Numerical Optimization of Computer Models
Numerical Optimization of Computer Models
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Metamodel-Assisted Evolution Strategies
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
PSFGA: parallel processing and evolutionary computation for multiobjective optimisation
Parallel Computing - Special issue: Parallel and nature-inspired computational paradigms and applications
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Distributed computing of Pareto-optimal solutions with evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
PSFGA: a parallel genetic algorithm for multiobjective optimization
EUROMICRO-PDP'02 Proceedings of the 10th Euromicro conference on Parallel, distributed and network-based processing
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Multi-objective particle swarm optimization on computer grids
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Molecular docking with multi-objective Particle Swarm Optimization
Applied Soft Computing
Towards high speed multiobjective evolutionary optimizers
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Parallel Approaches for Multiobjective Optimization
Multiobjective Optimization
Local models--an approach to distributed multi-objective optimization
Computational Optimization and Applications
A Hierarchical Approach in Distributed Evolutionary Algorithms for Multiobjective Optimization
Large-Scale Scientific Computing
Dynamic multiple swarms in multiobjective particle swarm optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
HM'05 Proceedings of the Second international conference on Hybrid Metaheuristics
Approaches to parallelize pareto ranking in NSGA-II algorithm
PPAM'11 Proceedings of the 9th international conference on Parallel Processing and Applied Mathematics - Volume Part II
General framework for localised multi-objective evolutionary algorithms
Information Sciences: an International Journal
Hi-index | 0.00 |
While single-objective Evolutionary Algorithms (EAs) parallelization schemes are both well established and easy to implement, this is not the case for Multi-Objective Evolutionary Algorithms (MOEAs). Nevertheless, the need for parallelizing MOEAs arises in many real-world applications, where fitness evaluations and the optimization process can be very time consuming. In this paper, we test the ‘divide and conquer' approach to parallelize MOEAs, aimed at improving the speed of convergence beyond a parallel island MOEA with migration. We also suggest a clustering based parallelization scheme for MOEAs and compare it to several alternative MOEA parallelization schemes on multiple standard multi-objective test functions.