PPLS/D: Parallel Pareto Local Search Based on Decomposition

PPLS/D: Parallel Pareto Local Search based on Decomposition

Pareto Local Search (PLS) is a basic building block in many multiobjective metaheuristics. In this paper, Parallel Pareto Local Search based on Decomposition (PPLS/D) is proposed. PPLS/D decomposes the original search space into L subregions and executes L parallel search processes in these subregions simultaneously. Inside each subregion, the PPLS/D process is first guided by a scalar objectiv...

Multi-objective Local Search Based on Decomposition

Many-Objective Pareto Local Search

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 prob...

Stochastic Pareto local search: Pareto neighbourhood exploration and perturbation strategies

Pareto local search (PLS) methods are local search algorithms for multiobjective combinatorial optimization problems based on the Pareto dominance criterion. PLS explores the Pareto neighbourhood of a set of non-dominated solutions until it reaches a local optimal Pareto front. In this paper, we discuss and analyse three different Pareto neighbourhood exploration strategies: best, first, and ne...

Pareto Local Policy Search for MOMDP Planning

Standard single-objective methods such as value iteration are not applicable to multi-objective Markov decision processes (MOMDPs) because they depend on a maximization, which is not defined if the rewards are multi-dimensional. As a result, special multi-objective algorithms are needed to find a set of policies that contains all optimal trade-offs between objectives, i.e. a set of Pareto optim...