Emergence of an Adaptive Movement from a Subjective Inference about Resource Distribution

Background: In animal foraging, the optimal search strategy in an unknown environment varies according to the context. When food is distributed sparsely and randomly, super-diffusive walks outperform normal-diffusive walks. However, super-diffusive walks are no longer advantageous when random walkers forage in a resource-rich environment. It is not currently clear whether a relationship exists between an agent’s use of local information to make subjective inferences about global food distribution and an optimal random walk strategy. Methods: Therefore, I investigated how flexible exploration is achieved if an agent alters its directional rule based on local resource distribution. In the proposed model, the agent, a Brownian-like walker, estimates global resource distribution using local resource patterns and makes a decision by altering its rules. Results: I showed that the agent behaved like a non-Brownian walker and the model adaptively switched between diffusive properties depending on the resource density. This led to a more effective resource-searching performance compared with that of a simple random-walk model. Conclusion: These results demonstrate a process of optimal searching dependent on context.

Optimal Randomness in Swarm-Based Search

Lévy flights is a random walk where the step-lengths have a probability distribution that is heavy-tailed. It has been shown that Lévy flights can maximize the efficiency of resource searching in uncertain environments and also the movements of many foragers and wandering animals have been shown to follow a Lévy distribution. The reason mainly comes from the fact that the Lévy distribution has an infinite second moment and hence is more likely to generate an offspring that is farther away from its parent. However, the investigation into the efficiency of other different heavy-tailed probability distributions in swarm-based searches is still insufficient up to now. For swarm-based search algorithms, randomness plays a significant role in both exploration and exploitation or diversification and intensification. Therefore, it is necessary to discuss the optimal randomness in swarm-based search algorithms. In this study, cuckoo search (CS) is taken as a representative method of swarm-based optimization algorithms, and the results can be generalized to other swarm-based search algorithms. In this paper, four different types of commonly used heavy-tailed distributions, including Mittag-Leffler distribution, Pareto distribution, Cauchy distribution, and Weibull distribution, are considered to enhance the searching ability of CS. Then four novel CS algorithms are proposed and experiments are carried out on 20 benchmark functions to compare their searching performances. Finally, the proposed methods are used to system identification to demonstrate the effectiveness.

A Survey over Mechanisms for Reducing Free Riding Behaviour in Structured P2P Networks

P2P (peer-to-peer) overlays have attracted many researchers due to increase in Internet based P2P applications. A P2P overlay is a distributed system in which the independent nodes participate at their will to share resources in distributed manner. P2P overlays are designed for wired based communication systems but today P2P applications are becoming popular in wireless networks even in multi-hop wireless networks. Routing mechanism in P2P overlays is based on IP infrastructure and many protocols are deployed successfully for efficient and fair P2P applications. However, there are many challenges in designing and deployment of efficient and fair protocols for the P2P overlays. These overlays suffer from many challenges such as dynamic overlay management, lack of robust trust model, counterfeit content distribution, free riding, poor resource search scalability, security, etc. The churn rate of nodes (join and leave of nodes) in the P2P overlay makes overlay management and resource searching more challenging. The free riding nature of the nodes in a P2P overlay is undesirable and it creates unfairness in the P2P overlay. There are several mechanisms proposed such as Eigen Trust model, tit-for-tat policy, point-based incentive policy, Page Rank policy, layered taxation, advertisement of incentive, etc. for encouraging fairness in the P2P overlays. I have presented in-depth survey over free riding behaviour, its effect, and existing mechanisms to reduce free riding in structured P2P overlays.