One more look on visualization of operation of a root-finding algorithm

Many algorithms that iteratively find solution of an equation require tuning. Due to the complex dependence of many algorithm’s elements, it is difficult to know their impact on the work of the algorithm. The article presents a simple root-finding algorithm with self-adaptation that requires tuning, similarly to evolutionary algorithms. Moreover, the use of various iteration processes instead of the standard Picard iteration is presented. In the algorithm’s analysis, visualizations of the dynamics were used. The conducted experiments and the discussion regarding their results allow to understand the influence of tuning on the proposed algorithm. The understanding of the tuning mechanisms can be helpful in using other evolutionary algorithms. Moreover, the presented visualizations show intriguing patterns of potential artistic applications.

[1]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[2]  Ireneusz Gosciniak Immune Algorithm in Non-stationary Optimization Task , 2008, 2008 International Conference on Computational Intelligence for Modelling Control & Automation.

[3]  Hiroki Sayama,et al.  Introduction to the Modeling and Analysis of Complex Systems , 2015 .

[4]  O. Granichin,et al.  Projective Approximation Based Gradient Descent Modification , 2017 .

[5]  Elijah Polak,et al.  Optimization: Algorithms and Consistent Approximations , 1997 .

[6]  Peter Richtárik,et al.  Semi-Stochastic Gradient Descent Methods , 2013, Front. Appl. Math. Stat..

[7]  Ireneusz Gosciniak Discussion on semi-immune algorithm behaviour based on fractal analysis , 2017, Soft Comput..

[8]  Hendrik Broer,et al.  Dynamical Systems and Chaos , 2010 .

[9]  Krzysztof Gdawiec,et al.  Polynomiography for the polynomial infinity norm via Kalantari's formula and nonstandard iterations , 2017, Appl. Math. Comput..

[10]  Ajith Abraham,et al.  Inertia Weight strategies in Particle Swarm Optimization , 2011, 2011 Third World Congress on Nature and Biologically Inspired Computing.

[11]  Max A. Viergever,et al.  Adaptive Stochastic Gradient Descent Optimisation for Image Registration , 2009, International Journal of Computer Vision.

[12]  Russell C. Eberhart,et al.  Tracking and optimizing dynamic systems with particle swarms , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[13]  Agnieszka Lisowska,et al.  Polynomiography Based on the Nonstandard Newton-Like Root Finding Methods , 2015 .

[14]  Anirban Sengupta,et al.  Time Varying vs. Fixed Acceleration Coefficient PSO Driven Exploration during High Level Synthesis: Performance and Quality Assessment , 2014, 2014 International Conference on Information Technology.

[15]  A. Kwasinski,et al.  Analysis of Classical Root-Finding Methods Applied to Digital Maximum Power Point Tracking for Sustainable Photovoltaic Energy Generation , 2011, IEEE Transactions on Power Electronics.

[16]  Yuelin Gao,et al.  A Particle Swarm Optimization Algorithm with Logarithm Decreasing Inertia Weight and Chaos Mutation , 2008, 2008 International Conference on Computational Intelligence and Security.

[17]  Agnieszka Lisowska,et al.  Polynomiography via the Hybrids of Gradient Descent and Newton Methods with Mann and Ishikawa Iterations , 2018, WorldCIST.

[18]  Xinbo Huang,et al.  Natural Exponential Inertia Weight Strategy in Particle Swarm Optimization , 2006, 2006 6th World Congress on Intelligent Control and Automation.

[19]  Yuhui Qiu,et al.  A new adaptive well-chosen inertia weight strategy to automatically harmonize global and local search ability in particle swarm optimization , 2006, 2006 1st International Symposium on Systems and Control in Aerospace and Astronautics.

[20]  Convergence to common fixed points by a modified iteration process , 2011 .

[21]  Reza Firsandaya Malik,et al.  New particle swarm optimizer with sigmoid increasing inertia weight , 2007 .

[22]  S.I. Shaheen,et al.  PSOSA: An Optimized Particle Swarm Technique for Solving the Urban Planning Problem , 2006, 2006 International Conference on Computer Engineering and Systems.

[23]  B. Kalantari Polynomial Root-finding and Polynomiography , 2008 .

[24]  Yu Wang,et al.  Adaptive Inertia Weight Particle Swarm Optimization , 2006, ICAISC.

[25]  R. Eberhart,et al.  Fuzzy adaptive particle swarm optimization , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[26]  S. Ishikawa Fixed points by a new iteration method , 1974 .

[27]  Changhe Li,et al.  A Clustering Particle Swarm Optimizer for Locating and Tracking Multiple Optima in Dynamic Environments , 2010, IEEE Transactions on Evolutionary Computation.

[28]  T. Funabashi,et al.  Unit Commitment Computation - A Novel Fuzzy Adaptive Particle Swarm Optimization Approach , 2006, 2006 IEEE PES Power Systems Conference and Exposition.

[29]  Wei Zhang,et al.  A parameter selection strategy for particle swarm optimization based on particle positions , 2014, Expert Syst. Appl..

[30]  Yuelin Gao,et al.  Particle Swarm Optimization Algorithm with Exponent Decreasing Inertia Weight and Stochastic Mutation , 2009, 2009 Second International Conference on Information and Computing Science.

[31]  Weiyin Ma,et al.  A planar quadratic clipping method for computing a root of a polynomial in an interval , 2015, Comput. Graph..

[32]  Agnieszka Lisowska,et al.  Polynomiography via Ishikawa and Mann Iterations , 2012, ISVC.

[33]  Jiangye Yuan,et al.  A modified particle swarm optimizer with dynamic adaptation , 2007, Appl. Math. Comput..

[34]  Bijaya Ketan Panigrahi,et al.  Adaptive particle swarm optimization approach for static and dynamic economic load dispatch , 2008 .

[35]  A. Rezaee Jordehi,et al.  Parameter selection in particle swarm optimisation: a survey , 2013, J. Exp. Theor. Artif. Intell..

[36]  Krzysztof Gdawiec,et al.  Fractal patterns from the dynamics of combined polynomial root finding methods , 2017 .

[37]  Bahman Kalantari Polynomiography and applications in art, education, and science , 2004, Comput. Graph..

[38]  Ajith Abraham,et al.  Inertia-Adaptive Particle Swarm Optimizer for Improved Global Search , 2008, 2008 Eighth International Conference on Intelligent Systems Design and Applications.

[39]  M. Senthil Arumugam,et al.  On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems , 2008, Appl. Soft Comput..

[40]  W. R. Mann,et al.  Mean value methods in iteration , 1953 .

[41]  R. Eberhart,et al.  Empirical study of particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[42]  J. Franklin,et al.  Computational Methods for Physics , 2015 .