Hybrid meta-heuristics for combinatorial optimization

Tommaso Urli Hybrid Meta - Heuristics for Combinatorial Optimization

Guidelines for the use of meta-heuristics in combinatorial optimization

The 18th EURO Summer/Winter Institute (ESWI XVIII) took place during the spring 2000 in Switzerland. The topic of ESWI XVIII, ‘‘Meta-heuristics in Combinatorial Optimization’’, was selected due to its great current scientific interest: indeed, in recent years, several meta-heuristics have proved to be highly efficient for the solution of difficult combinatorial optimization problems. The Instit...

HEURISTICS FOR COMBINATORIAL OPTIMIZATION Lecture 3 Metaheuristics for Construction Heuristics

Task: Pack all the items into a minimum number of unit-capacity bins An algorithm A is said to be a δ-approximation algorithm if it runs in polynomial time and for every problem instance π with optimal solution value OP T (π) minimization: A(π) OP T (π) ≤ δ δ ≥ 1 maximization: A(π) OP T (π) ≥ δ δ ≤ 1 A family of approximation algorithms for a problem Π, {A } , is called a polynomial approximati...

Optimization Heuristics for the Combinatorial Auction Problem

This paper presents and compares three heuristics for the combinatorial auction problem. Besides a simple greedy (SG) mechanism, two metaheuristics, a simulated annealing (SA), and a genetic algorithm (GA) approach are developed which use the combinatorial auction process to find an allocation with maximal revenue for the auctioneer. The performance of these three heuristics is evaluated in the...

Generic Heuristics for Combinatorial Optimization Problems

This paper discusses the use of several heuristic techniques for problems of combinatorial optimization. We especially consider the advantages and disadvantages of naturally inspired generic techniques like Simulated Annealing, Evolution Strategies, or Genetic Algorithms. This reflection gives a quite intuitive motivation for hybrid approaches that aim to combine advantageous aspects of the cer...