MOP2P: Peer-to-peer platform applied to multiobjective optimization problems
The resolution of multiobjective optimization problems (MOP) tipically requires a high demand of computational resources such as CPU and memory, finding candidate solutions at an acceptable response time, and with sufficient quality given a set of performance evaluation metrics. This paper presents a platform (denominated MOP2P) that allows the distributed execution of metaheuristic based algorithms for solving MOP using computers connected in a network in order to handle more computing power according to current demand. MOP2P implements Peer-to-Peer concepts to minimize the configuration effort and the management of a collection of computers. Also, a dynamic resource control mechanism is presented as a way to adapt the resource utilization levels intended for the optimization tasks executed on the different computers. Experimentally it was observed that the platform leverages the computing power available including desirable characteristics such as scalability, fault tolerance and non-intrusive use of computational resources.