Extremal optimization

About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.

From statistical physics methods to algorithms

Abstract: After a brief review of the Survey Propagation equations defined over single instances of complex glassy-like Hamiltonians, we discuss their application to the search for minimum energy configurations in difficult combinatorial optimization problems. Furthermore, we show that the application of arbitrary external field is helpful in the investigation of the topology of the configuration space.

Using Simulated Annealing for Solving Travelling Salesman Problem with Pickup and Delivery

Abstract: Travelling Salesman Problem with Pickup and Delivery (TSPD) is an important problem in combinatorial optimization, which maintains close links with vehicle routing problems. Opposed to the classical Travelling Salesman Problem (TSP), research on TSPD is rather scarce, and assumption that pick ups are served after finishing all deliveries, is enforced. In the paper, the assumption is eliminated, and based on developed Metropolis rule, a particular simulated annealing is applied to solve the problem.

Community Structure Detection for the Functional Connectivity Networks of the Brain

Abstract: The community structure detection problem in weighted networks is tackled with a new approach based on game theory and extremal optimization, called Weighted Nash Extremal Optimization. This method approximates the Nash equilibria of a game in which nodes, as players, chose their community by maximizing their payoffs. After performing numerical experiments on synthetic networks, the new method is used to analyze functional connectivity networks of the brain in order to reveal possible connections between different brain regions. Results show that the proposed approach may be used to find biomedically relevant knowledge about brain functionality.

Search tree-based approach for the p-median problem using the ant colony optimization algorithm

Abstract: In this paper we present an approximation algorithm for the p-median problem that uses the principles of ant colony optimization technique. We introduce a search tree that keeps the partial solutions during the solution process of the p-median problem. An adaptation is proposed that allows ant colony optimization algorithm to perform on this tree and obtain good results in short time.