Mostaghim, SanazPfeiffer, FriederikeSchmeck, Hartmut2017-12-062017-12-062011https://dl.gi.de/handle/20.500.12116/8557The parallelization of optimization algorithms is very beneficial when the function evaluations of optimization problems are time consuming. However, parallelization gets very complicated when we deal with a large number of parallel resources. In this paper, we present a framework called Self-organized Invasive Parallel Optimization (SIPO) in which the resources are self-organized. The optimization starts with a small number of resources which decide the number of further required resources on-demand. This means that more resources are stepwise added or eventually released from the platform. In this paper, we study an undesired effect in such a self-organized system and propose a self-repairing mechanism called Recovering-SIPO. These frameworks are tested on a series of multi-objective optimization problems.enShared MemoryMultiobjective OptimizationSelection MechanismParallel OptimizationParallel PlatformText/Journal Article10.1007/BF033419880177-0454