Multi-level image thresholding based on social spider algorithm for global optimization

2019 ◽  
Vol 11 (4) ◽  
pp. 713-718 ◽  
Author(s):  
Taymaz Rahkar Farshi ◽  
Mohanna Orujpour
Keyword(s):  
Global Optimization ◽  
Social Spider ◽  
Image Thresholding ◽  
Social Spider Algorithm ◽  
Multi Level

scholarly journals A social spider algorithm for global optimization

2015 ◽  
Vol 30 ◽  
pp. 614-627 ◽  
Author(s):  
James J.Q. Yu ◽  
Victor O.K. Li
Keyword(s):  
Global Optimization ◽  
Social Spider ◽  
Social Spider Algorithm

A competitive chain-based Harris Hawks Optimizer for global optimization and multi-level image thresholding problems

2020 ◽  
Vol 95 ◽  
pp. 106347 ◽  
Author(s):  
Mohamed Abd Elaziz ◽  
Ali Asghar Heidari ◽  
Hamido Fujita ◽  
Hossein Moayedi
Keyword(s):  
Global Optimization ◽  
Image Thresholding ◽  
Multi Level

Hyperspectral Multi-level Image Thresholding using Qutrit Genetic Algorithm

2021 ◽  
pp. 115107
Author(s):  
Tulika Dutta ◽  
Sandip Dey ◽  
Siddhartha Bhattacharyya ◽  
Somnath Mukhopadhyay ◽  
Prasun Chakrabarti
Keyword(s):  
Genetic Algorithm ◽  
Image Thresholding ◽  
Multi Level

Discrete Social Spider Algorithm for Solving Traveling Salesman Problem

2021 ◽  
pp. 2150020
Author(s):  
Asieh Khosravanian ◽  
Mohammad Rahmanimanesh ◽  
Parviz Keshavarzi
Keyword(s):  
Genetic Algorithm ◽  
Discrete Optimization ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Fitness Function ◽  
Traveling Salesman ◽  
The Other ◽  
Discrete Optimization Problem ◽  
Social Spider ◽  
Social Spider Algorithm

The Social Spider Algorithm (SSA) was introduced based on the information-sharing foraging strategy of spiders to solve the continuous optimization problems. SSA was shown to have better performance than the other state-of-the-art meta-heuristic algorithms in terms of best-achieved fitness values, scalability, reliability, and convergence speed. By preserving all strengths and outstanding performance of SSA, we propose a novel algorithm named Discrete Social Spider Algorithm (DSSA), for solving discrete optimization problems by making some modifications to the calculation of distance function, construction of follow position, the movement method, and the fitness function of the original SSA. DSSA is employed to solve the symmetric and asymmetric traveling salesman problems. To prove the effectiveness of DSSA, TSPLIB benchmarks are used, and the results have been compared to the results obtained by six different optimization methods: discrete bat algorithm (IBA), genetic algorithm (GA), an island-based distributed genetic algorithm (IDGA), evolutionary simulated annealing (ESA), discrete imperialist competitive algorithm (DICA) and a discrete firefly algorithm (DFA). The simulation results demonstrate that DSSA outperforms the other techniques. The experimental results show that our method is better than other evolutionary algorithms for solving the TSP problems. DSSA can also be used for any other discrete optimization problem, such as routing problems.

scholarly journals Field Weakening Control of a Separately Excited DC Motor Using Neural Network Optemized by Social Spider Algorithm

Engineering ◽  
2016 ◽  
Vol 08 (01) ◽  
pp. 1-10
Author(s):  
Waleed I. Hameed ◽  
Ahmed S. Kadhim ◽  
Ali Abdullah K. Al-Thuwaynee
Keyword(s):  
Neural Network ◽  
Dc Motor ◽  
Social Spider ◽  
Field Weakening ◽  
Social Spider Algorithm

A New Bio-Inspired Social Spider Algorithm

2021 ◽  
Vol 12 (1) ◽  
pp. 79-93
Author(s):  
Dharmpal Singh
Keyword(s):  
Swarm Intelligence ◽  
Real World ◽  
Optimization Problems ◽  
Fitness Function ◽  
Foraging Strategy ◽  
Superior Performance ◽  
Experimental Result ◽  
Social Spider ◽  
Spider Web ◽  
Social Spider Algorithm

The concept of bio-inspired algorithms is used in real-world problems to search the efficient problem-solving methods. Evolutionary computation and swarm intelligence are outstanding examples of nature-inspired solution techniques of metahuristics. In this paper, an effort has been made to propose a modified social spider algorithm to solve global optimization problems in the real world. Social spiders used the foraging strategy, vibrations on the spider web to determine the positions of prey. The selection of vibration, estimated new position and calculation of the fitness function, has been furnished in details way as compared to different previously proposed swarm intelligence algorithms. Moreover, experimental result has been carried out by modified social spider on series of widely-used benchmark problem with four benchmark algorithms. Furthermore, a modified form of the proposed algorithm has superior performance as compared to other state-of-the-art metaheuristics algorithms.

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