Spectrum Allocation Based on Spider Monkey Optimization Algorithm with Nonlinear Inertia Weight and Sine-Cosine Algorithm

The Individual ◽

Self Adaptive

Spider monkey optimization (SMO) algorithm is a new swarm intelligence optimization algorithm proposed in recent years. It simulates the foraging behavior of spider monkeys which have fission-fusion social structure (FFSS). In this paper, a modified spider monkey optimization algorithm is proposed. The self-adaptive inertia weight is introduced in the local leader phase to enhance the self-learning ability of the spider monkey. According to the function value of an individual, the distance from the optimal value is determined, so the inertia weight related the individual function value is added to strength the global search ability or local search ability. The proposed algorithm is tested on 20 benchmark problems and compared with the original SMO and the hybrid algorithm SMOGA and GASMO. The numerical results show that the proposed algorithm has a certain degree of improvement in convergence accuracy and convergence speed. The performance of the proposed algorithm is also inspected by two classical engineering design problems.

Hybrid multi‐objective optimization algorithm using Taylor series model and Spider Monkey Optimization

International Journal for Numerical Methods in Engineering ◽

10.1002/nme.6628 ◽

2021 ◽

Author(s):

Radhika Menon ◽

Anju Kulkarni ◽

Deepak Singh ◽

Mithra Venkatesan

Keyword(s):

Taylor Series ◽

Optimization Algorithm ◽

Spider Monkey ◽

Multi Objective Optimization ◽

Multi Objective ◽

Advances in Intelligent Systems and Computing - Soft Computing: Theories and Applications ◽

An Improved Spider Monkey Optimization Algorithm

10.1007/978-981-10-5687-1_7 ◽

2017 ◽

pp. 73-81 ◽

Cited By ~ 1

Author(s):

Viren Swami ◽

Sandeep Kumar ◽

Sanjay Jain

Keyword(s):

Optimization Algorithm ◽

Spider Monkey ◽

Algorithms for Intelligent Systems - Proceedings of International Conference on Communication and Computational Technologies ◽

Gaussian Distribution Operator Based Spider Monkey Optimization Algorithm to Improve Global Search Ability

10.1007/978-981-15-5077-5_43 ◽

2020 ◽

pp. 479-487

Author(s):

Himani ◽

S. K. Agrawal

Keyword(s):

Gaussian Distribution ◽

Optimization Algorithm ◽

Global Search ◽

Spider Monkey ◽

Design of an Urban Transport Network for the Optimal Location of Bus Stops in a Smart City Based on a Big Data Model and Spider Monkey Optimization Algorithm

10.1007/978-3-030-68655-0_9 ◽

2021 ◽

pp. 167-201

Author(s):

Marlyn Montalvo-Martel ◽

Alberto Ochoa-Zezzatti ◽

Elías Carrum ◽

Denise Barzaga

Keyword(s):

Big Data ◽

Optimization Algorithm ◽

Data Model ◽

Smart City ◽

Optimal Location ◽

Urban Transport ◽

Spider Monkey ◽

Transport Network ◽

Bus Stops

Advances in Intelligent Systems and Computing - Proceedings of the Second International Conference on Computer and Communication Technologies ◽

Multi-level Thresholding Segmentation Approach Based on Spider Monkey Optimization Algorithm

10.1007/978-81-322-2523-2_26 ◽

2015 ◽

pp. 273-287 ◽

Cited By ~ 12

Author(s):

Swaraj Singh Pal ◽

Sandeep Kumar ◽

Manish Kashyap ◽

Yogesh Choudhary ◽

Mahua Bhattacharya

Keyword(s):

Optimization Algorithm ◽

Spider Monkey ◽

Segmentation Approach ◽

Multi Level ◽

Ageist Spider Monkey Optimization algorithm

Swarm and Evolutionary Computation ◽

10.1016/j.swevo.2016.01.002 ◽

2016 ◽

Vol 28 ◽

pp. 58-77 ◽

Cited By ~ 39

Author(s):

Avinash Sharma ◽

Akshay Sharma ◽

B.K. Panigrahi ◽

Deep Kiran ◽

Rajesh Kumar

Keyword(s):

Optimization Algorithm ◽

Spider Monkey ◽

Improve the local search using Automatic Modified Position in Improved Spider Monkey Optimization Algorithm

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8211.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 1634-1637

Keyword(s):

Local Search ◽

Swarm Intelligence ◽

Optimization Algorithm ◽

Research Paper ◽

Population Based ◽

Spider Monkey ◽

Alternative Technique ◽

Smo 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

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 ◽

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 ◽

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.