Enhanced Directed Differential Evolution Algorithm for Solving Constrained Engineering Optimization Problems

被引：35

作者：

Mohamed, Ali Wagdy ^{[1
]}

Mohamed, Ali Khater ^{[2
]}

Elfeky, Ehab Z. ^{[3
]}

Saleh, Mohamed ^{[4
]}

机构：

[1] Cairo Univ, Inst Stat Studies & Res, Dept Operat Res, Giza, Egypt

[2] Majmaah Univ, Al Majmaah, Saudi Arabia

[3] Cairo Univ, Fac Comp & Informat, Giza, Egypt

[4] Cairo Univ, Fac Comp & Informat, Dept Operat Res & Decis Support, Giza, Egypt

来源：

INTERNATIONAL JOURNAL OF APPLIED METAHEURISTIC COMPUTING | 2019年 / 10卷 / 01期

关键词：

Constrained Optimization; Differential Evolution; Engineering Optimization; Handling Constraints; Novel Mutation; PARTICLE SWARM OPTIMIZATION; GLOBAL OPTIMIZATION; PARAMETERS; MUTATION; MODEL; SELECTION; STRATEGY;

D O I：

10.4018/IJAMC.2019010101

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The performance of Differential Evolution is significantly affected by the mutation scheme, which attracts many researchers to develop and enhance the mutation scheme in DE. In this article, the authors introduce an enhanced DE algorithm (EDDE) that utilizes the information given by good individuals and bad individuals in the population. The new mutation scheme maintains effectively the exploration/exploitation balance. Numerical experiments are conducted on 24 test problems presented in CEC' 2006, and five constrained engineering problems from the literature for verifying and analyzing the performance of EDDE. The presented algorithm showed competitiveness in some cases and superiority in other cases in terms of robustness, efficiency and quality the of the results.

引用

页码：1 / 28

页数：28

共 70 条

[1]

Acharjee S, 2016, 2016 INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, AND OPTIMIZATION TECHNIQUES (ICEEOT), P525, DOI 10.1109/ICEEOT.2016.7755669

[2]

[Anonymous], COMPUTATIONAL INTELL

[3]

[Anonymous], 2006, PROBLEM DEFINITIONS

[4] An adaptive hybrid differential evolution algorithm for single objective optimization [J].

Asafuddoula, Md ;

Ray, Tapabrata ;

Sarker, Ruhul .

APPLIED MATHEMATICS AND COMPUTATION, 2014, 231 :601-618

[5] Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems [J].

Brest, Janez ;

Greiner, Saso ;

Boskovic, Borko ;

Mernik, Marjan ;

Zumer, Vijern .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2006, 10 (06) :646-657

[6] Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings [J].

Chatterjee, Sankhadeep ;

Sarkar, Sarbartha ;

Hore, Sirshendu ;

Dey, Nilanjan ;

Ashour, Amira S. ;

Balas, Valentina E. .

NEURAL COMPUTING & APPLICATIONS, 2017, 28 (08) :2005-2016

[7] Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art [J].

Coello, CAC .

COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2002, 191 (11-12) :1245-1287

[8] A new algorithm inspired in the behavior of the social-spider for constrained optimization [J].

Cuevas, Erik ;

Cienfuegos, Miguel .

EXPERT SYSTEMS WITH APPLICATIONS, 2014, 41 (02) :412-425

[9] Seeker optimization algorithm [J].

Dai, Chaohua ;

Chen, Weirong ;

Zhu, Yunfang .

2006 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY, PTS 1 AND 2, PROCEEDINGS, 2006, :225-229

[10] Differential Evolution: A Survey of the State-of-the-Art [J].

Das, Swagatam ;

Suganthan, Ponnuthurai Nagaratnam .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2011, 15 (01) :4-31

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