Sigmoidal Spider Monkey Optimization Algorithm

Spider Monkey Optimization is the new field of Swarm Intelligence. The SMO algorithms well balanced for a good exploration. Algorithm based on Spider's extraordinary behavior. Monkeys the SMO algorithm is a population-based meta-heuristic. So these articles present automatic modifying the position of the local search to improve its position. Then we say the updating algorithm called Improved Spider Monkey Optimization algorithm. Using this alternative technique we improve speed convergence. Also this algorithm tested on the problems of reference. The research paper shows proposes a productive variant of SMO that improves the Number of function. Here we have some equations to resolve these problems also we compare the result between SMO and new ISMO

Download Full-text

Spider monkey optimization algorithm for constrained optimization problems

Soft Computing ◽

10.1007/s00500-016-2419-0 ◽

2016 ◽

Vol 21 (23) ◽

pp. 6933-6962 ◽

Cited By ~ 19

Author(s):

Kavita Gupta ◽

Kusum Deep ◽

Jagdish Chand Bansal

Keyword(s):

Constrained Optimization ◽

Optimization Algorithm ◽

Optimization Problems ◽

Spider Monkey ◽

Constrained Optimization Problems ◽

Spider Monkey Optimization

Download Full-text

Hyperbolic Spider Monkey Optimization Algorithm

Recent Advances in Computer Science and Communications ◽

10.2174/2213275912666181207155334 ◽

2020 ◽

Vol 13 (1) ◽

pp. 35-42 ◽

Cited By ~ 1

Author(s):

Sandeep Kumar ◽

Anand Nayyar ◽

Nhu Gia Nguyen ◽

Rajani Kumari

Keyword(s):

Optimization Algorithm ◽

Growth Function ◽

Spider Monkey ◽

Benchmark Problems ◽

Spider Monkeys ◽

Rapid Convergence ◽

New Approach ◽

Spider Monkey Optimization ◽

Increasing Function ◽

Search Capability

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.

Download Full-text