An Artificial Human Optimization Algorithm titled Human Thinking Particle Swarm Optimization
Artificial Human Optimization is a latest field proposed in December 2016. Just like artificial Chromosomes are agents for Genetic Algorithms, similarly artificial Humans are agents for Artificial Human Optimization Algorithms. Particle Swarm Optimization is very popular algorithm for solving complex optimization problems in various domains. In this paper, Human Thinking Particle Swarm Optimization (HTPSO) is proposed by applying the concept of thinking of Humans into Particle Swarm Optimization. The proposed HTPSO algorithm is tested by applying it on various benchmark functions. Results obtained by HTPSO algorithm are compared with Particle Swarm Optimization algorithm.
(1) Satish Gajawada; Entrepreneur: Artificial Human Optimization. Transactions on Machine Learning and Artificial Intelligence, Volume 4 No 6 December (2016); pp: 64-70
(2) Satish Gajawada, “CEO: Different Reviews on PhD in Artificial Intelligence”, Global Journal of Advanced Research, vol. 1, no.2, pp. 155-158, 2014.
(3) Satish Gajawada, “POSTDOC : The Human Optimization”, Computer Science & Information Technology (CS & IT), CSCP, pp. 183-187, 2013.
(4) Satish Gajawada, “Artificial Human Optimization – An Introduction”, Transactions on Machine Learning and Artificial Intelligence, Volume 6, No 2, pp: 1-9, April 2018.
(5) Satish Gajawada, “An Ocean of Opportunities in Artificial Human Optimization Field”, Transactions on Machine Learning and Artificial Intelligence, Volume 6, No 3, June 2018.
(6) Satish Gajawada, “25 Reviews on Artificial Human Optimization Field for the First Time in Research Industry”, International Journal of Research Publications, Volume 5, No 2, United Kingdom, 2018.
(7) Satish Gajawada and Hassan M. H. Mustafa, “Collection of Abstracts in Artificial Human Optimization Field”, International Journal of Research Publications, Volume 7, No 1, United Kingdom, 2018.
(8) Satish Gajawada, Hassan M. H. Mustafa , HIDE : Human Inspired Differential Evolution - An Algorithm under Artificial Human Optimization Field , International Journal of Research Publications (Volume: 7, Issue: 1), http://ijrp.org/paper_detail/264
(9) Hao Liu, Gang Xu, Gui-yan Ding, and Yu-bo Sun. Human Behavior-Based Particle Swarm Optimization. The Scientific World Journal. Volume 2014, Article ID 194706, 14 pages. http://dx.doi.org/10.1155/2014/194706
(10) Ruo-Li Tang, Yan-Jun Fang, "Modification of particle swarm optimization with human simulated property", Neurocomputing, Volume 153, Pages 319–331, 2015.
(11) Muhammad Rizwan Tanweer, Suresh Sundaram, "Human cognition inspired particle swarm optimization algorithm",2014 IEEE Ninth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 2014.
(12) M.R. Tanweer, S. Suresh, N. Sundararajan, "Self regulating particle swarm optimization algorithm", Information Sciences: an International Journal, Volume 294, Issue C, Pages 182-202, 2015.
(13) M. R. Tanweer, S. Suresh, N. Sundararajan, "Improved SRPSO algorithm for solving CEC 2015 computationally expensive numerical optimization problems", 2015 IEEE Congress on Evolutionary Computation (CEC), pp. 1943-1949, 2015.
(14) https://www.sfu.ca/~ssurjano/ackley.html (accessed 26th July, 2018)
Copyright (c) 2018 Computer Reviews Journal
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain the copyright of their manuscripts, and all Open Access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided that the original work is properly cited.
Computer Reviews Journal allow the author(s) to retain publishing rights without restrictions.