A Multi-Objective Imperialist Competitive Algorithm to Solve a New Multi-Modal Tree Hub Location Problem

被引:0
|
作者
Tavakkoli-Moghaddam, Reza [1 ,2 ]
Sedehzadeh, Samaneh [3 ]
机构
[1] Univ Tehran, Sch Ind Engn, Tehran, Iran
[2] Univ Tehran, Res Inst Energy Management & Planning, Coll Engn, Tehran, Iran
[3] Islamic Azad Univ, South Tehran Branch, Sch Ind Engn, Tehran, Iran
关键词
tree hub location; transportation mode; multi-objective optimization; imperialist competitive algorithm;
D O I
暂无
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
A hub location problem is a main group of the transportation network, which is utilized as a connecting and switching point for demand between origins and destinations. Recently, a tree hub location problem has been introduced as an incomplete hub network with single assignment, in which hubs are connected by means of a tree. This paper presents a new bi-objective, multi-modal tree hub location problem with different capacity levels. Besides the location and allocation decisions in tree hub network, this model decides on transportation modes and capacity levels such that the total transportation cost and time are minimized. Additionally, a multi-objective imperialist competitive algorithm (MOICA) is proposed to solve the presented model and obtain Pareto-optimal solutions of the given problem. Finally, the performance of this algorithm is compared with a non-dominated sorting genetic algorithm (NSGA-II).
引用
收藏
页码:202 / 207
页数:6
相关论文
共 50 条
  • [21] A Multi-modal Multi-objective Evolutionary Algorithm Based on Multi-criteria Grouping
    Wang, Xiaoxiong
    Zhang, Guochen
    Sun, Chaoli
    Wang, Hao
    Zhao, Kaili
    ADVANCES IN SWARM INTELLIGENCE, PT I, ICSI 2024, 2024, 14788 : 265 - 276
  • [22] A Decomposition-based Multi-modal Multi-objective Evolutionary Algorithm with Problem Transformation into Two-objective Subproblems
    Nojima, Yusuke
    Fujii, Yuto
    Masuyama, Naoki
    Liu, Yiping
    Ishibuchi, Hisao
    PROCEEDINGS OF THE 2023 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2023 COMPANION, 2023, : 399 - 402
  • [23] Multi-Modal Multi-Objective Traveling Salesman Problem and its Evolutionary Optimizer
    Liu, Yiping
    Xu, Liting
    Han, Yuyan
    Masuyama, Naoki
    Nojima, Yusuke
    Ishibuchi, Hisao
    Yen, Gary G.
    2021 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC), 2021, : 770 - 777
  • [24] Optimising Multi-Modal Polynomial Mutation Operators for Multi-Objective Problem Classes
    McClymont, Kent
    Keedwell, Ed
    2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2010,
  • [25] A Decomposition-Based Evolutionary Algorithm for Multi-modal Multi-objective Optimization
    Tanabe, Ryoji
    Ishibuchi, Hisao
    PARALLEL PROBLEM SOLVING FROM NATURE - PPSN XV, PT I, 2018, 11101 : 249 - 261
  • [26] A multi-modal multi-objective evolutionary algorithm based on scaled niche distance
    Cao, Jie
    Qi, Zhi
    Chen, Zuohan
    Zhang, Jianlin
    APPLIED SOFT COMPUTING, 2024, 152
  • [27] A hierarchical clustering algorithm for addressing multi-modal multi-objective optimization problems
    Gu, Qinghua
    Niu, Yiwen
    Hui, Zegang
    Wang, Qian
    Xiong, Naixue
    EXPERT SYSTEMS WITH APPLICATIONS, 2025, 264
  • [28] On the Potential of Automated Algorithm Configuration on Multi-Modal Multi-Objective Optimization Problems
    Rook, Jeroen
    Trautmann, Heike
    Bossek, Jakob
    Grimme, Christian
    PROCEEDINGS OF THE 2022 GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE COMPANION, GECCO 2022, 2022, : 356 - 359
  • [29] Dynamic Multi-modal Multi-objective Evolutionary Optimization Algorithm Based on Decomposition
    Xu, Biao
    Chen, Yang
    Li, Ke
    Fan, Zhun
    Gong, Dunwei
    Bao, Lin
    ADVANCES IN SWARM INTELLIGENCE, ICSI 2023, PT I, 2023, 13968 : 383 - 389
  • [30] An improved multi-objective imperialist competitive algorithm for surgical case scheduling problem with switching and preparation times
    Yu, Hui
    Li, Jun-qing
    Chen, Xiao-long
    Niu, Wei
    Sang, Hong-yan
    CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS, 2022, 25 (05): : 3591 - 3616