Anthropic Principle Algorithm:A new Heuristic Optimization Meth

Elder Oroski (Universidade Tecnológica Federal do Paraná (UTFPR), Curitiba, Brazil)
Beatriz S. Pês (Instituto Federal do Paraná (IFPR),Campo Largo, Brazil)
Rafael H. Lopez (Universidade Federal de Santa Catarina (UFSC),Florianópolis, Brazil)
Adolfo Bauchspiess (Universidade de Brasília (UnB), Brasília, Brazil)

Article ID: 353



Heuristic optimization is an appealing method for solving some en- gineering problems, in which gradient information may not be available, or yet, when the problem presents many minima points. Thus, the goal of this paper is to present a new heuristic algorithm based on the Anthropic Prin- ciple, the Anthropic Principle Algorithm (APA). This algorithm is based on the following idea: the universe developed itself in the exact way to allow the existence of all current things, including life. This idea is very similar to the convergence in an optimization process. Arguing about the merit of the An- thropic Principle is not among the goals of this paper. This principle is treated only as an inspiration for heuristic optimization algorithms. In the final of the paper, some applications of the APA are presented. Classical problems such as Rosenbrock function minimization, system identification examples and min- imization of some benchmark functions are also presented. In order to vali- date the APA’s functionality, a comparison between the APA and the classic heuristic algorithms, Genetic Algorithm (GA) and Particle Swarm Optimiza- tion (PSO) is made. In this comparison, the APA presented better results in majority of tested cases, proving that it has a great potential for application in optimization problems.


Heuristic; Optimization; Anthropic; Principle; System; Identifica- tion

Full Text:



[1] NOCEDAL, Jorge and STEPHEN, Wright. Numerical Optimization. Springer Publisher,

[2] st edition, 1999.

[3] BOYD, Stephen. Convex Optimization. Cambridge University Press, 7th edition, 2007.

[4] MOCKUS, Jonas; EDDY, William and REKLAITIS, Gintaras. Bayesian Heuristic Approach to Discrete and Global Optimization: Algorithms, Visualization, Software and Applications. Kluwer Academic Publishers, 1st edition, 1997.

[5] KOZA, John. Genetic Programming: On the Programming of Computers by Means of

[6] Natural Selection. MIT Press, 6th edition, 1998.

[7] SOLNON, Christine. Ant Colony Optimization and Constraint Programming. Wiley

[8] Press, 1st edition, 2010.

[9] OLSSON, Andrea E. Particle Swarm: Theory, Techniques and Applications. Nova Science

[10] Publishers, 1st edition, 2011.

[11] ATASHPAZ, Esmaeil and LUCAS, Caro. Imperialist Competitive Algorithm: An Algorithm for Optimization Inspired by Imperialistic Competition. IEEE Congress on Evolutionary Computation, page 4661-4667. IEEE, 2007.

[12] HE, S. and WU, K. H. and SAUNDERS, J. R. Group Search Optimizer: An optimization

[13] Algorithm inspired by animal Searching Behavior. IEEE Transactions on Evolutionary

[14] Computation, Vol. 13, no 5, 2009.

[15] SIMON, Dan. Biogeography-Based Optimization. IEEE Transactions on Evolutionary

[16] Computation, Vol. 12, no 6, 2008.

[17] HAWKING, Stephen and MLODINOW, Leonard, The Great Project, Nova Fronteira

[18] Print, 1st edition, 2012.

[19] CARTER, B. Confrontation of Cosmological Theories with Observational Data. IAU

[20] Symposium no 63, Krakow, 1973.

[21] BARROW, J. D. and TIPLER, F. J. The Anthropic Cosmological Principle. Oxford

[22] Univ. Press. Oxford. 1986.

[23] COMITTI, V. S., Princ´ıpio Antr´opico Cosmol´ogico, Revista Brasileira de Ensino da

[24] F´ısica, volume: 33, no 1, 1504, 2011.

[25] KALLOSH, Renata and LINDE, Andrei. M theory, Cosmological Constant and Anthropic Principle. Physical Review D 67, 023510, 2003.

[26] STARKMAN, Glenn D. and TROTTA, Roberto. Why Anthropic Principle Cannot

[27] Predict α. Physical Review Letters, PRL 97, 201301. 2006.

[28] Rosenbrock, H. H. An automatic method for finding the greatest or least value of a

[29] function. The Computer Journal 3, pp. 175 − 184. 1960.

[30] RAVAL, Falguni and MAKWANA, Jagruti. Optimization of Resonance Frequency of

[31] Circular Patch Antenna at 5 GHz Using Particle Swarm Optimization. International Journal of Advances in Engineering and Technology, Vol. 01, pp. 99-106, 2011.

[32] LJUNG, Lennart. System Identification, Theory for the User. 2nd edition. Prentice Hall.


[34] OROSKI, E. and HOLDORF R. and BAUCHSPIESS, A. Nonlinear Buck Circuit Identification Using Orthonormal Functions with Heuristic Optimization. XX Congresso Brasileiro de Autom´atica, pp. 804-811, 2014.

[35] ROSA, Alex and CAMPELLO, Ricardo and AMARAL, Wagner. Exact Search Directions for Optimizations of Linear and Nonlinear Models Based on Generalized Orthonormal Functions. IEEE Transations on Automatic Control, vol. 54, No 12, pp. 2757-2772,



  • There are currently no refbacks.
Copyright © 2018 Elder Oroski, Beatriz S. Pês, Rafael H. Lopez, Adolfo Bauchspiess

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.