Showing papers by "Christian Blum published in 2004"

The hyper-cube framework for ant colony optimization

Abstract: Ant colony optimization is a metaheuristic approach belonging to the class of model-based search algorithms. In this paper, we propose a new framework for implementing ant colony optimization algorithms called the hyper-cube framework for ant colony optimization. In contrast to the usual way of implementing ant colony optimization algorithms, this framework limits the pheromone values to the interval [0,1]. This is obtained by introducing changes in the pheromone value update rule. These changes can in general be applied to any pheromone value update rule used in ant colony optimization. We discuss the benefits coming with this new framework. The benefits are twofold. On the theoretical side, the new framework allows us to prove that in the ant system, the ancestor of all ant colony optimization algorithms, the average quality of the solutions produced increases in expectation over time when applied to unconstrained problems. On the practical side, the new framework automatically handles the scaling of the objective function values. We experimentally show that this leads on average to a more robust behavior of ant colony optimization algorithms.

An Ant Colony Optimization Algorithm for Shop Scheduling Problems

Abstract: We deal with the application of ant colony optimization to group shop scheduling, which is a general shop scheduling problem that includes, among others, the open shop scheduling problem and the job shop scheduling problem as special cases. The contributions of this paper are twofold. First, we propose a neighborhood structure for this problem by extending the well-known neighborhood structure derived by Nowicki and Smutnicki for the job shop scheduling problem. Then, we develop an ant colony optimization approach, which uses a strong non-delay guidance for constructing solutions and which employs black-box local search procedures to improve the constructed solutions. We compare this algorithm to an adaptation of the tabu search by Nowicki and Smutnicki to group shop scheduling. Despite its general nature, our algorithm works particularly well when applied to open shop scheduling instances, where it improves the best known solutions for 15 of the 28 tested instances. Moreover, our algorithm is the first competitive ant colony optimization approach for job shop scheduling instances.

Theoretical and Practical Aspects of Ant Colony Optimization

Abstract: Combinatorial optimization problems are of high academical as well as practical importance. Many instances of relevant combinatorial optimization problems are, due to their dimensions, intractable for complete methods such as branch and bound. Therefore, approximate algorithms such as metaheuristics received much attention in the past 20 years. Examples of metaheuristics are simulated annealing, tabu search, and evolutionary computation. One of the most recent metaheuristics is ant colony optimization (ACO), which was developed by Prof. M. Dorigo (who is the supervisor of this thesis) and colleagues. This thesis deals with theoretical as well as practical aspects of ant colony optimization.* A survey of metaheuristics. Chapter 1 gives an extensive overview on the nowadays most important metaheuristics. This overview points out the importance of two important concepts in metaheuristics: intensification and diversification. * The hyper-cube framework. Chapter 2 introduces a new framework for implementing ACO algorithms. This framework brings two main benefits to ACO researchers. First, from the point of view of the theoretician: we prove that Ant System (the first ACO algorithm to be proposed in the literature) in the hyper-cube framework generates solutions whose expected quality monotonically increases with the number of algorithm iterations when applied to unconstrained problems. Second, from the point of view of the experimental researcher, we show through examples that the implementation of ACO algorithms in the hyper-cube framework increases their robustness and makes the handling of the pheromone values easier.* Deception. In the first part of Chapter 3 we formally define the notions of first and second order deception in ant colony optimization. Hereby, first order deception corresponds to deception as defined in the field of evolutionary computation and is therefore a bias introduced by the problem (instance) to be solved. Second order deception is an ACO-specific phenomenon. It describes the observation that the quality of the solutions generated by ACO algorithms may decrease over time in certain settings. In the second part of Chapter 3 we propose different ways of avoiding second order deception.* ACO for the KCT problem. In Chapter 4 we outline an ACO algorithm for the edge-weighted k-cardinality tree (KCT) problem. This algorithm is implemented in the hyper-cube framework and uses a pheromone model that was determined to be well-working in Chapter 3. Together with the evolutionary computation and the tabu search approaches that we develop in Chapter 4, this ACO algorithm belongs to the current state-of-the-art algorithms for the KCT problem.* ACO for the GSS problem. Chapter 5 describes a new ACO algorithm for the group shop scheduling (GSS) problem, which is a general shop scheduling problem that includes among others the well-known job shop scheduling (JSS) and the open shop scheduling (OSS) problems. This ACO algorithm, which is implemented in the hyper-cube framework and which uses a new pheromone model that was experimentally tested in Chapter 3, is currently the best ACO algorithm for the JSS as well as the OSS problem. In particular when applied to OSS problem instances, this algorithm obtains excellent results, improving the best known solution for several OSS benchmark instances. A final contribution of this thesis is the development of a general method for the solution of combinatorial optimization problems which we refer to as Beam-ACO. This method is a hybrid between ACO and a tree search technique known as beam search. We show that Beam-ACO is currently a state-of-the-art method for the application to the existing open shop scheduling (OSS) problem instances.

Deception in ant colony optimization

Abstract: The search process of a metaheuristic is sometimes misled. This may be caused by features of the tackled problem instance, by features of the algorithm, or by the chosen solution representation. In the field of evolutionary computation, the first case is called deception and the second case is referred to as bias. In this work we formalize the notions of deception and bias for ant colony optimization. We formally define first order deception in ant colony optimization, which corresponds to deception as being described in evolutionary computation. Furthermore, we formally define second order deception in ant colony optimization, which corresponds to the bias introduced by components of the algorithm in evolutionary computation. We show by means of an example that second order deception is a potential problem in ant colony optimization algorithms.

Ant colony optimization for the maximum edge-disjoint paths problem

Abstract: 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 which 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 inspired by the foraging behavior of real ants, whose distributed nature makes them suitable for the application in network environments. Our current version is aimed for the application in static graphs. In comparison to a multi-start greedy approach, our algorithm has advantages especially when speed is an issue.

Ant colony optimization and swarm intelligence : 4th International Workshop, ANTS 2004, Brussels, Belgium, September 5-8, 2004 : proceedings

Abstract: A Comparison Between ACO Algorithms for the Set Covering Problem.- A Comparison Between ACO Algorithms for the Set Covering Problem.- A VLSI Multiplication-and-Add Scheme Based on Swarm Intelligence Approaches.- ACO for Continuous and Mixed-Variable Optimization.- An Ant Approach to Membership Overlay Design.- An Ant Colony Optimisation Algorithm for the Set Packing Problem.- An Empirical Analysis of Multiple Objective Ant Colony Optimization Algorithms for the Bi-criteria TSP.- An External Memory Implementation in Ant Colony Optimization.- BeeHive: An Efficient Fault-Tolerant Routing Algorithm Inspired by Honey Bee Behavior.- Competition Controlled Pheromone Update for Ant Colony Optimization.- Cooperative Transport of Objects of Different Shapes and Sizes.- Deception in Ant Colony Optimization.- Evolution of Direct Communication for a Swarm-bot Performing Hole Avoidance.- Gathering Multiple Robotic A(ge)nts with Limited Sensing Capabilities.- Improvements on Ant Routing for Sensor Networks.- Integrating ACO and Constraint Propagation.- Logistic Constraints on 3D Termite Construction.- Modeling Ant Behavior Under a Variable Environment.- Multi-type Ant Colony: The Edge Disjoint Paths Problem.- On the Design of ACO for the Biobjective Quadratic Assignment Problem.- Reasons of ACO's Success in TSP.- S-ACO: An Ant-Based Approach to Combinatorial Optimization Under Uncertainty.- Time-Scattered Heuristic for the Hardware Implementation of Population-Based ACO.- Short Papers.- Ad Hoc Networking with Swarm Intelligence.- An Ant Colony Heuristic for the Design of Two-Edge Connected Flow Networks.- An Experimental Analysis of Loop-Free Algorithms for Scale-Free Networks.- An Experimental Study of the Ant Colony System for the Period Vehicle Routing Problem.- An Extension of Ant Colony System to Continuous Optimization Problems.- Ant Algorithms for Urban Waste Collection Routing.- Ants Can Play Music.- Backtracking Ant System for the Traveling Salesman Problem.- Colored Ants for Distributed Simulations.- Dynamic Routing in Mobile Wireless Networks Using ABC-AdHoc.- Fuzzy Ant Based Clustering.- How to Use Ants for Hierarchical Clustering.- Inversing Mechanical Parameters of Concrete Gravity Dams Using Ant Colony Optimization.- Large Pheromones: A Case Study with Multi-agent Physical A*.- Near Parameter Free Ant Colony Optimisation.- Particle Swarm Optimization Algorithm for Permutation Flowshop Sequencing Problem.- Search Bias in Constructive Metaheuristics and Implications for Ant Colony Optimisation.- Task Oriented Functional Self-organization of Mobile Agents Team: Memory Optimization Based on Correlation Feature.- Towards a Real Micro Robotic Swarm.- Posters.- A Hybrid Ant Colony System Approach for the Capacitated Vehicle Routing Problem.- A Swarm-Based Approach for Selection of Signal Plans in Urban Scenarios.- Ant Colony Behaviour as Routing Mechanism to Provide Quality of Service.- Applying Ant Colony Optimization to the Capacitated Arc Routing Problem.- Dynamic Optimization Through Continuous Interacting Ant Colony.- Dynamic Routing in Traffic Networks Using AntNet.- First Competitive Ant Colony Scheme for the CARP.- Hypothesis Corroboration in Semantic Spaces with Swarming Agents.- Mesh-Partitioning with the Multiple Ant-Colony Algorithm.