Travelling Salesman Problem (TSP) is a classical combinatorial optimization problem. This problem is NP-hard in nature. Meta-heuristic approaches have proved to be quite useful for approximate solution of difficult combinatorial optimization problems. Ant colony optimization is one of the popular Meta-heuristics and is unique on the basis of its distributed computation and indirect communicatio...

In this paper we propose an adaptation of the GRASP metaheuristic to solve multi-objective combinatorial optimization problems. In particular we describe several alternatives to specialize the construction and improvement components of GRASP when two or more objectives are considered. GRASP has been successfully coupled with path-relinking for single-objective optimization. In this paper, we pr...

In this work, we present an agent-based approach to multi-criteria combinatorial optimization. It allows to flexibly combine elementary heuristics that may be optimal for corresponding single-criterion problems. In the multi-criteria case a smart combination of such heuristics is supposed to efficiently approximate the whole Pareto-front of non-dominated trade-off solutions. We optimize an inst...

Graph vertex coloring is one of the most studied NP-hard combinatorial optimization problems. Given the hardness of the problem, various heuristic algorithms have been proposed for practical graph coloring, based on local search, population-based approaches and hybrid methods. The research in graph coloring heuristics is very active and improved results have been obtained recently, notably for ...

In many combinatorial domains, simple stochastic algorithms often exhibit superior performance when compared to highly customized approaches. Many of these simple algorithms outperform more sophisticated approaches on difficult benchmark problems; and often lead to better solutions as the algorithms are taken out of the world of benchmarks and into the real-world. Simple stochastic algorithms a...

The topic of this thesis, integer and combinatorial optimization, involves minimizing (or maximizing) a function of many variables, some of which belong to a discrete set, subject to constraints. This area has abundant applications in industry. Integer and combinatorial optimization problems are often difficult to solve due to the large and complex set of alternatives. The objective of this the...

In this paper we present an approach for performing very large state-space search on parallel machines. While the majority of searching methods in ArtificiM Intelligence rely on heuristics, the paralld algorithm we propose exploits the algebraic structure of problems to reduce both the time and space complexity required to solve these problems on massively parallel machines. Our algorithms have...

An f (n) dominance bound on a heuristic for some problem is a guarantee that the heuristic always returns a solution not worse than at least f (n) solutions. In this paper, we analyze several heuristics for Vertex Cover, Set Cover, and Knapsack for dominance bounds. In particular, we show that the well-known maximal matching heuristic of Vertex Cover provides an excellent dominance bound. We in...

In this paper we investigate the use of two evolutionary based heuristic to the bin packing problem. The intractability of this problem is a motivation for the pursuit of heuristics that produce approximate solutions. Unlike other evolutionary based heuristics used with optimization problems, ours do not use domain-speciic knowledge and has no specialized genetic operators. It uses a straightfo...