Combinatorial optimization problems are often used to test heuris-tics. Among heuristics, stochastic ones deserve particular consideration being generally meta-heuristics that aim at performing reasonnably well on a wide spectrum of problems. Among them, evolutionary algorithms have recently appeared. Emphasis have been put on them by researches that have shown that they are able to solve eecie...

We present heuristics and fast approximate algorithms for NP-complete combinatorial optimization problems in binary variables. These heuristics are inspired from gradient ascents in a randomized phase space. In good cases a formula of (Russo, 1981) and a theorem of Lindeberg allow us to estimate the dynamics and derive algorithms. As an application we present a new approximate algorithm for the...

The Traveling Salesman Problem (TSP) is one of the most famous problems in combinatorial optimization. In this paper, we are going to examine how the techniques of Guided Local Search (GLS) and Fast Local Search (FLS) can be applied to the problem. Guided Local Search sits on top of local search heuristics and has as a main aim to guide these procedures in exploring efficiently and effectively ...

The Traveling Salesman Problem (TSP) is one of the most famous problems in combinatorial optimization. In this paper, we are going to examine how the techniques of Guided Local Search (GLS) and Fast Local Search (FLS) can be applied to the problem. Guided Local Search sits on top of local search heuristics and has as a main aim to guide these procedures in exploring efficiently and effectively ...

Stochastic optimization using simulated annealing has found wide applications in combinatorial optimization of NP-complete problems in recent years, especially in VLSI related problems. The Grid Connection Problem is a combinatorial optimization problem that is proven to be NP-complete. In this paper the simulated annealing algorithm has been applied to the Grid Connection Problem. We also prop...

Meta-heuristics support managers in decision-making with robust tools that provide high-quality solutions to important applications in business, engineering, economics and science in reasonable time horizons. In this paper we give some insight into the state of the art of meta-heuristics. This primarily focuses on the significant progress which general frames within the meta-heuristics field ha...

The scheduling problem is one of the most challenging problems faced in many different areas of everyday life. This problem can be formulated as a combinatorial optimization problem, and it has been solved with various methods using meta-heuristics and intelligent algorithms. We present in this paper a solution to the scheduling problem using two different heuristics namely Simulated Annealing ...

Randomized search heuristics, among them randomized local search and evolutionary algorithms, are applied to problems whose structure is not well understood, as well as to problems in combinatorial optimization. The analysis of these randomized search heuristics has been started for some well-known problems, and this approach is followed here for the minimum spanning tree problem. After motivat...