Showing papers by "Christian Blum published in 2007"

An ant colony optimization algorithm for continuous optimization: application to feed-forward neural network training

Abstract: Ant colony optimization (ACO) is an optimization technique that was inspired by the foraging behaviour of real ant colonies. Originally, the method was introduced for the application to discrete optimization problems. Recently we proposed a first ACO variant for continuous optimization. In this work we choose the training of feed-forward neural networks for pattern classification as a test case for this algorithm. In addition, we propose hybrid algorithm variants that incorporate short runs of classical gradient techniques such as backpropagation. For evaluating our algorithms we apply them to classification problems from the medical field, and compare the results to some basic algorithms from the literature. The results show, first, that the best of our algorithms are comparable to gradient-based algorithms for neural network training, and second, that our algorithms compare favorably with a basic genetic algorithm.

ACO vs EAs for solving a real-world frequency assignment problem in GSM networks

Abstract: Frequency planning is a very important task for current GSM operators. In this work we present a new mathematical formulation of the problem in which the frequency plans are evaluated by using accurate interference information coming from a real GSM network. We have developed an ant colony optimization (ACO) algorithm to tackle this problem. After accurately tuning this algorithm, it has been compared against a (1,10) Evolutionary Algorithm (EA). The results show that the ACO clearly outperforms the EA when using different time limits as stopping condition for a rather extensive comparison.

Ant Colony Optimization: Introduction and Hybridizations

Abstract: This paper contains complimentary material to the tutorial "ant colony optimization: introduction and hybridizations" given by the author at HIS 2007, Kaiserslautern, Germany. First, ant colony optimization is shortly introduced. Then, successful recent hybridizations of ant colony optimization algorithms with other techniques for optimization are reviewed.

Probabilistic beam search for the longest common subsequence problem

Abstract: Finding the common part of a set of strings has many important applications, for example, in pattern recognition or computational biology. In computer science, this problem is known as the longest common subsequence problem. In this work we present a probabilistic beam search approach to solve this classical problem. To our knowledge, this algorithm is the first stochastic local search algorithm proposed for this problem. The results show the great potential of our algorithm when compared to existing heuristic methods.

Finding Edge-disjoint Paths in Networks: An Ant Colony Optimization Algorithm

Abstract: One of the basic operations in communication networks consists in establishing routes for connection requests between physically separated network nodes. In many situations, either due to technical constraints or to quality-of-service and survivability requirements, it is required that no two routes interfere with each other. These requirements apply in particular to routing and admission control in large-scale, high-speed and optical networks. The same requirements also arise in a multitude of other applications such as real-time communications, vlsi design, scheduling, bin packing, and load balancing. This problem can be modeled as a combinatorial optimization problem as follows. Given a graph G representing a network topology, and a collection T={(s 1,t 1)...(s k ,t k )} of pairs of vertices in G representing connection request, the maximum edge-disjoint paths problem is an NP-hard problem that consists in determining the maximum number of pairs in T that can be routed in G by mutually edge-disjoint s i −t i paths. We propose an ant colony optimization (aco) algorithm to solve this problem. aco algorithms are approximate algorithms that are inspired by the foraging behavior of real ants. The decentralized nature of these algorithms makes them suitable for the application to problems arising in large-scale environments. First, we propose a basic version of our algorithm in order to outline its main features. In a subsequent step we propose several extensions of the basic algorithm and we conduct an extensive parameter tuning in order to show the usefulness of those extensions. In comparison to a multi-start greedy approach, our algorithm generates in general solutions of higher quality in a shorter amount of time. In particular the run-time behaviour of our algorithm is one of its important advantages.

A probabilistic beam search approach to the shortest common supersequence problem

Abstract: The Shortest Common Supersequence Problem (SCSP) is a well-known hard combinatorial optimization problem that formalizes many real world problems. This paper presents a novel randomized search strategy, called probabilistic beam search (PBS), based on the hybridization between beam search and greedy constructive heuristics. PBS is competitive (and sometimes better than) previous state-of-the-art algorithms for solving the SCSP. The paper describes PBS and provides an experimental analysis (including comparisons with previous approaches) that demonstrate its usefulness.

Using branch & bound concepts in construction-based metaheuristics: exploiting the dual problem knowledge

Abstract: In recent years it has been shown by means of practical applications that the incorporation of branch & bound concepts within construction-based metaheuristics can be very useful. In this paper, we attempt to give an explanation of why this type of hybridization works. First, we introduce the concepts of primal and dual problem knowledge, and we show that metaheuristics only exploit the primal problem knowledge. In contrast, hybrid metaheuristic that include branch & bound concepts exploit both the primal and the dual problem knowledge. After giving a survey of these techniques, we conclude the paper with an application example that concerns the longest common subsequence problem.

Iterated local search and constructive heuristics for error correcting code design

Abstract: Error Correcting Codes (ECCs) play an important role, for example, in the transmission of messages over telecommunication networks or in reading information from digital data media such as DVDs or CDs. The design of ECCs is computationally a hard problem. Due to its hardness, several metaheuristic approaches for its solution have been proposed in the literature. In this paper, we present different algorithms based on solution construction and iterated local search. The experimental evaluation shows that a simple multistart constructive heuristic is often between two and three orders of magnitude faster than current state-of-the-art metaheuristics when applied to rather small problem instances. When bigger problem instances are concerned, the proposed iterated local search algorithm has advantages over both the multistart constructive heuristic and state-of-the-art metaheuristics.

Hybrid metaheuristics : 4th International Workshop, HM 2007, Dortmund, Germany, October 8-9, 2007 : proceedings

Abstract: Evolutionary Local Search for the Super-Peer Selection Problem and the p-Hub Median Problem.- An Effective Memetic Algorithm with Population Management for the Split Delivery Vehicle Routing Problem.- Empirical Analysis of Two Different Metaheuristics for Real-World Vehicle Routing Problems.- Guiding ACO by Problem Relaxation: A Case Study on the Symmetric TSP.- Hybrid Local Search Techniques for the Resource-Constrained Project Scheduling Problem.- Evolutionary Clustering Search for Flowtime Minimization in Permutation Flow Shop.- A Hybrid ILS Heuristic to the Referee Assignment Problem with an Embedded MIP Strategy.- On the Combination of Constraint Programming and Stochastic Search: The Sudoku Case.- Improvement Strategies for the F-Race Algorithm: Sampling Design and Iterative Refinement.- Using Branch & Bound Concepts in Construction-Based Metaheuristics: Exploiting the Dual Problem Knowledge.- Gradient-Based/Evolutionary Relay Hybrid for Computing Pareto Front Approximations Maximizing the S-Metric.- A Hybrid VNS for Connected Facility Location.- A Memetic Algorithm for the Optimum Communication Spanning Tree Problem.- Hybrid Numerical Optimization for Combinatorial Network Problems.