Population Size Control for Efficiency and Efficacy Optimization in Population Based Metaheuristics

This paper proposes a mechanism of dynamic adjustment of the population size of population based metaheuristics in order to balance its efficacy and efficiency. In this approach, an external trajectory based metaheuristic (MH) is used to dynamically adjust the population size of an inner population based metaheuristic. A Particle Swarm Optmization (PSO) implemented for a Compute Unified Device Architecture platform (CUDA), called CUDA-PSO, is used as inner MH, while a sequential Simulated Annealing (SA) is used as an external one. The main objective of this paper is to evaluate the SA capabilities of finding a good balance between efficiency and efficacy during the CUDA-PSO execution and to assess its adaptability to different hardwares without any prior information about the computing platform. The results show that the new approach was able to find a good balance in most cases. Also, it was observed that this approach is able to adapt its operation to different hardwares.