Providing a Bird Swarm Algorithm based on Classical Conditioning Learning Behavior and Comparing this Algorithm with sinDE, JOA, NPSO and D-PSO-C Based on Using in Nanoscience

Subject Areas : Journal of Optoelectronical Nanostructures

Abdorreza Asrar ¹ , Mojtaba Servatkhah ² , Milad Yasrebi ³

1 - Malek Ashtar University of Technology
2 - Department of Physics, Marvdasht Branch, Islamic Azad University, Marvdasht, Iran
3 - Faculty of Naval Aviation, Malek Ashtar University of Technology, Iran

Received: 2020-05-18 Accepted : 2020-07-14 Published : 2020-08-01

Keywords: Optimization, Nanotechnology, Quantum, Swarm Algorithm, Cost, Speed, Particle, Standard Deviation,

Abstract :

There can be no doubt that nanotechnology will play a major role in our future
technology. Computer science offers more opportunities for quantum and
nanotechnology systems. Soft Computing techniques such as swarm intelligence, can
enable systems with desirable emergent properties. Optimization is an important and
decisive activity in structural designing. The inexpensive requirement in memory and
computation suits well with nanosized autonomous agents whose capabilities may be
limited by their size. To apply in nanorobot control, a modification of PSO algorithm is
required. Using birds’ classical conditioning learning behavior in this paper, particles will
learn to perform a natural conditional behavior towards an unconditioned stimulus.
Particles in the problem space are divided into multiple categories and if any particle finds
the diversity of its category in a low level, it will try to move towards its best personal
experience. We also used the idea of birds’ sensitivity to the space in which they fly and
tried to move the particles more quickly in improper spaces so that they would depart the
spaces. On the contrary, we reduced the particles’ speed in valuable spaces in order to do
more search. The proposed method was implemented in MATLAB software and
compared to similar results. It was shown that the proposed method finds a good solution
to the problem regardless of nondeterministic functions or stochastic conditions.

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