We extend the number sorting algorithms on the GPU to sort large multi-field records. We notice that traditional way of sorting the records by first sorting a (key, index) pair to obtain the sorted permutation of the records followed by actually rearranging the entire records to their final position might not actually be the most efficient way to sort them depending on the type of sorting algor...

This paper introduces a novel multi-objective evolutionary algorithm based on cat swarm optimizationalgorithm (EMCSO) and its application to solve a multi-objective knapsack problem. The multi-objective optimizers try to find the closest solutions to true Pareto front (POF) where it will be achieved by finding the less-crowded non-dominated solutions. The proposed method applies cat swarm optim...

In this paper we present a new method of 3D non-negative tensor factorization (NTF) that is robust in the presence of noise and has many potential applications, including multi-way blind source separation (BSS), multi-sensory or multi-dimensional data analysis, and sparse image coding. We consider alphaand beta-divergences as error (cost) functions and derive three different algorithms: (1) mul...

In this study, by using a multi-objective optimization technique, the optimal design points of forced convective heat transfer in tubular arrangements were predicted upon the size, pitch and geometric configurations of a tube bank. In this way, the main concern of the study is focused on calculating the most favorable geometric characters which may gain to a maximum heat exchange as well as a m...

resource limitation in zero time may cause to some profitable projects not to be selected in project selection problem, thus simultaneous project portfolio selection and scheduling problem has received significant attention. in this study, budget, investment costs and earnings are considered to be stochastic. the objectives are maximizing net present values of selected projects and minimizing v...

Previous theory work on multi-objective evolutionary algorithms considers mostly easy problems that are composed of unimodal objectives. This paper takes a first step towards deeper understanding how solve multi-modal problems. We propose the OneJumpZeroJump problem, bi-objective problem whose single objectives isomorphic to classic jump functions benchmark. prove simple optimizer (SEMO) cannot...

The extension of estimation of distribution algorithms (EDAs) to the multi–objective domain has led to multi–objective optimization EDAs (MOEDAs). Most MOEDAs have limited themselves to porting single-objective EDAs to the multi–objective domain. Although MOEDAs have proved to be a valid approach, the last point is an obstacle to the achievement of a significant improvement regarding “standard”...