This paper develops and assesses the performance of a hybrid prediction model using a radial basis function neural network and non-dominated sorting multiobjective genetic algorithm-II (NSGA-II) for various stock market forecasts. The proposed technique simultaneously optimizes two mutually conflicting objectives: the structure (the number of centers in the hidden layer) and the output mean square error (MSE) of the model. The best compromised non-dominated solution-based model was determined from the optimal Pareto front using fuzzy set theory. The performances of this model were evaluated in terms of four different measures using Standard and Poor 500 (S&P500) and Dow Jones Industrial Average (DJIA) stock data. The results of the simulation of the new model demonstrate a prediction performance superior to that of the conventional radial basis function (RBF)-based forecasting model in terms of the mean average percentage error (MAPE), directional accuracy (DA), Thelis’ U and average relative variance (ARV) values

Layout is an important issue in designing sensor networks. This paper proposes a new approach for energy efficient layout of wireless sensor network. The sensors communicate with each other to transmit their data to a high energy communication node which acts as an interface between data processing unit and sensors. Optimization of sensor locations is essential to provide communication for a longer duration. An energy efficient layout with good coverage based on Multi-objective Particle Swarm Optimization algorithm is proposed here. During the process of optimization, sensors move to form a uniformly distributed network The two objectives taken into consideration are coverage and lifetime. Basically a set of network layouts are obtained. The simulation results also exhibit improvement of performance with increase in number of generations in the algorithm

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 multi-objective 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, range resolution and Doppler shift are evaluated through exhaustive simulation study and are compared with those obtained by radial basis function (RBF) and auto correlation (ACF) based methods. The results demonstrate excellent performance of the proposed multi-objective method compared to its counterparts. As the number of center increases, the performance compressor also progressively increases but its complexity correspondingly increases. The proposed multi-objective method helps to select appropriate structure that makes a judicious compromise between the complexity and performance.