Aesthetic Differential Evolution Algorithm for Solving Computationally Expensive Optimization Problems

被引：0

作者：

Poonia, Ajeet Singh ^{[1
]}

Sharma, Tarun Kumar ^{[2
]}

Sharma, Shweta ^{[2
]}

Rajpurohit, Jitendra ^{[2
]}

机构：

[1] Govt Coll Engn & Technol, Bikaner, India

[2] Amity Univ Rajasthan, Noida, India

来源：

ADVANCES IN NATURE AND BIOLOGICALLY INSPIRED COMPUTING | 2016年 / 419卷

关键词：

Differential evolution; DE; Optimization; Computationally expensive optimization problems;

D O I：

10.1007/978-3-319-27400-3_8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The applications of Differential Evolution (DE) and the attraction of researchers towards it, shows that it is a simple, powerful, efficient as well as reliable evolutionary algorithm to solve optimization problems. In this study an improved DE called aesthetic DE algorithm (ADEA) is introduced to solve Computationally Expensive Optimization (CEO) problems discussed in competition of congress of evolutionary computation (CEC) 2014. ADEA uses the concept of mirror images to produce new decorative positions. The mirror is placed near the most beautiful (global best) individual to accentuate its attractiveness (significance). Simulated statistical results demonstrate the efficiency and ability of the proposal to obtain good results.

引用

下载

页码：87 / 96

页数：10

共 50 条

[31] A novel differential evolution algorithm for solving constrained engineering optimization problems
Mohamed, Ali Wagdy
JOURNAL OF INTELLIGENT MANUFACTURING, 2018, 29 (03) : 659 - 692
[32] A Self-adaptive Differential Evolution Algorithm for Solving Optimization Problems
Farda, Irfan
Thammano, Arit
PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON COMPUTING AND INFORMATION TECHNOLOGY (IC2IT 2022), 2022, 453 : 68 - 76
[33] A hybrid firefly and particle swarm optimization algorithm for computationally expensive numerical problems
Aydilek, Ibrahim Berkan
APPLIED SOFT COMPUTING, 2018, 66 : 232 - 249
[34] Memetic algorithm using multi-surrogates for computationally expensive optimization problems
Zhou, Zongzhao
Ong, Yew Soon
Lim, Meng Hiot
Lee, Bu Sung
SOFT COMPUTING, 2007, 11 (10) : 957 - 971
[35] An Iterative Two-Stage Multifidelity Optimization Algorithm for Computationally Expensive Problems
Kenny, Angus
Ray, Tapabrata
Singh, Hemant Kumar
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2023, 27 (03) : 520 - 534
[36] Memetic algorithm using multi-surrogates for computationally expensive optimization problems
Zongzhao Zhou
Yew Soon Ong
Meng Hiot Lim
Bu Sung Lee
Soft Computing, 2007, 11 : 957 - 971
[37] An optimization algorithm combining local exploitation and global exploration for computationally expensive problems
Ye P.
Pan G.
Soft Computing, 2024, 28 (13-14) : 7841 - 7860
[38] Integrating ε-dominance and RBF surrogate optimization for solving computationally expensive many-objective optimization problems
Wang, Wenyu
Akhtar, Taimoor
Shoemaker, Christine A.
JOURNAL OF GLOBAL OPTIMIZATION, 2022, 82 (04) : 965 - 992
[39] Hybrid Differential Evolution - Particle Swarm Optimization Algorithm for Solving Global Optimization Problems
Pant, Millie
Thangaraj, Radha
Grosan, Crina
Abraham, Ajith
2008 THIRD INTERNATIONAL CONFERENCE ON DIGITAL INFORMATION MANAGEMENT, VOLS 1 AND 2, 2008, : 19 - +
[40] A Surrogate-Assisted Differential Evolution Algorithm for High-Dimensional Expensive Optimization Problems
Wang, Weizhong
Liu, Hai-Lin
Tan, Kay Chen
IEEE TRANSACTIONS ON CYBERNETICS, 2023, 53 (04) : 2685 - 2697

← 1 2 3 4 5 →