Genetic algorithm crossover operators for ordering applications
Computers and Operations Research - Special issue on genetic algorithms
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
A review of metrics on permutations for search landscape analysis
Computers and Operations Research
First vs. best improvement: An empirical study
Discrete Applied Mathematics - Special issue: IV ALIO/EURO workshop on applied combinatorial optimization
On the complexity of computing the hypervolume indicator
IEEE Transactions on Evolutionary Computation
Instance generators and test suites for the multiobjective quadratic assignment problem
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
PISA: a platform and programming language independent interface for search algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Two-phase Pareto local search for the biobjective traveling salesman problem
Journal of Heuristics
Path-guided mutation for stochastic pareto local search algorithms
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
A hybrid TP+PLS algorithm for bi-objective flow-shop scheduling problems
Computers and Operations Research
An efficient algorithm for computing hypervolume contributions**
Evolutionary Computation
Hype: An algorithm for fast hypervolume-based many-objective optimization
Evolutionary Computation
On sequential online archiving of objective vectors
EMO'11 Proceedings of the 6th international conference on Evolutionary multi-criterion optimization
Generalized adaptive pursuit algorithm for genetic pareto local search algorithms
Proceedings of the 13th annual conference on Genetic and evolutionary computation
An EMO algorithm using the hypervolume measure as selection criterion
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Fitness landscape analysis and memetic algorithms for the quadratic assignment problem
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
Proceedings of the 15th annual conference on Genetic and evolutionary computation
A survey of multi-objective sequential decision-making
Journal of Artificial Intelligence Research
Variable and large neighborhood search to solve the multiobjective set covering problem
Journal of Heuristics
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Pareto local search (PLS) methods are local search algorithms for multi-objective 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 neutral improvement. Furthermore, we introduce a deactivation mechanism that restarts PLS from an archive of solutions rather than from a single solution in order to avoid the exploration of already explored regions. To escape from a local optimal solution set we apply stochastic perturbation strategies, leading to stochastic Pareto local search algorithms (SPLS). We consider two perturbation strategies: mutation and path-guided mutation. While the former is unbiased, the latter is biased towards preserving common substructures between 2 solutions. We apply SPLS on a set of large, correlated bi-objective quadratic assignment problems (bQAPs) and observe that SPLS significantly outperforms multi-start PLS. We investigate the reason of this performance gain by studying the fitness landscape structure of the bQAPs using random walks. The best performing method uses the stochastic perturbation algorithms, the first improvement Pareto neigborhood exploration and the deactivation technique.