Honey-bee mating optimization (HBMO) algorithm for optimal reservoir operation

In recent years, evolutionary and meta-heuristic algorithms have been extensively used as search and optimization tools in various problem domains, including science, commerce, and engineering. Ease of use, broad applicability, and global perspective may be considered as the primary reason for their success. The honey-bee mating process has been considered as a typical swarm-based approach to optimization, in which the search algorithm is inspired by the process of real honey-bee mating. In this paper, the honey-bee mating optimization (HBMO) algorithm is presented and tested with a nonlinear, continuous constrained problem with continuous decision and state variables to demonstrate the efficiency of the algorithm in handling the single reservoir operation optimization problems. It is shown that the performance of the model is quite comparable with the results of the well-developed traditional linear programming (LP) solvers such as LINGO 8.0. Results obtained are quite promising and compare well with the final results of the other approach.     

Not an outgroup, not yet an ingroup: Immigrants in the Stereotype Content Model

Stereotype research depicts the generic immigrant as incompetent and untrustworthy. The current research expands this image, specifying key information dimensions (e.g. nationality, socioeconomic status) about immigrants. To see how perceivers differentiate among particular immigrant groups, we extend a model of intergroup perception, the Stereotype Content Model (SCM; Fiske, Cuddy, Glick, & Xu, 2002. Journal of Personality and Social Psychology, 82, 878–902), to immigrant subgroups. The SCM predicts that perception centers on competence and warmth, and relates to targets’ perceived status and competition within society. Specified by nationality, race, ethnicity, and class, images of immigrants differ by both competence and warmth, with most groups receiving ambivalent (low–high or high–low) stereotypes rather than the uniform low–low for the generic immigrant. As predicted, ambivalent stereotypes reflect target nationality combined with socioeconomic status, supporting the SCM's ambivalent stereotypes and social structural hypotheses, as well as better defining immigrant stereotypes and their contingencies.