Hybridizations of GRASP with path relinking for the far from most string problem-Reference-Cited by-同舟云学术

Hybridizations of GRASP with path relinking for the far from most string problem

Published:2015-04-24 Issue:3 Volume:23 Page:481-506
ISSN:0969-6016
Container-title:International Transactions in Operational Research
language:en
Short-container-title:Intl. Trans. in Op. Res.

Author:

Ferone Daniele¹,Festa Paola¹,Resende Mauricio G.C.²

Affiliation:

1. Department of Mathematics and Applications “R. Caccioppoli,”; University of Napoli FEDERICO II; Compl. MSA, Via Cintia 80126 Napoli Italy

2. Algorithms and Optimization Research Department; AT&T Labs Research; 180 Park Avenue, Room C241 Florham Park NJ 07932 USA

Publisher

Wiley

Subject

Management of Technology and Innovation,Management Science and Operations Research,Strategy and Management,Computer Science Applications,Business and International Management

Reference37 articles.

1. Probability distribution of solution time in grasp: an experimental investigation;Aiex;Journal of Heuristics,2002

2. Local search with perturbations for the prize-collecting Steiner tree problem in graphs;Canuto;Networks,2001

3. Statistical analysis of computational tests of algorithms and heuristics;Coffin;INFORMS Journal on Computing,2000

4. Solving a bus driver scheduling problem with randomized multistart heuristics;Leone;International Transactions in Operational Research,2011

Cited by 15 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Extension of CMSA with a Learning Mechanism: Application to the Far from Most String Problem;International Journal of Computational Intelligence Systems;2024-04-29

2. Adding Learning to CMSA;Computational Intelligence Methods and Applications;2024

3. Application of Negative Learning Ant Colony Optimization to the Far from Most String Problem;Evolutionary Computation in Combinatorial Optimization;2023

4. On the role of metaheuristic optimization in bioinformatics;International Transactions in Operational Research;2022-06-16

5. A Visual Grasping Strategy for Improving Assembly Efficiency Based on Deep Reinforcement Learning;Journal of Sensors;2021-07-23