Coping with strangers: how familiarity and active interactions shape group coordination in Corydoras aeneus

Social groups composed of familiar individuals exhibit better coordination than unfamiliar groups; however, the ways familiarity contributes to coordination are poorly understood. Prior social experience probably allows individuals to learn the tendencies of familiar group-mates and respond accordingly. Without prior experience, individuals would benefit from strategies for enhancing coordination with unfamiliar others. We used a social catfish, Corydoras aeneus , that uses discrete, observable tactile interactions to assess whether active interactions could facilitate coordination, and how their role might be mediated by familiarity. We describe this previously understudied physical interaction, ‘nudges’, and show it to be associated with group coordination and cohesion. Furthermore, we investigated nudging and coordination in familiar/unfamiliar pairs. In all pairs, we found that nudging rates were higher during coordinated movements than when fish were together but not coordinating. We observed no familiarity-based difference in coordination or cohesion. Instead, unfamiliar pairs exhibited significantly higher nudging rates, suggesting that unfamiliar pairs may be able to compensate for unfamiliarity through increased nudging. By contrast, familiar individuals coordinated with comparatively little nudging. Second, we analysed nudging and cohesion within triplets of two familiar and one unfamiliar individual (where familiar individuals had a choice of partner). Although all individuals nudged at similar rates, the unfamiliar group-mate was less cohesive than its familiar group-mates and spent more time alone. Unfamiliar individuals that nudged their group-mates more frequently exhibited higher cohesion, indicating that nudging may facilitate cohesion for the unfamiliar group-mate. Overall, our results suggest that nudges can mitigate unfamiliarity, but that their usage is reduced in the case of familiar individuals, implying a cost is associated with the behaviour.

Meleagris Gallopavo Algorithm for Solving Optimal Reactive Power Problem

In this paper, Meleagris Gallopavo Algorithm (MGA) is proposed for solving optimal reactive power problem.As a group-mate Meleagris gallopavo follow their poultry to explore food, at the same time it prevent the same ones to eat their own food. Always the overriding individuals have the lead to grab more food and Meleagris gallopavo would arbitrarily pinch the high-quality food which has been already found by other Meleagris gallopavo. In the region of the mother Meleagris gallopavo, Poults always search for food. In the Projected Meleagris Gallopavo Algorithm (MGA) additional parameters are eliminated, in order to upsurge the search towards global optimization solution.Proposed Meleagris Gallopavo Algorithm (MGA) has been tested on two modes a. with the voltage stability Evaluation in standard IEEE 30 bus test system, b. Without voltage stability Evaluation in standard IEEE 30, 57,118 bus test systems & practical 191 test system. Simulation results show clearly the better performance of the proposed Meleagris Gallopavo Algorithm (MGA) in reducing the real power loss, enhancement of static voltage stability Index and particularly voltage profiles within the specified limits.