Journal of Computer and System Sciences - 26th IEEE Conference on Foundations of Computer Science, October 21-23, 1985
An aspiration-level interactive model for multiple criteria decision making
Computers and Operations Research - Special issue: implementing multiobjective optimization methods: behavioral and computational issues
Models for iterative global optimization
Models for iterative global optimization
Multi-objective genetic algorithm and its applications to flowshop scheduling
Computers and Industrial Engineering
Multiobjective Scheduling by Genetic Algorithms
Multiobjective Scheduling by Genetic Algorithms
Scheduling Computer and Manufacturing Processes
Scheduling Computer and Manufacturing Processes
Parto-Optimal Solutions for Multi-objective Production Scheduling Problems
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Multicriteria Scheduling: Theory, Models and Algorithms
Multicriteria Scheduling: Theory, Models and Algorithms
Design of multi-objective evolutionary algorithms: application to the flow-shop scheduling problem
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem
INFORMS Journal on Computing
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
An analysis of local search for the bi-objective bidimensional knapsack problem
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
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The article 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 and compared to other local search approaches. 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 metaheuristic is a key element of the Multi-Objective Optimization and Production Planning Solver MOOPPS. The software has been awarded the European Academic Software Award in Ronneby, Sweden (http://www.bth.se/llab/easa_2002.nsf), and has since been used for research and higher education in the mentioned problem domain (Geiger, 2006).