Full-Text Search:
Home|About CNKI|User Service|中文
Add to Favorite Get Latest Update

Application of modified penalty function method and bats algorithm in constrained optimization

LIU Yunlian;WU Tiebin;WANG Junnian;ZHOU Taoyun;CHENG Yun;Department of Information Science and Engineering, Hunan University of Humanities Science and Technology;College of Information and Electrical Engineering, Hunan University of Science and Technology;Department of Electrical and Mechanical Engineering, Hunan University of Humanities Science and Technology;  
A solving method for constrained optimization problem based on adaptive penalty function and improved bats algorithm is designed. An adaptive penalty function method is proposed, which both takes the circumstances of constraint violations and characteristics of evolutionary process into consideration. The more frequently a constraint is violated, the more powerful it is, the larger penalty coefficient is given to it. The more infeasible solutions in the population, the smaller the constrain should be, in other words, the smaller the penalty coefficient should be, in order to keep the diversity of the population. An improved bats algorithm is proposed, which generates the initial population by using the ergodicity of chaos,and enhances the quality of the initial population and diversity of population. In the local search of bats algorithm which takes the pulse loudness into consideration, crossover operation is added. In order to prevent the algorithm from falling into local optimal solution in the late, variation operation is added, which ensures the diversity of the population. Then adaptive penalty function and improved bats algorithm are mixed to solve constrained optimization problem, and 4 complex standard test functions and 2 practical engineering problems prove the feasibility and effectiveness of the solving methods for constrained optimization problem.
Download(CAJ format) Download(PDF format)
CAJViewer7.0 supports all the CNKI file formats; AdobeReader only supports the PDF format.
©CNKI All Rights Reserved