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Arabian Journal for Science and Engineering

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10-08-2021 | Research Article-Computer Engineering and Computer Science

Multi-Colony Ant Optimization Based on Pheromone Fusion Mechanism of Cooperative Game

Authors: Yadong Mo, Xiaoming You, Sheng Liu

Published in: Arabian Journal for Science and Engineering | Issue 2/2022

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Abstract

In this paper, we propose a multi-colony ant optimization based on pheromone fusion mechanism of cooperative game (CGMACO) to balance the convergence speed and diversity of the algorithm. Firstly, the heterogeneous multi-colony is composed of ant colony system (ACS) and Max–Min ant system (MMAS), and these two classical colonies coordinate together to improve the solution quality. Secondly, the cooperative game model determines which sub-colonies can interact with each other based on evaluating each union’s payoff, while the pheromone fusion mechanism decides what information can exchange by regulating the pheromone matrix of each subpopulation. Those two methods can greatly diversify the solution of algorithm. In addition, the information entropy is also introduced to control the interaction frequency, which enhances the adaptability of the algorithm. Finally, the experimental results of the large-scale TSP instances show that the improved algorithm can improve the accuracy of the solution without affecting the convergence speed and better than the existing intelligent algorithms.

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Liu, F.; Zeng, G.: Study of genetic algorithm with reinforcement learning to solve the TSP. Expert Syst. Appl. 36, 6995–7001 (2009). https://doi.org/10.1016/j.eswa.2008.08.026CrossRef

Mahi, M.; Baykan, Ö.K.; Kodaz, H.: A new hybrid method based on particle swarm optimization, ant colony optimization and 3-opt algorithms for traveling salesman problem. Appl. Soft Comput. J. 30, 484–490 (2015). https://doi.org/10.1016/j.asoc.2015.01.068CrossRef

Panwar, K.; Deep, K.: Discrete grey wolf optimizer for symmetric travelling salesman problem. Appl. Soft Comput. 105, 107298 (2021). https://doi.org/10.1016/j.asoc.2021.107298CrossRef

Dorigo, M.; Maniezzo, V.; Colorni, A.: Ant system: optimization by a colony of cooperating agents. IEEE Trans. Syst. Man, Cybern. Part B 26, 29–41 (1996). https://doi.org/10.1109/3477.484436CrossRef

Dorigo, M.; Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans. Evol. Comput. 1, 53–66 (1997). https://doi.org/10.1109/4235.585892CrossRef

Stützle, T.: Hoos HH (2000) MAX–MIN ant system. Futur. Gener. Comput. Syst. 16, 889–914 (2000). https://doi.org/10.1016/S0167-739X(00)00043-1CrossRef

Sangeetha, V.; Krishankumar, R.; Ravichandran, K.S.; Kar, S.: Energy-efficient green ant colony optimization for path planning in dynamic 3D environments. Soft Comput. 25, 4749–4769 (2021). https://doi.org/10.1007/s00500-020-05483-6CrossRef

Ye, K.; Zhang, C.; Ning, J.; Liu, X.: Ant-colony algorithm with a strengthened negative-feedback mechanism for constraint-satisfaction problems. Inf. Sci. (Ny) 406, 29–41 (2017). https://doi.org/10.1016/j.ins.2017.04.016CrossRef

Ning, J.; Zhang, Q.; Zhang, C.; Zhang, B.: A best-path-updating information-guided ant colony optimization algorithm. Inf. Sci. (Ny) 433, 142–162 (2018). https://doi.org/10.1016/j.ins.2017.12.047MathSciNetCrossRef

10.

Tseng, H.E.; Chang, C.C.; Lee, S.C.; Huang, Y.M.: Hybrid bidirectional ant colony optimization (hybrid BACO): an algorithm for disassembly sequence planning. Eng. Appl. Artif. Intell. 83, 45–56 (2019). https://doi.org/10.1016/j.engappai.2019.04.015CrossRef

11.

Olivas, F.; Valdez, F.; Castillo, O.; Gonzalez, C.I.; Martinez, G.; Melin, P.: Ant colony optimization with dynamic parameter adaptation based on interval type-2 fuzzy logic systems. Appl. Soft Comput. J. 53, 74–87 (2017). https://doi.org/10.1016/j.asoc.2016.12.015CrossRef

12.

Tuani, A.F.; Keedwell, E.; Collett, M.: Heterogenous adaptive ant colony optimization with 3-opt local search for the travelling salesman problem. Appl. Soft Comput. 97, 106720 (2020). https://doi.org/10.1016/j.asoc.2020.106720CrossRef

13.

Miao, C.; Chen, G.; Yan, C.; Wu, Y.: Path planning optimization of indoor mobile robot based on adaptive ant colony algorithm. Comput. Ind. Eng. 156, 107230 (2021). https://doi.org/10.1016/j.cie.2021.107230CrossRef

14.

Chatterjee, S.; Das, S.: Ant colony optimization based enhanced dynamic source routing algorithm for mobile Ad-hoc network. Inf. Sci. (Ny) 295, 67–90 (2015). https://doi.org/10.1016/j.ins.2014.09.039MathSciNetCrossRef

15.

Raveendra, K.; Vinothkanna, R.: Hybrid ant colony optimization model for image retrieval using scale-invariant feature transform local descriptor. Comput. Electr. Eng. 74, 281–291 (2019). https://doi.org/10.1016/j.compeleceng.2019.02.006CrossRef

16.

Wang, Y.; Wang, L.; Chen, G.; Cai, Z.; Zhou, Y.; Xing, L.: An improved ant colony optimization algorithm to the periodic vehicle routing problem with time window and service choice. Swarm Evol. Comput. (2020). https://doi.org/10.1016/j.swevo.2020.100675CrossRef

17.

Hong, T.P.; Tung, Y.F.; Wang, S.L.; Wu, Y.L.; Wu, M.T.: A multi-level ant-colony mining algorithm for membership functions. Inf. Sci. (Ny) 182, 3–14 (2012). https://doi.org/10.1016/j.ins.2010.12.019MathSciNetCrossRef

18.

Gambardella, L.M.: MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows. New Ideas Optim. (1999)

19.

Chu, S.-C.; Roddick, J.F.; Pan, J.-S.: Ant colony system with communication strategies. Inf. Sci. (Ny) 167, 63–76 (2004). https://doi.org/10.1016/j.ins.2003.10.013MathSciNetCrossRefMATH

20.

Twomey, C.; Stützle, T.; Dorigo, M.; Manfrin, M.; Birattari, M.: An analysis of communication policies for homogeneous multi-colony ACO algorithms. Inf. Sci. (Ny) 180, 2390–2404 (2010). https://doi.org/10.1016/j.ins.2010.02.017CrossRef

21.

Dong, G.; Guo, W.W.; Tickle, K.: Solving the traveling salesman problem using cooperative genetic ant systems. Expert Syst. Appl. 39, 5006–5011 (2012). https://doi.org/10.1016/j.eswa.2011.10.012CrossRef

22.

Zhang, D.; You, X.; Liu, S.; Yang, K.: Multi-colony ant colony optimization based on generalized jaccard similarity recommendation strategy. IEEE Access. 7, 157303–157317 (2019). https://doi.org/10.1109/ACCESS.2019.2949860CrossRef

23.

Wang, Y.; Wang, L.; Peng, Z.; Chen, G.; Cai, Z.; Xing, L.: A multi ant system based hybrid heuristic algorithm for vehicle routing problem with service time customization. Swarm Evol. Comput. 50, 100563 (2019). https://doi.org/10.1016/j.swevo.2019.100563CrossRef

24.

Yang, K.; You, X.; Liu, S.; Pan, H.: A novel ant colony optimization based on game for traveling salesman problem. Appl. Intell. 50, 4529–4542 (2020). https://doi.org/10.1007/s10489-020-01799-wCrossRef

25.

Li, S.; You, X.; Liu, S.: Multiple ant colony optimization using both novel LSTM network and adaptive Tanimoto communication strategy. Appl. Intell. (2021). https://doi.org/10.1007/s10489-020-02099-zCrossRef

26.

Deng, Y.: Uncertainty measure in evidence theory. Sci. China Inf. Sci. 63, 1–19 (2020). https://doi.org/10.1007/s11432-020-3006-9MathSciNetCrossRef

27.

Deng, Y.: Information volume of mass function. Int. J. Comput. Commun. Control (2020). https://doi.org/10.15837/ijccc.2020.6.3983CrossRef

28.

Xue, Y.; Deng, Y.: Tsallis eXtropy. Commun. Stat - Theory Methods (2021). https://doi.org/10.1080/03610926.2021.1921804CrossRef

29.

Zhao, J.; Liang, J.M.; Dong, Z.N.; Tang, D.Y.; Liu, Z.: Accelerating information entropy-based feature selection using rough set theory with classified nested equivalence classes. Pattern Recognit. 107, 107517 (2020). https://doi.org/10.1016/j.patcog.2020.107517CrossRef

30.

Sabirov, D.S.: Information entropy of mixing molecules and its application to molecular ensembles and chemical reactions. Comput. Theor. Chem. 1187, 112933 (2020). https://doi.org/10.1016/j.comptc.2020.112933CrossRef

31.

Zhu, H.; Wang, Y.; Du, C.; Zhang, Q.; Wang, W.: A novel odor source localization system based on particle filtering and information entropy. Rob. Auton. Syst. 132, 103619 (2020). https://doi.org/10.1016/j.robot.2020.103619CrossRef

32.

Saji, Y.; Barkatou, M.: A discrete bat algorithm based on Lévy flights for Euclidean traveling salesman problem. Expert Syst. Appl. 172, 114639 (2021). https://doi.org/10.1016/j.eswa.2021.114639CrossRef

33.

Akhand, M.A.H.; Ayon, S.I.; Shahriyar, S.A.; Siddique, N.; Adeli, H.: Discrete spider monkey optimization for travelling salesman problem. Appl. Soft Comput. J. 86, 105887 (2020). https://doi.org/10.1016/j.asoc.2019.105887CrossRef

34.

Gülcü, Ş; Mahi, M.; Baykan, Ö.K.; Kodaz, H.: A parallel cooperative hybrid method based on ant colony optimization and 3-Opt algorithm for solving traveling salesman problem. Soft Comput. 22, 1669–1685 (2018). https://doi.org/10.1007/s00500-016-2432-3CrossRef

35.

Ebadinezhad, S.: DEACO: adopting dynamic evaporation strategy to enhance ACO algorithm for the traveling salesman problem. Eng. Appl. Artif. Intell. 92, 103649 (2020). https://doi.org/10.1016/j.engappai.2020.103649CrossRef

36.

Yong, W.: Hybrid Max-Min ant system with four vertices and three lines inequality for traveling salesman problem. Soft Comput. 19, 585–596 (2015). https://doi.org/10.1007/s00500-014-1279-8CrossRef

37.

Osaba, E.; Ser, J.D.; Sadollah, A.; Bilbao, M.N.; Camacho, D.: A discrete water cycle algorithm for solving the symmetric and asymmetric traveling salesman problem. Appl. Soft Comput. J. 71, 277–290 (2018). https://doi.org/10.1016/j.asoc.2018.06.047CrossRef

38.

Khan, I.; Maiti, M.K.: A swap sequence based artificial bee colony algorithm for traveling salesman problem. Swarm Evol. Comput. 44, 428–438 (2019). https://doi.org/10.1016/j.swevo.2018.05.006CrossRef

39.

Ezugwu, A.E.S.; Adewumi, A.O.: Discrete symbiotic organisms search algorithm for travelling salesman problem. Expert Syst. Appl. 87, 70–78 (2017). https://doi.org/10.1016/j.eswa.2017.06.007CrossRef

40.

Osaba, E.; Yang, X.S.; Diaz, F.; Lopez-Garcia, P.; Carballedo, R.: An improved discrete bat algorithm for symmetric and asymmetric traveling salesman problems. Eng. Appl. Artif. Intell. 48, 59–71 (2016). https://doi.org/10.1016/j.engappai.2015.10.006CrossRef

Title: Multi-Colony Ant Optimization Based on Pheromone Fusion Mechanism of Cooperative Game
Authors: Yadong Mo
Xiaoming You
Sheng Liu
Publication date: 10-08-2021
Publisher: Springer Berlin Heidelberg
Published in: Arabian Journal for Science and Engineering / Issue 2/2022
Print ISSN: 2193-567X
Electronic ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-021-06033-4

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