Font Size: a A A

Research On Artificial Bee Colony Algorithm Based On Opposite Learning

Posted on:2017-03-03Degree:MasterType:Thesis
Country:ChinaCandidate:B Y WeiFull Text:PDF
GTID:2348330488996275Subject:Computer technology
Abstract/Summary:PDF Full Text Request
With the rapid development of science and technology and the continuous improvement of the level of productive forces,and the scale of production has gradually expanded, the complexity is also increasing, the shortage of resources, the market competition is becoming more and more fierce.So we put forward the higher requirements for enterprise management and production process monitoring. In order to change this situation and make the enterprises remain invincible in the competition, it is very important to study an effective way of job shop scheduling problem and apply it to the actual shop scheduling.The core of the job shop scheduling problem is to study the scheduling algorithm, which is based on the objective function to calculate the optimal or approximate optimal scheduling scheme. Therefore, this paper will propose an effective algorithm to better solve the job shop scheduling problem.As the artificial bee colony algorithm is proposed in recent years, it has been proved to have a good search ability, so it has great development prospects and research value. However, artificial bee colony algorithm has the advantages of less parameters and easy use, it is easy to fall into local optimum and premature convergence when using the standard artificial bee colony algorithm, and there is still a gap between the convergence speed, convergence precision and robustness. Therefore, this paper proposes a new algorithm based on opposite learning to solve the problem of job shop scheduling problem. The introduction of the mechanism of opposite learning makes the algorithm "jump out" local optimal, and speed up the convergence rate of the algorithm, which is more advantageous to find the real optimal solution.The main research content of this paper is as follows:(1) Research on artificial bee colony algorithm and its convergence analysis.The confidence interval of the solution is obtained by the feasible region of the initial solution, and the convergence of the initial solution is proved by using the Markov chain theory of the stochastic process.(2) Research on artificial bee colony algorithm based on opposite learning and itsconvergence analysis.The basic steps of the algorithm are given, the flow chart and the flow chart of the algorithm are proved. The performance of the algorithm is compared with the 4 benchmark functions.(3) The artificial bee colony algorithm based on opposite learning use in the job shopscheduling problem.The standard artificial bee colony algorithm is applied to the job shop scheduling problem. Experiments show that the improved artificial bee colony algorithm has better optimization ability in solving the job shop scheduling problem.
Keywords/Search Tags:Job shop scheduling problem, artificial bee colony algorithm, opposite learning, Markov chain, benchmark function
PDF Full Text Request
Related items