scholarly journals Social-Spider Optimization Neural Networks for Microwave Filters Modeling

2018 ◽  
pp. 364-372
Author(s):  
Erredir Chahrazad ◽  
Emir Bouarroudj ◽  
Mohamed Lahdi Riabi
Keyword(s):  
Neural Networks ◽  
Microwave Filters ◽  
Social Spider ◽  
Social Spider Optimization

Designing evolutionary feedforward neural networks using social spider optimization algorithm

2015 ◽  
Vol 26 (8) ◽  
pp. 1919-1928 ◽  
Author(s):  
Seyedeh Zahra Mirjalili ◽  
Shahrzad Saremi ◽  
Seyed Mohammad Mirjalili
Keyword(s):  
Neural Networks ◽  
Optimization Algorithm ◽  
Feedforward Neural Networks ◽  
Social Spider ◽  
Social Spider Optimization

scholarly journals Social Spider Optimization Algorithm: Theory and its Applications

2019 ◽  
Vol 8 (10) ◽  
pp. 327-332
Keyword(s):  
Neural Networks ◽  
Swarm Intelligence ◽  
Optimization Problems ◽  
Basic Sciences ◽  
Insect Behaviour ◽  
Social Spider ◽  
Electric Circuits ◽  
Social Spider Optimization ◽  
Algorithm Theory ◽  
Swarm Intelligence Algorithm

An extensive variety of optimization problems are solved by swarm intelligence algorithms that are modelled based on the animal or insect behaviour while living in groups. One such recent swarm intelligence algorithm is Social Spider Optimization (SSO). This paper thoroughly reviews and analyses the characteristics of this meta-heuristic algorithm. Since the existing literature of this algorithm is comparatively limited, the paper discusses the research ideas presented in such existing works and classifies the literature on basis of the application areas like image processing, optical flow, electric circuits, neural networks and basic sciences. It also sets a basis for research applications of the algorithm in order to tap the complete potential of the algorithm in other areas to achieve desired results

scholarly journals Social spider optimization algorithm for tuning parameters in PD-like Interval Type-2 Fuzzy Logic Controller applied to a parallel robot

2021 ◽  
Vol 54 (3-4) ◽  
pp. 303-323
Author(s):  
Amjad J Humaidi ◽  
Huda T Najem ◽  
Ayad Q Al-Dujaili ◽  
Daniel A Pereira ◽  
Ibraheem Kasim Ibraheem ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Trajectory Tracking ◽  
Fuzzy Logic Controller ◽  
Optimization Technique ◽  
Parallel Robot ◽  
Design Parameters ◽  
Social Spider ◽  
Social Spider Optimization ◽  
Interval Type

This paper presents control design based on an Interval Type-2 Fuzzy Logic (IT2FL) for the trajectory tracking of 3-RRR (3-Revolute-Revolute-Revolute) planar parallel robot. The design of Type-1 Fuzzy Logic Controller (T1FLC) is also considered for the purpose of comparison with the IT2FLC in terms of robustness and trajectory tracking characteristics. The scaling factors in the output and input of T1FL and IT2FL controllers play a vital role in improving the performance of the closed-loop system. However, using trial-and-error procedure for tuning these design parameters is exhaustive and hence an optimization technique is applied to achieve their optimal values and to reach an improved performance. In this study, Social Spider Optimization (SSO) algorithm is proposed as a useful tool to tune the parameters of proportional-derivative (PD) versions of both IT2FLC and T1FLC. Two scenarios, based on two square desired trajectories (with and without disturbance), have been tested to evaluate the tracking performance and robustness characteristics of proposed controllers. The effectiveness of controllers have been verified via numerical simulations based on MATLAB/SIMULINK programming software, which showed the superior of IT2FLC in terms of robustness and tracking errors.

Minimum attribute reduction algorithm based on quick extraction and multi-strategy social spider optimization

2021 ◽  
pp. 1-16
Author(s):  
Qianjin Wei ◽  
Chengxian Wang ◽  
Yimin Wen
Keyword(s):  
Rough Set Theory ◽  
Attribute Reduction ◽  
Search Space ◽  
Initial State ◽  
Solution Quality ◽  
Social Spider ◽  
Opposition Based Learning ◽  
Social Spider Optimization ◽  
Intelligent Optimization Algorithm ◽  
Redundancy Detection

Intelligent optimization algorithm combined with rough set theory to solve minimum attribute reduction (MAR) is time consuming due to repeated evaluations of the same position. The algorithm also finds in poor solution quality because individuals are not fully explored in space. This study proposed an algorithm based on quick extraction and multi-strategy social spider optimization (QSSOAR). First, a similarity constraint strategy was called to constrain the initial state of the population. In the iterative process, an adaptive opposition-based learning (AOBL) was used to enlarge the search space. To obtain a reduction with fewer attributes, the dynamic redundancy detection (DRD) strategy was applied to remove redundant attributes in the reduction result. Furthermore, the quick extraction strategy was introduced to avoid multiple repeated computations in this paper. By combining an array with key-value pairs, the corresponding value can be obtained by simple comparison. The proposed algorithm and four representative algorithms were compared on nine UCI datasets. The results show that the proposed algorithm performs well in reduction ability, running time, and convergence speed. Meanwhile, the results confirm the superiority of the algorithm in solving MAR.

A Novel Method for Multispectral Image Classification by Using Social Spider Optimization Algorithm Integrated to Fuzzy C-Mean Clustering

2019 ◽  
Vol 45 (1) ◽  
pp. 42-53 ◽  
Author(s):  
Quang-Thanh Bui ◽  
Quoc-Huy Nguyen ◽  
Van Manh Pham ◽  
Vu Dong Pham ◽  
Mai Hoang Tran ◽  
...  
Keyword(s):  
Image Classification ◽  
Optimization Algorithm ◽  
Multispectral Image ◽  
Social Spider ◽  
Social Spider Optimization ◽  
Novel Method
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