Parallel Optimization: Theory, Algorithms and Applications
Parallel Optimization: Theory, Algorithms and Applications
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Multiple Tuple Spaces in Linda
PARLE '89 Proceedings of the Parallel Architectures and Languages Europe, Volume II: Parallel Languages
Multi-objective particle swarm optimization on computer grids
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Integrating user preferences with particle swarms for multi-objective optimization
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Consideration of Partial User Preferences in Evolutionary Multiobjective Optimization
Multiobjective Optimization
Self-organized Parallel Cooperation for Solving Optimization Problems
ARCS '09 Proceedings of the 22nd International Conference on Architecture of Computing Systems
Covering pareto sets by multilevel evolutionary subdivision techniques
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Distributed computing of Pareto-optimal solutions with evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Preference-based multi-objective particle swarm optimization using desirabilities
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Considerations in engineering parallel multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
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Self-organized Invasive Parallel Optimization (SIPO) is a new framework for solving optimization problems on parallel platforms. In contrast to existing approaches, the resources in SIPO are self-organized and represented as a unified resource to the user who specifies the optimization problem and its preferences to the system. SIPO starts working with one resource and automatically divides the optimization task stepwise into smaller tasks which are assigned to more resources. This job assignment is decided on demand by the resources. The novelty here is that there is no need to specify the number of parallel computing resources in the beginning of the optimization. This number is estimated during the optimization process by the resources. The proposed new framework of SIPO is described in this paper with respect to multi-objective optimization problems but it has a much larger scope. A comparative evaluation of using SIPO in multi-objective optimization problems shows that this adaptive approach can obtain equally good or sometimes even better solutions than other parallel and non-parallel methods which are not self-organized.