Abstract
Population topology of particle swarm optimization (PSO) has an important impact on solving performance of PSO. The more commonly used population topology is with static structure, such as fully connected structure and ring structure. In the process of evolution, the static population topology is always the same, which affects the information exchange between individuals of the population to a certain extent. In this paper, several feasible dynamic random population topologies are proposed based on the study of random population topology. In the PSO algorithm with dynamic random population topology, the neighborhood particles of a particle will evolve according to certain rules. In detail, a population topology is abstracted into an undirected connected graph which could be randomly generated according to predefined rule and degree. By tuning the rule and degree, the communication mechanisms evolve in the evolutionary process and the solving performance of PSO will be enhanced significantly. Furthermore, for the generalized portfolio selection model in the financial engineering field, the proposed several PSO algorithms are employed to solve the problems related to the generalized portfolio selection model, and the performance of them have been compared with the classic PSO variant in detail. The data of experiment is the weekly prices in a certain period which include the indices of HangSeng, DAX 100, FTSE 100, S&P 100 and Nikkei 225. The computational results demonstrate that the proposed dynamic random population topology could obviously improve the performance of PSO. It is especially worth noting that one proposed dynamic random population topology strategy shows an excellent performance on most data sets which could find good solutions to the generalized portfolio selection problems.
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References
Anione S, Loraschi A, Tettamanzi A (1993) A genetic approach to portfolio selection. Neural Netw World 6(93):597–604
Armananzas R, Lozano JA (2005) A multiobjective approach to the portfolio optimization problem. In: Proceedings of IEEE congress on evolutionary computation (CEC), vol 2, pp 1388–1395
Chang T-J, Meade N, Beasley JE, Sharaiha YM (2000) Heuristics for cardinality constrained portfolio optimisation. Comput Oper Res 27(13):1271–1302
Clerc M (1999) The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation (CEC), vol 3
Clerc M (2007) Back to random topology. Technical Report
Clerc M, Kennedy J (2002) The particle swarm-explosion, stability, and convergence in a multidimensional complex space. IEEE Trans Evol Comput 6(1):58–73
Crama Y, Schyns M (2003) Simulated annealing for complex portfolio selection problems. Eur J Oper Res 150(3):546–571
Cura T (2009) Particle swarm optimization approach to portfolio optimization. Nonlinear Anal: Real World Appl 10(4):2396–2406
Hu Y, Liu K, Zhang X, Su L, Ngai EWT, Liu M (2015) Application of evolutionary computation for rule discovery in stock algorithmic trading: a literature review. Appl Soft Comput 36:534–551
Jobst NJ, Horniman MD, Lucas CA, Mitra G (2001) Computational aspects of alternative portfolio selection models in the presence of discrete asset choice constraints. Quant Finance 1(5):489–501
Kellerer H, Mansini R (2000) Selecting portfolios with fixed costs and minimum transaction lots. Ann Oper Res 99:287–304
Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of IEEE congress on evolutionary computation (CEC), vol 3, pp 1931–1938
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks, vol 4, pp 1942–1948
Konno H, Yamazaki H (1991) Mean-absolute deviation portfolio optimization model and its applications to tokyo stock market. Manag Sci 37(5):519–531
Kulkarni RV, Venayagamoorthy GK (2011) Particle swarm optimization in wireless-sensor networks: a brief survey. IEEE Trans Syst Man Cybern Part C: Appl Rev 41(2):262–267
Renata M, Maria GS (1999) Heuristic algorithms for the portfolio selection problem with minimum transaction lots. Eur J Oper Res 114(2):219–233
Markowitz H (1952) Portfolio selection*. J Finance 7(1):77–91
Mendes R (2004) Population topologies and their influence in particle swarm performance. PhD thesis, Universidade do Minho
Nazemi A, Tahmasbi N (2014) A computational intelligence method for solving a class of portfolio optimization problems. Soft Comput 18(11):2101–2117
Ni Q, Deng J (2013) A new logistic dynamic particle swarm optimization algorithm based on random topology. Sci World J
Ni Q, Cao C, Yin X (2014) A new dynamic probabilistic particle swarm optimization with dynamic random population topology. In: Proceedings of IEEE congress on evolutionary computation (CEC), pp 1321–1327
Ouederni BN, Sullivan WG (1991) A semi-variance model for incorporating risk into capital investment analysis. Eng Econ 36(2):83–106
Ponsich A, Jaimes AL, Coello C et al (2013) A survey on multiobjective evolutionary algorithms for the solution of the portfolio optimization problem and other finance and economics applications. IEEE Trans Evol Comput 17(3):321–344
Shi Y (2004) Particle swarm optimization. IEEE Connect 2(1):8–13
Wang J-b, Chen W-N, Zhang J, Lin Y (2015) A dimension-decreasing particle swarm optimization method for portfolio optimization. In: Proceedings of the companion publication of the 2015 on genetic and evolutionary computation conference, pp. 1515–1516
Yin X, Ni Q, Zhai Y (2015a) A novel pso for portfolio optimization base on heterogeneous multiple population strategy. In: Proceedings of IEEE congress on evolutionary computation (CEC)
Yin X, Ni Q, Zhai Y (2015b) A novel particle swarm optimization for portfolio optimization based on random population topology strategies. In: Proceedings of the 6th international conference on swarm intelligence, Springer, pp. 164–175
Zhu H, Wang Y, Wang K, Chen Y (2011) Particle swarm optimization (pso) for the constrained portfolio optimization problem. Expert Syst Appl 38(8):10161–10169
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This work is supported by National High-Tech Research and Development Program of China (863 Program) (Grant No. 2015AA015904).
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Ni, Q., Yin, X., Tian, K. et al. Particle swarm optimization with dynamic random population topology strategies for a generalized portfolio selection problem. Nat Comput 16, 31–44 (2017). https://doi.org/10.1007/s11047-016-9541-x
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DOI: https://doi.org/10.1007/s11047-016-9541-x