Earticle

초록

영어

Differential evolution (DE) algorithms have been extensively and frequently applied to solve optimizationproblems. Theoretical analyses of their properties are important to understand the underlying mechanismsand to develop more efficient algorithms. In this paper, firstly, we introduce an absorbing Markovsequence to model a DE algorithm. Secondly, we propose and prove two theorems that provide sufficientconditions for DE algorithm to guarantee converging to the global optimality region. Finally, we design two DE algorithms that satisfy the preconditions of the two theorems, respectively. The two proposed algorithmsare tested on the CEC2013 benchmark functions, and compared with other existing algorithms.Numerical simulations illustrate the converge, effectiveness and usefulness of the proposed algorithms.

목차

Abstract
 1. Introduction
 2. Basic Concepts and Formulations of Differential Evolution
 3. Modeling DE Using Absorbing Markov Sequence
 4. Sufficient Conditions for DE Guaranteed Convergence
  4.1. Global search and Local Search Methods
  4.2. DE Convergence as a Global Search Method
  4.3. DE Convergence as a Local Search Method
 5. Stochastic Differential Evolution Algorithms
  5.1. Stochastic Differential Evolution Optimizer
  5.2. Convergence Analysis
 6. Simulation and Discussions
  6.1. Test Functions and Experimental Settings
  6.2. Simulation Results and Discussions
 7. Conclusions
 Acknowledgement
 References[1]

키워드

저자

• Liang Sun [ College of Computer Science and Technology, Dalian University of Technology, Dalian 116023, China ]
• Hongwei Ge [ College of Computer Science and Technology, Dalian University of Technology, Dalian 116023, China ]
• Limin Wang [ Department of Management Science and Information Engineering, Jilin University of Finance and Economics, Changchun 130117, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605