scholarly journals Modified Spider Monkey Optimization Algorithm Based on Self-Adaptive Inertia Weight

2021 ◽  
Author(s):  
Ximing Liang ◽  
◽  
Yang Zhang ◽  
Keyword(s):  
Optimization Algorithm ◽  
Spider Monkey ◽  
The Self ◽  
Learning Ability ◽  
Benchmark Problems ◽  
Inertia Weight ◽  
Engineering Design Problems ◽  
Spider Monkey Optimization ◽  
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.

Hyperbolic Spider Monkey Optimization Algorithm

2020 ◽  
Vol 13 (1) ◽  
pp. 35-42 ◽  
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.

scholarly journals Particle Swarm Optimization Combined with Inertia-Free Velocity and Direction Search

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 597
Author(s):  
Kun Miao ◽  
Qian Feng ◽  
Wei Kuang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Small Region ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Local Searches ◽  
Engineering Design Problems ◽  
Direction Search

The particle swarm optimization algorithm (PSO) is a widely used swarm-based natural inspired optimization algorithm. However, it suffers search stagnation from being trapped into a sub-optimal solution in an optimization problem. This paper proposes a novel hybrid algorithm (SDPSO) to improve its performance on local searches. The algorithm merges two strategies, the static exploitation (SE, a velocity updating strategy considering inertia-free velocity), and the direction search (DS) of Rosenbrock method, into the original PSO. With this hybrid, on the one hand, extensive exploration is still maintained by PSO; on the other hand, the SE is responsible for locating a small region, and then the DS further intensifies the search. The SDPSO algorithm was implemented and tested on unconstrained benchmark problems (CEC2014) and some constrained engineering design problems. The performance of SDPSO is compared with that of other optimization algorithms, and the results show that SDPSO has a competitive performance.

scholarly journals Adaptive Learning Mode of a Multimedia-based "English Literature" Learning System

2017 ◽  
Vol 12 (01) ◽  
pp. 71 ◽  
Author(s):  
Jie Mei ◽  
Yanhong Guo ◽  
Xiaokun Li
Keyword(s):  
Adaptive Learning ◽  
English Literature ◽  
Learning System ◽  
The Self ◽  
Independent Learning ◽  
Learning Ability ◽  
Teaching Model ◽  
Learning Situation ◽  
Teaching Pattern ◽  
Self Adaptive

In this paper, a multimedia-based English pronunciation learning system was designed. On this basis, a self-adaptive learning mode which consists of the teaching mode and the independent learning mode was proposed. The self-adaptive teaching model uses corpus technology and covers the exploratory “3I” (Illustration-Interaction-Induction) teaching model, thereby changing the traditional teaching pattern of “spoon-feeding”; when it comes to the independent learning mode, the self-adaptive system can automatically set corresponding learning tasks according to the learning situation of students, to improve the autonomy and differences of students’ self-learning. At the same time, the approach of comparative teaching was especially adopted to test the validity of this system and the learning mode. Specifically, the exquisite course of “English Literature” for students of Grade 2015 majoring in English was selected as the experimental group, to compare with the learning situation of their counterparts of Grade 2014 in the last year. The results show that the learning mode is remarkable in its teaching practicality, could bring a significant effect on improving teaching efficiency and students’ independent learning ability, and enjoys a high research value and a promising application prospect.

Ageist Spider Monkey Optimization algorithm

2016 ◽  
Vol 28 ◽  
pp. 58-77 ◽  
Author(s):  
Avinash Sharma ◽  
Akshay Sharma ◽  
B.K. Panigrahi ◽  
Deep Kiran ◽  
Rajesh Kumar
Keyword(s):  
Optimization Algorithm ◽  
Spider Monkey ◽  
Spider Monkey Optimization
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