in this paper, a multi-objective method is used to optimize a heat recovery steam generator (hrsg). two objective functions have been used in the optimization, which are irreversibility and hrsg equivalent volume. the former expresses the exergetic efficiency and the latter demonstrates the cost of the hrsg. decision variables are geometric and operational parameters of the hrsg. the results of...

Green manufacturing has become a new production mode for the development and operation of modern future industries. The flexible job shop scheduling problem (FJSP), as one key core problems in field green process planning, hot topic difficult issue research. In this paper, an improved multi-objective wolf pack algorithm (MOWPA) is proposed solving with transportation constraints. Firstly, model...

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 appr...

PM2.5 is one of the main factors air pollution, so prediction great significance. For this reason, innovation priority discrete nonlinear gray model based on wolf optimization algorithm established, which principle in system principle. Try to optimize model, and use solve parameters. First, basic theory proposed. On basis, used improve cumulative generation sequence, with parameters defined. Fi...

Abstract A combined prediction model based on long short-term memory neural network (LSTM) and convolutional (CNN) is proposed in order to increase the accuracy of load. To address issue that gray wolf optimization (GWO) search process prone falling into local optimum. An improved grey algorithm (IGWO) update convergence factor using lower incomplete gamma function improve global performance. T...

Abstract In many real-world situations, we have to deal with multiple objectives simultaneously in order make appropriate decisions. The presence of an optimization problem makes the challenging because most time these are conflicting nature. For example, may want maximize return on investment a portfolio and, other hand, minimize risk associated assets portfolio. We cost product while maximizi...