2 research outputs found
Introductory Review of Swarm Intelligence Techniques
With the rapid upliftment of technology, there has emerged a dire need to
fine-tune or optimize certain processes, software, models or structures, with
utmost accuracy and efficiency. Optimization algorithms are preferred over
other methods of optimization through experimentation or simulation, for their
generic problem-solving abilities and promising efficacy with the least human
intervention. In recent times, the inducement of natural phenomena into
algorithm design has immensely triggered the efficiency of optimization process
for even complex multi-dimensional, non-continuous, non-differentiable and
noisy problem search spaces. This chapter deals with the Swarm intelligence
(SI) based algorithms or Swarm Optimization Algorithms, which are a subset of
the greater Nature Inspired Optimization Algorithms (NIOAs). Swarm intelligence
involves the collective study of individuals and their mutual interactions
leading to intelligent behavior of the swarm. The chapter presents various
population-based SI algorithms, their fundamental structures along with their
mathematical models.Comment: Submitted to Springe