At depots with refined oil shortage, arranging a reasonable distribution scheme limited supply affects operation costs, demand satisfaction rate of gasoline stations (hereafter, ‘station satisfaction’), and overtime penalty. This study considers the problem shortages using multi-objective optimisation approach from perspective decision makers marketing companies. The modelling solving process i...

Reconfigurable Manufacturing System (RMS) justifies the need of hour by combining the high throughput of dedicated manufacturing system with the flexibility of flexible manufacturing systems. At the heart of RMS lies the Reconfigurable Machine Tools which are capable of performing multiple operations in its existing configurations and can further be reconfigured into more configurations which m...

Software cost estimation is the process of predicting the effort required to develop a software system. The basic input for the software cost estimation is coding size and set of cost drivers, the output is Effort in terms of Person-Months (PM’s). Here, the use of support vector regression (SVR) has been proposed for the estimation of software project effort. We have used the COCOMO dataset and...

Abstract To solve the problems of lack diversity and low quality solution set in solving process traditional transformer optimization algorithm, a multi-objective algorithm based on NSGA-II is proposed. Firstly, model power winding was established with manufacturing cost, additional coil loss short-circuit impedance as objectives. Secondly, used to optimize design. The results showed that, comp...

Finite-set model predictive control (FS-MPC) as current (PCC) is considered an exciting option for the stator of multiphase machines due to their flexibility and easy inclusion constraints. The weighting factors (WFs) PCC must be tuned variables interest, such machine losses <tex-math nota...

Modified source code validation is done by regression testing. In testing, the time and resources are limited, in which we have to select minimal test cases from suites reduce execution time. The case minimization process deals with optimization of testing removing redundant or prioritizing cases. This study proposed a prioritization approach based on multiobjective particle swarm (MOPSO) consi...

With the increase in number of high-dimensional data, characteristic phenomenon unbalanced distribution is increasingly presented various big data applications. At same time, most existing clustering and feature selection algorithms are based on maximizing accuracy. In addition, hybrid approach can effectively solve problem data. Aiming at shortcomings algorithm, a multi-objective PSO algorithm...

numerous problems encountered in real life cannot be actually formulated as a single objective problem; hence the requirement of multi-objective optimization (moo) had arisen several years ago. due to the complexities in such type of problems powerful heuristic techniques were needed, which has been strongly satisfied by swarm intelligence (si) techniques. particle swarm optimization (pso) has ...

A multi-objective resource scheduling model with quality of service (QoS) restriction was built to improve the computing efficiency of Map-Reduce resource scheduling. The model considered the scheduling problem of both Map and Reduce phase and a chaotic multi-objective particle swarm algorithm was proposed to solve the model. The information entropy theory was used to maintain nondomination sol...

In this paper, we proposed an algorithm for solving the problem of task scheduling using particle swarm optimization algorithm, with changes in the Selection and removing the guide and also using the technique to get away from the bad, to move away from local extreme and diversity. Scheduling algorithms play an important role in grid computing, parallel tasks Scheduling and sending them to ...