Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Reference point based multi-objective optimization using evolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Multi-objective particle swarm optimization on computer grids
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Parallel Approaches for Multiobjective Optimization
Multiobjective Optimization
Multiobjective Optimization
Local models--an approach to distributed multi-objective optimization
Computational Optimization and Applications
Self-organized Parallel Cooperation for Solving Optimization Problems
ARCS '09 Proceedings of the 22nd International Conference on Architecture of Computing Systems
A Hierarchical Approach in Distributed Evolutionary Algorithms for Multiobjective Optimization
Large-Scale Scientific Computing
A distributed pool architecture for genetic algorithms
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A cognitive system based on fuzzy information processing and multi-objective evolutionary algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Dynamic search initialisation strategies for multi-objective optimisation in peer-to-peer networks
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A systems approach to evolutionary multiobjective structural optimization and beyond
IEEE Computational Intelligence Magazine
On gradient based local search methods in unconstrained evolutionary multi-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Self-organized invasive parallel optimization
Proceedings of the 3rd workshop on Biologically inspired algorithms for distributed systems
Parallelization of multi-objective evolutionary algorithms using clustering algorithms
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
A service oriented architecture for decision making in engineering design
EGC'05 Proceedings of the 2005 European conference on Advances in Grid Computing
Asynchronous master/slave moeas and heterogeneous evaluation costs
Proceedings of the 14th annual conference on Genetic and evolutionary computation
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
The asynchronous island model and NSGA-II: study of a new migration operator and its performance
Proceedings of the 15th annual conference on Genetic and evolutionary computation
Multi-objective evolutionary design of robust controllers on the grid
Engineering Applications of Artificial Intelligence
General framework for localised multi-objective evolutionary algorithms
Information Sciences: an International Journal
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In this paper, we suggest a distributed computing approach for finding multiple Pareto-optimal solutions. When the number of objective functions is large, the resulting Pareto-optimal front is of large dimension, thereby requiring a single processor multi-objective EA (MOEA) to use a large population size and run for a large number of generations. However, the task of finding a well-distributed set of solutions on the Pareto-optimal front can be distributed among a number of processors, each pre-destined to find a particular portion of the Pareto-optimal set. Based on the guided domination approach [1], here we propose a modified domination criterion for handling problems with a convex Pareto-optimal front. The proof-of-principle results obtained with a parallel version of NSGA-II shows the efficacy of the proposed approach.