  We propose a new Pareto Local Search Algorithm for the many-objective
combinatorial optimization. Pareto Local Search proved to be a very effective
tool in the case of the bi-objective combinatorial optimization and it was used
in a number of the state-of-the-art algorithms for problems of this kind. On
the other hand, the standard Pareto Local Search algorithm becomes very
inefficient for problems with more than two objectives. We build an effective
Many-Objective Pareto Local Search algorithm using three new mechanisms: the
efficient update of large Pareto archives with ND-Tree data structure, a new
mechanism for the selection of the promising solutions for the neighborhood
exploration, and a partial exploration of the neighborhoods. We apply the
proposed algorithm to the instances of two different problems, i.e. the
traveling salesperson problem and the traveling salesperson problem with
profits with up to 5 objectives showing high effectiveness of the proposed
algorithm.
