TL;DR
This paper introduces a novel Many-Objective Pareto Local Search algorithm that enhances efficiency for multi-objective combinatorial optimization problems with more than two objectives, using innovative data structures and exploration strategies.
Contribution
It presents a new algorithm with three mechanisms to improve Pareto Local Search efficiency for many-objective problems, demonstrated on TSP variants.
Findings
High effectiveness on TSP and TSP with profits instances
Efficient large Pareto archive updates with ND-Tree
Partial neighborhood exploration improves performance
Abstract
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…
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