Development of modified cooperative particle swarm optimization with inertia weight for feature selection

Abstract

The article presents a modified Cooperative Particle Swarm Optimization with Inertia Weight (CPSOIW) for Smart-technology of forecasting and control of complex objects. The software “CPSOIW (Cooperative Particle Swarm Optimization with Inertia Weight)” based on a modified CPSOIW algorithm has been developed in Python programming language and is used to process a multidimensional data and to create an optimal set of descriptors. The proposed algorithm combines the advantages of inertia weight particle swarm optimization (IWPSO) algorithm and cooperative particle swarm optimization (CPSO) algorithm. IWPSO algorithm allows to avoid an early convergence and to prevent particles from trapping into local optima due to update an inertia weight at each iteration. CPSO algorithm explores a search space efficiency and more detailed in a real time by parallel computing of subswarms The modelling results and comparative analysis of CPSOIW and IWPSO algorithms have been performed based on benchmark datasets and a real production data from Installation 300 of Tengizchevroil oil and gas company.