Background:
Spider monkey optimization algorithm is recently developed natureinspired
algorithm. It is based on fission-fusion social structure of spider monkeys. Perturbation rate
is one of the important parameter of spider monkey optimization algorithm, which affects the convergence
behavior of spider monkey optimization algorithm. Generally, perturbation rate is a linearly
increasing function. However, due to the availability of non-linearity in different applications, a
non-linear function may affect the performance of spider monkey optimization algorithm.
Objective:
This paper provides a detailed study on various perturbation techniques used in spider
monkey optimization algorithm and recommends a novel alternative of hyperbolic spider monkey
optimization algorithm. The new approach is named as hyperbolic Spider Monkey Optimization algorithm
as the perturbation strategy inspired by hyperbolic growth function.
Methods:
The proposed algorithm is tested over a set of 23 CEC 2005 benchmark problems.
Results:
The experimental outcomes illustrate that the hyperbolic spider monkey optimization algorithm
effectively increase the reliability of spider monkey optimization algorithm in comparison to
the considered approaches.
Conclusion:
The hyperbolic spider monkey optimization algorithm provides improved perturbation
rate, desirable convergence precision, rapid convergence rate, and improved global search capability.
Improve the local search using Automatic Modified Position in Improved Spider Monkey Optimization Algorithm
