ε-constraint heat transfer search (ε-HTS) algorithm for solving multi-objective engineering design problems

被引:16
|
作者
Tawhid, Mohamed A. [1 ,2 ]
Savsani, Vimal [1 ,3 ]
机构
[1] Thompson Rivers Univ, Dept Math & Stat, Fac Sci, Kamloops, BC V2C 0C8, Canada
[2] Alexandria Univ, Dept Math & Comp Sci, Fac Sci, Moharam Bey 21511, Alexandria, Egypt
[3] Pandit Deendayal Petr Univ, Dept Mech Engn, Gandinagar, Gujarat, India
基金
加拿大自然科学与工程研究理事会;
关键词
Multiobjective optimization; Heat transfer search; Design optimization; Pareto front; MULTI OBJECTIVE OPTIMIZATION; EVOLUTIONARY ALGORITHMS; THERMODYNAMIC ANALYSIS; PARTICLE SWARM; PERFORMANCE; EFFICIENT; EMISSION; SYSTEM; FLOW;
D O I
10.1016/j.jcde.2017.06.003
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
In this paper, an effective epsilon-constraint heat transfer search (e-HTS) algorithm for the multi-objective engineering design problems is presented. This algorithm is developed to solve multi-objective optimization problems by evaluating a set of single objective sub-problems. The effectiveness of the proposed algorithm is checked by implementing it on multi-objective benchmark problems that have various characteristics of Pareto front such as discrete, convex, and non-convex. This algorithm is also tested for several distinctive multi-objective engineering design problems, such as four bar truss problem, gear train problem, multi-plate disc brake design, speed reducer problem, welded beam design, and spring design problem. Moreover, the numerical experimentation shows that the proposed algorithm generates the solution to represent true Pareto front. (C) 2017 Society for Computational Design and Engineering. Publishing Services by Elsevier. This is an open access article under the CC BY-NC-ND license.
引用
收藏
页码:104 / 119
页数:16
相关论文
共 50 条
  • [41] A Hybrid Variable Neighborhood Search Algorithm for Solving Multi-Objective Flexible Job Shop Problems
    Li, Jun-qing
    Pan, Quan-ke
    Xie, Sheng-xian
    COMPUTER SCIENCE AND INFORMATION SYSTEMS, 2010, 7 (04) : 907 - 930
  • [42] A Comprehensive Review on Evolutionary Algorithm Solving Multi-Objective Problems
    Qu, Ying
    Ma, Zheng
    Clausen, Anders
    Jorgensen, Bo Norregaard
    2021 22ND IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT), 2021, : 825 - 831
  • [43] Sharing Mutation Genetic Algorithm for Solving Multi-objective Problems
    Hsieh, Sheng-Ta
    Chiu, Shih-Yuan
    Yen, Shi-Jim
    2011 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2011, : 1833 - 1839
  • [44] Water cycle algorithm for solving multi-objective optimization problems
    Ali Sadollah
    Hadi Eskandar
    Ardeshir Bahreininejad
    Joong Hoon Kim
    Soft Computing, 2015, 19 : 2587 - 2603
  • [46] Solving Multi-Objective Problems Using Bird Swarm Algorithm
    Houssein, Essam H.
    Ahmed, Mohammed M.
    Abd Elaziz, Mohamed
    Ewees, Ahmed A.
    Ghoniem, Rania M.
    IEEE ACCESS, 2021, 9 : 36382 - 36398
  • [47] A collaborative evolutionary algorithm for solving constrained multi-objective problems
    Wang R.
    Gu Q.-H.
    Gu, Qing-Hua (qinghuagu@126.com); Gu, Qing-Hua (qinghuagu@126.com), 1600, Northeast University (36): : 2656 - 2664
  • [48] Water cycle algorithm for solving multi-objective optimization problems
    Sadollah, Ali
    Eskandar, Hadi
    Bahreininejad, Ardeshir
    Kim, Joong Hoon
    SOFT COMPUTING, 2015, 19 (09) : 2587 - 2603
  • [49] An Archive-Based Multi-Objective Arithmetic Optimization Algorithm for Solving Industrial Engineering Problems
    Khodadadi, Nima
    Abualigah, Laith
    El-Kenawy, El-Sayed M.
    Snasel, Vaclav
    Mirjalili, Seyedali
    IEEE ACCESS, 2022, 10 : 106673 - 106698
  • [50] Multi-objective equilibrium optimizer slime mould algorithm and its application in solving engineering problems
    Luo, Qifang
    Yin, Shihong
    Zhou, Guo
    Meng, Weiping
    Zhao, Yixin
    Zhou, Yongquan
    STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2023, 66 (05)