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Item Prediction based mean-variance model for constrained portfolio assets selection using multiobjective evolutionary algorithms(2016) Mishra S.K.; Panda G.; Majhi B.In this paper, a novel prediction based mean-variance (PBMV) model has been proposed, as an alternative to the conventional Markowitz mean-variance model, to solve the constrained portfolio optimization problem. In the Markowitz mean-variance model, the expected future return is taken as the mean of the past returns, which is incorrect. In the proposed model, first the expected future returns are predicted, using a low complexity heuristic functional link artificial neural network (HFLANN) model and the portfolio optimization task is carried out by using multi-objective evolutionary algorithms (MOEAs). In this paper, swarm intelligence based, multiobjective optimization algorithm, namely self-regulating multiobjective particle swarm optimization (SR-MOPSO) has also been proposed and employed efficiently to solve this important problem. The Pareto solutions obtained by applying two other competitive MOEAs and using the proposed PBMV models and Markowitz mean-variance model have been compared, considering six performance metrics and the Pareto fronts. Moreover, in the present study, the nonparametric statistical analysis using the Sign test and Wilcoxon rank test are also carried out, to compare the performance of the algorithms pair wise. It is observed that, the proposed PBMV model based approach provides better Pareto solutions, maintaining adequate diversity, and also quite comparable to the Markowitz model. From the simulation result, it is observed that the self regulating multiobjective particle swarm optimization (SR-MOPSO) algorithm based on PBMV model, provides the best Pareto solutions amongst those offered by other MOEAs. � 2016 Elsevier B.V.Item Improved portfolio optimization combining multiobjective evolutionary computing algorithm and prediction strategy(2012) Mishra S.K.; Panda G.; Majhi B.; Majhi R.In conventional mean-variance model of portfolio optimization problem the expected return is taken as the mean of the past returns. This assumption is not correct and hence the method leads to poor portfolio optimization performance. Hence an alternative but efficient method is proposed in which the mean and variance of expected return are first predicted with a low complexity functional link artificial neural network model (FLANN). The predicted values of mean and variance are consequently used in multi objective swarm intelligence techniques for achieving better performance. The multi objective swarm intelligence techniques chosen are non-dominated sorting genetic algorithm-II (NSGA - II) and multi objective particle swarm optimization (MOPSO).The performance of the proposed prediction based portfolio optimization model has been compared with the Markowitz mean-variance model. The comparison of the performance includes three performance metrics, Pareto front and nonparametric statistical test using the Sign test. On examining the performance metrics it is observed that the proposed prediction based portfolio optimization model approach provided improved Pareto solutions but maintaining adequate diversity. � 2012 Newswood Limited. All rights reserved.Item An efficient multi-objective pulse radar compression technique using RBF and NSGA-II(2009) Baghel V.; Panda G.; Srihari P.; Rajarajeswari K.; Majhi B.The task of radar pulse compression is formulated as a multi-objective optimization problem and has been effectively solved using radial basis function (RBF) network and multiobjective genetic algorithm (NSGA-II). The pulse compression performance of three different codes in terms of signal to peak side-lobe ratio (SSR) under noisy environment