Abstract
In general, the existing evolutionary algorithms are prone to premature convergence and slow convergence in coping with combinatorial optimization problems. So an intelligent optimization algorithm called physarum-energy optimization algorithm (PEO) is proposed and put TSP as the carrier in this paper. This algorithm consists of four parts: the physarum biological model, the energy model, the age factor model and the stochastic disturbance model. First, the high parallelism of PEO is enlightened from the physarum’s low complexity and high parallelism. Second, we present an energy mechanism model in PEO, which is mainly to develop the shortcomings of existing algorithm, such as slow convergence and lack of interaction capability. Third, inspired by the characteristic of ants’ spatiotemporal variations, the age factor mechanism is introduced to raise search capacity, which can control the convergence speed and precision ability of PEO. In addition, in order to avoid premature convergence, the stochastic disturbance mechanism is adopted into PEO. And also the feasibility and convergence of PEO has been analyzed and verified theoretically. Moreover, we compare the algorithm and other algorithms to TSPs of diverse scope. The experiment results show that PEO has the advantages of excellent global optimization, high optimization accuracy and high parallelism and is significantly better than other algorithms.
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References
Booker LB, Goldberg DE, Holland JH (1989) Classifier systems and genetic algorithms. Artif Intell 40(1–3):235–282
Chen W-N, Zhang J, Chung HSH et al (2010) A novel set-based particle swarm optimization method for discrete optimization problems. IEEE Trans Evol Comput 14(2):278–300
Das S, Suganthan PN (2011) Differential evolution: a survey of the state-of-the-art. IEEE Trans Evol Comput 15(1):4–31
Dorigo M, Gambardella LM (1997) Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Trans Evol Comput 1(1):53–66
Duan Y, Ying S (2009) A particle swarm optimization algorithm with ant search for solving traveling salesman problem. In: International conference on computational intelligence and security, 2009. CIS’09, vol 2. IEEE, pp 137–141
Feng X, Yang T, Yu H (2016) A new multi-colony fairness algorithm for feature selection. Soft Comput. doi:10.1007/s00500-016-2257-0
Goldberg DE, Holland JH (1988) Genetic algorithms and machine learning. Mach Learn 3(2):95–99
Han K-H, Kim J-H (2002) Quantum-inspired evolutionary algorithm for a class of combinatorial optimization. IEEE Trans Evol Comput 6(6):580–593
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, 1995, vol 4. IEEE, pp 1942–1948
Laporte G (1992) The traveling salesman problem: an overview of exact and approximate algorithms. Eur J Oper Res 59(2):231–247
Li K, Kang L, Zhang W, et al. (2008) Comparative analysis of genetic algorithm and ant colony algorithm on solving traveling salesman problem. In: IEEE international workshop on semantic computing and systems, 2008. WSCS’08. IEEE, pp 72–75
Liang JJ, Kai Qin A, Suganthan PN et al (2006) Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE Trans Evol Comput 10(3):281–295
Liu L, Song Y, Zhang H et al (2015) Physarum optimization: a biology-inspired algorithm for the steiner tree problem in networks. IEEE Trans Comput 64(3):818–831
Mavrovouniotis M, Yang S (2011) A memetic ant colony optimization algorithm for the dynamic travelling salesman problem. Soft Comput 15(7):1405–1425
Mersch DP, Crespi A, Keller L (2013) Tracking individuals shows spatial fidelity is a key regulator of ant social organization. Science 340(6136):1090–1093
Nakagaki T, Yamada H, Tóth Á (2000) Intelligence: maze-solving by an amoeboid organism. Nature 407(6803):470–470
Nguyen HD, Yoshihara I, Yamamori K et al (2007) Implementation of an effective hybrid ga for large-scale traveling salesman problems. IEEE Trans Syst Man Cybern Part B (Cybern) 37(1):92–99
Prügel-Bennett A, Tayarani-Najaran M-H (2012) Maximum satisfiability: anatomy of the fitness landscape for a hard combinatorial optimization problem. IEEE Trans Evol Comput 16(3):319–338
Shuang B, Chen J, Li Z (2011) Study on hybrid ps-aco algorithm. Appl Intell 34(1):64–73
Simopoulos DN, Kavatza SD, Vournas CD (2006) Unit commitment by an enhanced simulated annealing algorithm. IEEE Trans Power Syst 21(1):68–76
Song Y, Liu L, Ma H et al (2014) A biology-based algorithm to minimal exposure problem of wireless sensor networks. IEEE Trans Netw Serv Manag 11(3):417–430
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359
Tayarani-Najaran M-H, Prügel-Bennett A (2015a) Anatomy of the fitness landscape for dense graph-colouring problem. Swarm Evol Comput 22:47–65
Tayarani-Najaran M-H, Prügel-Bennett A (2015b) Quadratic assignment problem: a landscape analysis. Evol Intell 8(4):165–184
Tayarani-Najaran M-H, Prügel-Bennett A (2016) An analysis of the fitness landscape of travelling salesman problem. Evol Comput 24(2):347–384
Tayarani-Najaran M-H, Yao X, Xu H (2015) Meta-heuristic algorithms in car engine design: a literature survey. IEEE Trans Evol Comput 19(5):609–629
Tero A, Takagi S, Saigusa T et al (2010) Rules for biologically inspired adaptive network design. Science 327(5964):439–442
Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1):67–82
Zhang J, Xiong W (2009) An improved particle swarm optimization algorithm and its application for solving traveling salesman problem. In: 2009 WRI world congress on computer science and information engineering, vol 4. IEEE, pp 612–616
Acknowledgements
This work was supported in part by the National Natural Science Foundation of China under Grant Nos. 61472139 and 61462073, the Information Development Special Funds of Shanghai Economic and Information Commission under Grant No. 201602008, the Open Funds of Shanghai Smart City Collaborative Innovation Center.
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Feng, X., Liu, Y., Yu, H. et al. Physarum-energy optimization algorithm. Soft Comput 23, 871–888 (2019). https://doi.org/10.1007/s00500-017-2796-z
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DOI: https://doi.org/10.1007/s00500-017-2796-z