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A Survey to Nature Inspired Soft Computing

Author

Listed:
  • Deepak Kumar

    (Amity University, Noida, India)

  • Sushil Kumar

    (Amity University, Noida, India)

  • Rohit Bansal

    (Department of Management Studies, Vaish College of Engineering, Rohtak, India)

  • Parveen Singla

    (ECE Department, Chandigarh Engineering College, Landran, India)

Abstract

This article describes how swarm intelligence (SI) and bio-inspired techniques shape in-vogue topics in the advancements of the latest algorithms. These algorithms can work on the basis of SI, using physical, chemical and biological frameworks. The authors can name these algorithms as SI-based, inspired by biology, physics and chemistry as per the basic concept behind the particular algorithm. A couple of calculations have ended up being exceptionally effective and consequently have turned out to be the mainstream devices for taking care of real-world issues. In this article, the reason for this survey is to show a moderately complete list of the considerable number of algorithms in order to boost research in these algorithms. This article discusses Ant Colony Optimization (ACO), the Cuckoo Search, the Firefly Algorithm, Particle Swarm Optimization and Genetic Algorithms in detail. For ACO a real-time problem, known as Travelling Salesman Problem, is considered while for other algorithms a min-sphere problem is considered, which is well known for comparison of swarm techniques.

Suggested Citation

  • Deepak Kumar & Sushil Kumar & Rohit Bansal & Parveen Singla, 2017. "A Survey to Nature Inspired Soft Computing," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 8(2), pages 112-133, April.
    • Handle: RePEc:igg:jismd0:v:8:y:2017:i:2:p:112-133
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    Cited by:

    1. Alberto Garces-Jimenez & Jose-Manuel Gomez-Pulido & Nuria Gallego-Salvador & Alvaro-Jose Garcia-Tejedor, 2021. "Genetic and Swarm Algorithms for Optimizing the Control of Building HVAC Systems Using Real Data: A Comparative Study," Mathematics, MDPI, vol. 9(18), pages 1-24, September.

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