Efficient and Accurate Parallel Genetic Algorithms
Efficient and Accurate Parallel Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Multi-objective particle swarm optimization on computer grids
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Multiobjective Optimization: Interactive and Evolutionary Approaches
Multiobjective Optimization: Interactive and Evolutionary Approaches
Local models--an approach to distributed multi-objective optimization
Computational Optimization and Applications
Distributed computing of Pareto-optimal solutions with evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Parallelism and evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Self-organized invasive parallel optimization
Proceedings of the 3rd workshop on Biologically inspired algorithms for distributed systems
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This paper is about using a set of self-organized computing resources to perform multi-objective optimization. In the proposed approach, the computing resources are presented as a unified resource to the user where in traditional parallel optimization paradigms the user has to assign tasks to the resources, collect the best available solutions and deal with failing resources. In this approach called self-organized parallel cooperation model, the user has to specify the preferences and only give the objective functions to the system. The self-organized computing resources deliver the obtained solutions after a certain time to the user. In such a system, fast resources must continue the optimization as long as the overall computing time is not over. However as the solutions of a multi-objective problem depend on each other (via the domination relation) adding a waiting time to the fast processors would affect the quality of the solutions. This has been studied on a scenario of 100 heterogeneous computing resources in the presence of failures in the system.