Self-organized Invasive Parallel Optimization with Self-repairing Mechanism

The 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.