  The paper describes the proposition and application of a local search
metaheuristic for multi-objective optimization problems. It is based on two
main principles of heuristic search, intensification through variable
neighborhoods, and diversification through perturbations and successive
iterations in favorable regions of the search space. The concept is
successfully tested on permutation flow shop scheduling problems under multiple
objectives. While the obtained results are encouraging in terms of their
quality, another positive attribute of the approach is its' simplicity as it
does require the setting of only very few parameters. The implementation of the
Pareto Iterated Local Search metaheuristic is based on the MOOPPS computer
system of local search heuristics for multi-objective scheduling which has been
awarded the European Academic Software Award 2002 in Ronneby, Sweden
(http://www.easa-award.net/, http://www.bth.se/llab/easa_2002.nsf)
