Multifactorial Evolutionary Algorithm For Clustered Minimum Routing Cost Problem

Minimum Routing Cost Clustered Tree Problem (CluMRCT) is applied in various fields in both theory and application. Because the CluMRCT is NP-Hard, the approximate approaches are suitable to find the solution for this problem. Recently, Multifactorial Evolutionary Algorithm (MFEA) has emerged as one of the most efficient approximation algorithms to deal with many different kinds of problems. Therefore, this paper studies to apply MFEA for solving CluMRCT problems. In the proposed MFEA, we focus on crossover and mutation operators which create a valid solution of CluMRCT problem in two levels: first level constructs spanning trees for graphs in clusters while the second level builds a spanning tree for connecting among clusters. To reduce the consuming resources, we will also introduce a new method of calculating the cost of CluMRCT solution. The proposed algorithm is experimented on numerous types of datasets. The experimental results demonstrate the effectiveness of the proposed algorithm, partially on large instances.

[1]  Huynh Thi Thanh Binh,et al.  An Effective Representation Scheme in Multifactorial Evolutionary Algorithm for Solving Cluster Shortest-Path Tree Problem , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[2]  Huynh Thi Thanh Binh,et al.  Effective Multifactorial Evolutionary Algorithm for Solving the Cluster Shortest Path Tree Problem , 2018, CEC.

[3]  Huynh Thi Thanh Binh,et al.  New Mechanism of Combination Crossover Operators in Genetic Algorithm for Solving the Traveling Salesman Problem , 2020, KSE.

[4]  Nguyen Xuan Hoai,et al.  A new hybrid Genetic Algorithm for solving the Bounded Diameter Minimum Spanning Tree problem , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[5]  Yew-Soon Ong,et al.  Multifactorial Evolution: Toward Evolutionary Multitasking , 2016, IEEE Transactions on Evolutionary Computation.

[6]  Luiz Satoru Ochi,et al.  GRASP with path relinking for the symmetric Euclidean clustered traveling salesman problem , 2013, Comput. Oper. Res..

[7]  Bang Ye Wu,et al.  On the minimum routing cost clustered tree problem , 2017, J. Comb. Optim..

[8]  Yew-Soon Ong,et al.  Genetic transfer or population diversification? Deciphering the secret ingredients of evolutionary multitask optimization , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

[9]  Lei Zhou,et al.  Evolutionary multitasking in combinatorial search spaces: A case study in capacitated vehicle routing problem , 2016, 2016 IEEE Symposium Series on Computational Intelligence (SSCI).

[10]  Huynh Thi Thanh Binh,et al.  New approach to solving the clustered shortest-path tree problem based on reducing the search space of evolutionary algorithm , 2019, Knowl. Based Syst..

[11]  Huynh Thi Thanh Binh,et al.  A Heuristic Based on Randomized Greedy Algorithms for the Clustered Shortest-Path Tree Problem , 2019, 2019 IEEE Congress on Evolutionary Computation (CEC).

[12]  Adriano Masone,et al.  The Minimum Routing Cost Tree Problem , 2018, Soft Comput..

[13]  Thi Thanh Binh Huynh,et al.  An effective combination of Genetic Algorithms and the Variable Neighborhood Search for solving Travelling Salesman Problem , 2015, 2015 Conference on Technologies and Applications of Artificial Intelligence (TAAI).

[14]  Mattia D'Emidio,et al.  Hardness, approximability, and fixed-parameter tractability of the clustered shortest-path tree problem , 2018, J. Comb. Optim..

[15]  Bang Ye Wu.  On the Clustered Steiner Tree Problem , 2013, COCOA.

[16]  Yew-Soon Ong,et al.  Linearized domain adaptation in evolutionary multitasking , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[17]  Huynh Thi Thanh Binh,et al.  A survey on hybridizing genetic algorithm with dynamic programming for solving the traveling salesman problem , 2013, 2013 International Conference on Soft Computing and Pattern Recognition (SoCPaR).

[18]  Abhishek Gupta,et al.  Multifactorial Evolutionary Algorithm With Online Transfer Parameter Estimation: MFEA-II , 2020, IEEE Transactions on Evolutionary Computation.

[19]  Yew-Soon Ong,et al.  Landscape synergy in evolutionary multitasking , 2016, 2016 IEEE Congress on Evolutionary Computation (CEC).

[20]  Hua Xu,et al.  Evolutionary multitasking in permutation-based combinatorial optimization problems: Realization with TSP, QAP, LOP, and JSP , 2016, 2016 IEEE Region 10 Conference (TENCON).

[21]  Huynh Thi Thanh Binh,et al.  Heuristic Algorithms for Solving Bounded Diameter Minimum Spanning Tree Problem and Its Application to Genetic Algorithm Development , 2008 .

[22]  Liang Feng,et al.  An Adaptive Archive-Based Evolutionary Framework for Many-Task Optimization , 2020, IEEE Transactions on Emerging Topics in Computational Intelligence.