Computer Science & Electrical

Computer Science & Electrical

HIDE : Human Inspired Differential Evolution – An Algorithm under Artificial Human Optimization Field

Pages: 6  ,  Volume: 7  ,  Issue: 1 , June   2018
Received: 03 Jul 2018  ,  Published: 06 July 2018
Views: 483  ,  Download: 179


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


Artificial Human Optimization is a new field that came into existence on December 2016. All the optimization algorithms that were created and are being created based on Artificial Humans will come under Artificial Human Optimization Field. Just like agents in Ant Colony Optimization are Artificial Ants, agents in Bee Colony Optimization are Artificial Bees, agents in Genetic Algorithms are Artificial chromosomes, agents in Particle Swarm Optimization are Artificial Birds or Artificial Fishes, similarly agents in Artificial Human Optimization Algorithms are Artificial Humans. “Multiple Strategy Human Optimization (MSHO)” is a new algorithm designed recently based on Artificial Humans. The key concept in MSHO is to use more than one strategy in the optimization process. Two strategies are used in MSHO. One strategy is to move towards the best individual in one generation. Another strategy is to move away from the worst individual in next generation. Differential Evolution is a popular algorithm for solving optimization problems in various domains. In this paper “Human Inspired Differential Evolution (HIDE)” is proposed. The idea of HIDE algorithm is to use the concept of Multiple Strategies of MSHO algorithm in Differential Evolution. The mutation operator of Differential Evolution algorithm is modified to incorporate the key concept of MSHO algorithm in Differential Evolution. The proposed HIDE algorithm is tested by applying it on a complex benchmark problem.


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

    (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 (In Review).

    (8) 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.

    (9) Ali W. Mohamed, Hegazy Z. Sabry, Motaz Khorshid. An alternative differential evolution algorithm for global Optimization. Journal of Advanced Research (2012) 3, 149–165.

    (10) Ali Wagdy Mohamed, Hegazy Zaher Sabry, Tareq Abd-Elaziz. Real parameter optimization by an effective differential evolution algorithm. Egyptian Informatics Journal (2013) 14, 37–53

    (11) Hsin-Chuan Kuo, Ching-Hai Lin, and Jeun-Len Wu. A Creative Differential Evolution Algorithm for Global Optimization Problems. Journal of Marine Science and Technology. DOI: 10.6119/JMST-012-0917-1

    (12) (accessed on 29th june, 2018).