optimization (PSO) has been proposed as an alternative to traditional evolutionary algorithms. In this work, we present a new PSO approach to control the trade-off between exploitation and exploration in the search process for solving multimodal functions. Our approach focuses on two search strategies for multimodal functions. One is a two-swarm cooperative strategy that controls search region and integrates partial and full dimension PSO search. The other strategy is to control the velocity of the particles in an adaptive way. To evaluate the proposed approach, experiments have been conducted and comparisons to several popular PSO variants have been made. The results prove that our approach can have better performance than others in most of the test cases.