Peer-to-peer Optimization in Large Unreliable Networks with Branch-and-Bound and Particle Swarms

Abstract. Recent developments in the area of peer-to-peer (P2P) computing have enabled a new generation of fully-distributed global optimization algorithms via providing self-organizing control and load balancing mechanisms in very large scale, unreliable networks. Such decentralized networks (lacking a GRID-style resource management and scheduling infrastructure) are an increasingly important platform to exploit. So far, little is known about the scaling and reliability of optimization algorithms in P2P environments. In this paper we present empirical results comparing two algorithms for real-valued search spaces in large-scale and unreliable networks. Some interesting, and perhaps counter-intuitive findings are presented: for example, failures in the network can in fact significantly improve performance under some conditions. The two algorithms that are compared are a known distributed particle swarm optimization (PSO) algorithm and a novel P2P branch-and-bound (B&B) algorithm based on interval arithmetic. Although our B&B algorithm is not a black-box heuristic, the PSO algorithm is competitive in certain cases, in particular, in larger networks. Comparing two rather different paradigms for solving the same problem gives a better characterization of the limits and possibilities of optimization in P2P networks

Grassroots Tradition : A Comparison of the Gay People's Union and the City Club of Milwaukee

Color poster with text and images.This study compares two Milwaukee, Wisconsin-based groups seeking reform during vastly different eras--the City Club of Milwaukee in the 1910s and the Gay People's Union in the 1970s.University of Wisconsin--Eau Claire Office of Research and Sponsored Program

P2P Evolutionary Algorithms: A Suitable Approach for Tackling Large Instances in Hard Optimization Problems

Abstract. In this paper we present a distributed Evolutionary Algorithm (EA) whose population is structured using newscast, a gossiping protocol. This algorithm has been designed to deal with computationally expensive problems via massive scalability; therefore, we analyse the response time of the model using large instances of well-known hard optimization problems that require from EAs a (sometimes exponentially) bigger computational effort as these problems scale. Our approach has been matched against a sequential Genetic Algorithm (sGA) applied to the same set of problems, and we found that it needs less computational effort than the sGA in yielding success. Furthermore, the response time scale logarithmically with respect to the problem size, which makes it suitable to tackle large instances.