Towards an analysis of local optimization algorithms
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Go with the winners for graph bisection
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows
New ideas in optimization
Multiobjective Scheduling by Genetic Algorithms
Multiobjective Scheduling by Genetic Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Application of Multi Objective Evolutionary Algorithms to Analogue Filter Tuning
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Empirical and analytic approaches to understanding local search heuristics
Empirical and analytic approaches to understanding local search heuristics
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
"Go with the winners" algorithms
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Experimental genetic operators analysis for the multi-objective permutation flowshop
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
| Hi-index | 0.00 |
This paper introduces a new algorithm to deal with multi-objective combinatorial and continuous problems. The algorithm is an extension of a previous one designed to deal with single objective combinatorial problems. The original purpose of the single objective version was to study in a rigorous way the properties the search graph of a particular problem needs to hold so that a randomized local search heuristic can find the optimum with high probability. The extension of these results to better understand multi-objective combinatorial problems seems to be a promising line of research. The work presented here is a first small step in this direction. A detailed description of the multi-objective version is presented along with preliminary experimental results on a well known combinatorial problem. The results show that the algorithm has the desired characteristics.