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: 65  ,  Download: 31

Authors

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

Abstract

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.

Keywords

  • 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

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