Computer Science & Electrical

Computer Science & Electrical

Collection of Abstracts in Artificial Human Optimization Field

Pages: 15  ,  Volume: 7  ,  Issue: 1 , June   2018
Received: 03 Jul 2018  ,  Published: 06 July 2018
Views: 138  ,  Download: 69


# Author Name
1 Satish Gajawada
2 Hassan M. H. Mustafa


Collection of Abstracts is a new kind of research paper where the complete focus is to show abstracts published in a particular field. Collection of Abstracts is generally published in conferences as pre-conference proceedings book and given to the authors on the day of conference. This kind of research papers give valuable information to researchers like “what are the titles? What are the abstracts? Who are the authors working in the field?”. The corresponding author of this paper published a paper titled “Entrepreneur : Artificial Human Optimization” where he collected 13 abstracts and shown them as it is in an attempt to propose a new field. This work was published in Transactions on Machine Learning and artificial Intelligence. Now, “Collection of Abstracts in Artificial Human Optimization Field” is the second research paper in the entire research industry, which is completely based on Abstracts of papers. The first 2 sections of this paper shows titles and abstracts of papers in Artificial Human Optimization Field. Third section shows corrections to previous work in Artificial Human Optimization Field.


  • Artificial Humans
  • Global Optimization Techniques
  • Artificial Human Optimization
  • Nature Inspired Computing
  • Bio-Inspired Computing
  • Artificial Intelligence
  • Machine Learning
  • Genetic Algorithms
  • Particle Swarm Optimization
  • Differential Evolution
  • Ant Colony Optimization
  • References

    1) Ahmadi, SA. Neural Comput & Applic (2017) 28(Suppl 1): 233.

    2) Da-Zheng Feng, Han-Zhe Feng and Hai-Qin Zhang. Human Behavior Algorithms for Highly Efficient Global Optimization.

    3) 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.

    4)  Satish Gajawada, “POSTDOC : The Human Optimization”, Computer Science & Information Technology (CS & IT), CSCP, pp. 183-187, 2013.

    5) Edris Fattahi, Mahdi Bidar, and Hamidreza Rashidy Kanan, Int. J. Comp. Intel. Appl. 17, 1850002 (2018) [27 pages]

    6)  Satish Gajawada; Entrepreneur: Artificial Human OptimizationTransactions on Machine Learning and Artificial Intelligence, Volume 4 No 6 December (2016); pp: 64-70

    7) Ruo-Li Tang, Yan-Jun Fang, "Modification of particle swarm optimization with human simulated property", Neurocomputing, Volume 153, Pages 319–331, 2015.

    8) 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.

    9) L. M. Zhang, C. Dahlmann and Y. Zhang. “Human-inspired algorithms for continuous function optimization”, In IEEE International Conference on Intelligent Computing and Intelligent Systems, 2009, vol. 1, pp. 318-321. 

    10) A. Ahmadi-Javid, "Anarchic Society Optimization: A human-inspired method", Proc. 2011 IEEE Congr. Evol. Comput., pp. 2586-2592, 2011.    

    11) Mingyi Zhang, Luna; Zhang, Yanqing. "The Human-Inspired Algorithm: A Hybrid Nature-Inspired Approach to Optimizing Continuous Functions with Constraints," Journal of Computational Intelligence and Electronic Systems, Volume 2, Number 1, June 2013, pp. 80-87(8).

    12) Satish Gajawada, “CEO: Different Reviews on PhD in Artificial Intelligence”, Global Journal of Advanced Research, vol. 1, no.2, pp. 155-158, 2014.      

    13) Satish Gajawada, “Artificial Human Optimization – An Introduction”, Transactions on Machine Learning and Artificial Intelligence, Volume 6, No 2, pp: 1-9, April 2018.

    14) Satish Gajawada, “An Ocean of Opportunities in Artificial Human Optimization Field”, Transactions on Machine Learning and Artificial Intelligence, Volume 6, No 3, June 2018.

    15) Satish Gajawada. "25 Reviews on Artificial Human Optimization Field for the First Time in Research Industry".  International Journal of Research Publications, United Kingdom. Vol 5, no. 2, 2018.

    16) Singh M.K. (2013) A New Optimization Method Based on Adaptive Social Behavior: ASBO. In: Kumar M. A., R. S., Kumar T. (eds) Proceedings of International Conference on Advances in Computing. Advances in Intelligent Systems and Computing, vol 174. Springer, New Delhi

    17) M. R. Tanweer, S. Suresh, N. Sundararajan, "Human meta-cognition inspired collaborative search algorithm for optimization", 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI), pp. 1-6, 2014.

    18) 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.

    19) 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.

    20) Prakasha S, H R Shashidhar, Manoj Kumar Singh, G T Raju, ”Clustering of Text Document based on ASBO”, Wulfenia journal, Vol 20, No. 6; pp: 152-165, 2013.

    21) Sridhar N, Nagaraj Ramrao, Manoj Kumar Singh, "PID Controller Auto tuning using ASBO Technique”, Journal of Control Engineering and Technology, Vol. 4, Iss. 3, PP. 192-204, 2014.

    22) Devika P. D, Dinesh P. A, Rama Krishna Prasad, Manoj Kumar Singh, "ASBO Based Compositional in Combinatorial Catalyst", J. Math.Comput.Sci.5 (2015), No.3, 351-393, ISSN: 1927-5307, 2015.”

    23) Dai C., Zhu Y., Chen W. (2007) Seeker Optimization Algorithm. In: Wang Y., Cheung Y., Liu H. (eds) Computational Intelligence and Security. CIS 2006. Lecture Notes in Computer Science, vol 4456. Springer, Berlin, Heidelberg.

    24) R.V.Rao, V.J.Savsani, D.P.Vakharia. Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design Volume 43, Issue 3, March 2011, Pages 303-315.

    25) Esmaeil Atashpaz-Gargari; Caro Lucas. Imperialist competitive algorithm: An algorithm for optimization inspired by imperialistic competition. IEEE Congress on Evolutionary Computation, 2007. CEC 2007.

    26) Eita M.A., Fahmy M.M. (2010) Group Counseling Optimization: A Novel Approach. In: Bramer M., Ellis R., Petridis M. (eds) Research and Development in Intelligent Systems XXVI. Springer, London.

    27) Wang L., Ni H., Yang R., Fei M., Ye W. (2014) A Simple Human Learning Optimization Algorithm. In: Fei M., Peng C., Su Z., Song Y., Han Q. (eds) Computational Intelligence, Networked Systems and Their Applications. ICSEE 2014, LSMS 2014. Communications in Computer and Information Science, vol 462. Springer, Berlin, Heidelberg.

    28) Feng, X., Zou, R. & Yu, H. Soft Comput (2015) 19: 2955.

    29) Hamid Reza Kamali, Ahmad Sadegheih, Mohammad Ali Vahdat-Zad, Hassan Khademi-Zare (2015) Immigrant Population Search Algorithm for Solving Constrained Optimization Problems, Applied Artificial Intelligence, 29:3, 243-258, DOI: 10.1080/08839514.2015.1004613.

    30) Burman, R., Chakrabarti, S. & Das, S. Soft Comput (2017) 21: 3267.

    31) Xu Y., Cui Z., Zeng J. (2010) Social Emotional Optimization Algorithm for Nonlinear Constrained Optimization Problems. In: Panigrahi B.K., Das S., Suganthan P.N., Dash S.S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2010. Lecture Notes in Computer Science, vol 6466. Springer, Berlin, Heidelberg.

    32) Kaur, Rishemjit and Kumar, Ritesh and Bhondekar, A.P. and Kapur, Pawan (2013) Human opinion dynamics: An inspiration to solve complex optimization problems. Scientific Reports, 3. pp. 1-7. ISSN 2045-2322.