Property testing algorithms are “ultra”-efficient algorithms that decide whether a given object (e.g., a graph) has a certain property (e.g., bipartiteness), or is significantly different from any object that has the property. To this end property testing algorithms are given the ability to perform (local) queries to the input, though the decision they need to make usually concern properties wi...

Solving multi-level capacitated lot-sizing problems is still a challenging task, despite of increasing computational power and faster algorithms. In this paper a new approach combining an ant-based algorithm with an exact solver for (mixed-integer) linear programs is presented. A MAX-MIN ant system is developed to determine the principle production decisions, a LP/MIP solver is used to calculat...

This paper gives an algorithm for the graph bisection problem using the Ant System (AS) technique. The ant algorithm given in this paper differs from the usual ant algorithms in that the individual ant in the system does not construct a solution to the problem nor a component of the solution directly. Rather the collective behavior of the two species of ants in the system induces a solution to ...

In this paper we present a study of several Ant Colony Optimization (ACO) algorithms for the Set Covering Problem. In our computational study we emphasize the influence of different ways of defining the heuristic information on the performance of the ACO algorithms. Finally, we show that the best performing ACO algorithms we implemented, when combined with a fine-tuned local search procedure, r...

This paper presents two different efficiency-enhancement techniques for probabilistic model building genetic algorithms. The first technique proposes the use of a mutation operator which performs local search in the sub-solution neighborhood identified through the probabilistic model. The second technique proposes building and using an internal probabilistic model of the fitness along with the ...

This paper reports a Fast Local Search (FLS) algorithm which helps to improve the efficiency of hill climbing and a Guided Local Search (GLS) Algorithm which is developed to help local search to escape local optima and distribute search effort. To illustrate how these algorithms work, this paper describes their application to British Telecom’s workforce scheduling problem, which is a hard real ...

Search algorithms for solving CSP (Constraint Satisfaction Problems) usually fall into one of two main families: local search algorithms and systematic algorithms. Both families have their own advantages, Designing hybrid approaches seem promising since those advantages may be combined into a single approach. In this paper, we propose a new hybrid technique which performs an overall local searc...

In this article we present an application of the Ant Colony Optimization (ACO) metaheuristic to the single machine total weighted tardiness problem. First, we briefly discuss the constructive phase of ACO in which a colony of artificial ants generates a set of feasible solutions. Then, we introduce some simple but very effective local search. Last, we combine the constructive phase with local s...

We developed a two-stage approach (ACOLS) combining the ant colony optimization (ACO) algorithm and a 1-opt local search to solve forest transportation planning problems (FTPPs) considering fixed and variables costs and sediment yields expected to erode from road surfaces as side constraints. The ACOLS was designed for improving ACO performance and ensure the applicability to real-world, large-...

The Flexible Job Shop Scheduling Problem (FJSP) is one of the most general and difficult of all traditional scheduling problems. The Flexible Job Shop Problem (FJSP) is an extension of the classical job shop scheduling problem which allows an operation to be processed by any machine from a given set. The problem is to assign each operation to a machine and to order the operations on the machine...