|Table of Contents|

Differential Evolutionary Algorithm Based on New Ensemble ofConstraint Handing Techniques(PDF)

《南京师大学报（自然科学版）》[ISSN:1001-4616/CN:32-1239/N]

Issue:
2019年04期
Page:
1-11
Research Field:
·数学与计算机科学·
Publishing date:

Info

Title:
Differential Evolutionary Algorithm Based on New Ensemble ofConstraint Handing Techniques
Author(s):
Sun Yuehong1; 2; Wang Dan1
(1. School of Mathematical Sciences,Nanjing Normal University,Nanjing 210023,China)(2.Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex Systems,Nanjing 210023,China)
Keywords:
constrained optimization; differential evolutionary algorithm; ensemble of constraint handling techniques
PACS:
TP391; TN911.7
DOI:
10.3969/j.issn.1001-4616.2019.04.001
Abstract:
In this paper,a differential evolutionary algorithm based on new ensemble of constraint handing techniques is proposed to solve optimization problems with constraints. At the stage of generating new individuals,the algorithm adopts three different mutation strategies. Different constraint handing techniques are used to select new individuals,and local search is introduced to enhance the local optimization ability and avoid the algorithm falling into local optimum. Numerical experiments are carried out on 28 benchmark functions from CEC 2017 and compared with other advanced algorithms. The results show that the new algorithm performs better in solution accuracy.

References:

[1] MICHALEWICZ Z,SCHOENAUER M. Evolutionary algorithms for constrained parameter optimization problems[J]. Evolutionary computation,1996,4(1):1-32.
[2]KOZIEL S,MICHALEWICZ Z. Evolutionary algorithms,homomorphous mappings,and constrained parameter optimization[J]. Evolutionary computation,1999,7(1):19-44.
[3]RUNARSSON T P,YAO X. Stochastic ranking for constrained evolutionary optimization[J]. IEEE transactions evolutionary computation,2000,4(3):284-294.
[4]TESSEMA B,YEN G G. A self adaptive penalty function based algorithm for constrained optimization[C]//2006 IEEE Congress on Evolutionary Computation(CEC),Vancouver,BC,Canada,2006:246-253.
[5]TAKAHAMA T,SAKAI S. Constrained optimization by the constrained differential evolution with gradient-based mutation and feasible elites[C]//2006 IEEE Congress on Evolutionary Computation(CEC),Vancouver,BC,Canda,2006:246-253,1-8.
[6]WANG Y,CAI Z,GUO G,et al. Multiobjective optimization and hybrid evolutionary algorithm to solve constrained optimization problems[J]. IEEE transactions on systems man & cybernetics part B,2007,37(3):560-575.
[7]MALLIPEDDI R,SUGANTHAN P N. Ensemble of constraint handling techniques[J]. IEEE transactions on evolutionary computation,2010,14(4):561-579.
[8]WOLPERT D H,MACREADY W G. No free lunch theorems for optimization[J]. IEEE transactions evolutionary computation,1997,1(1):67-82.
[9]DEB K. An efficient constraint handling method for genetic algorithms[J]. Computer methods in applied mechanics and engineering,2000,186(2):311-338.
[10]TRIVEDI A,SANYAL K,VERMA P,et al. A unified differential evolution algorithm for constrained optimization problems[C]//2017 IEEE Congress on Evolutionary Computation(CEC),San Sebastian,Spain,2017:1231-1238.
[11]QIN A K,HUANG V L,SUGANTHAN P N. Differential evolution algorithm with strategy adaptation for global numerical optimization[J]. IEEE transactions on evolutionary computation,2009,13(2):398-417.
[12]WANG Y,CAI Z,ZHOU Y,et al. An adaptive trade-off model for constrained evolutionary optimization[J]. IEEE transactions on evolutionary computation,2008,12(1):80-92.
[13]STORN R,PRICE K V. Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces[J]. Global optimization,1997,11:41-359.
[14]WU G,MALLIPEDI R,SUGANTHAN P N. Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization[R]. Singapore:Nanyang Technological University,2016.
[15]R.Pol6kov6. L-SHADE with competing strategies applied to constrained optimization[C]//2017 IEEE Congress on Evolutionary Computation(CEC),San Sebastian,Spain,2017:1683-1689.
[16]TVRDIK J,POLAKOVA R. A simple framework for constrained problems with application of L-SHADE44 and IDE[C]//2017 IEEE Congress on Evolutionary Computation(CEC),San Sebastian,Spain,2017:1436-1443.
[17]ALES^B,ZAMUDA. Adaptive constraint handling and success history differential evolution for CEC 2017 constrained real-parameter optimization[C]//2017 IEEE Congress on Evolutionary Computation(CEC),San Sebastian,Spain,2017:2443-2450.

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Last Update: 2019-12-31