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

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

Keywords:

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