Memetic particle swarm optimisation for missing value imputation

Incomplete values in databases stand as a major concern for data analysts and many methods have been devised to handle them in different missing scenarios. Many researchers are increasingly using evolutionary algorithms for handling them. In this paper, a memetic algorithm based approach is proposed which integrates the principles of particle swarm optimisation and simulated annealing, a local search method. A novel initialisation strategy for PSO is also proposed in order to seed good particles into the population. Simulated annealing prevents PSO from premature convergence and helps it in reaching global optimum. PSO algorithm exhibits explorative behaviour and SA exhibits exploitative behaviour and serves as the right combination for memetic algorithm implementation. The proposed algorithm is implemented in different datasets to estimate the missing values and the imputation accuracy and the time taken for execution is found to be better than other standard methods.