Social Spider Algorithm Employed Multi-level Thresholding Segmentation Approach

2016 ◽  
pp. 249-259 ◽  
Author(s):  
Prateek Agarwal ◽  
Rahul Singh ◽  
Sandeep Kumar ◽  
Mahua Bhattacharya
Keyword(s):  
Social Spider ◽  
Segmentation Approach ◽  
Social Spider Algorithm ◽  
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.

Parameter identification of solar cells and fuel cell using improved social spider algorithm

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Hadi Kashefi ◽  
Ahmad Sadegheih ◽  
Ali Mostafaeipour ◽  
Mohammad Mohammadpour Omran
Keyword(s):  
Fuel Cell ◽  
Proton Exchange Membrane ◽  
Search Space ◽  
Proton Exchange ◽  
Model Parameters ◽  
Social Spider ◽  
Content Type ◽  
Pv Systems ◽  
Modified Method ◽  
Social Spider Algorithm

Purpose To design, control and evaluate photovoltaic (PV) systems, an accurate model is required. Accuracy of PV models depends on model parameters. This study aims to use a new algorithm called improved social spider algorithm (ISSA) to detect model parameters. Design/methodology/approach To improve performance of social spider algorithm (SSA), an elimination period is added. In addition, at the beginning of each period, a certain number of the worst solutions are replaced by new solutions in the search space. This allows the particles to find new paths to get the best solution. Findings In this paper, ISSA is used to estimate parameters of single-diode and double-diode models. In addition, effect of irradiation and temperature on I–V curves of PV modules is studied. For this purpose, two different modules called multi-crystalline (KC200GT) module and polycrystalline (SW255) are used. It should be noted that to challenge the performance of the proposed algorithm, it has been used to identify the parameters of a type of widely used module of fuel cell called proton exchange membrane fuel cell. Finally, comparing and analyzing of ISSA results with other similar methods shows the superiority of the presented method. Originality/value Changes in the spider’s movement process in the SSA toward the desired response have improved the algorithm’s performance. Higher accuracy and convergence rate, skipping local minimums, global search ability and search in a limited space can be mentioned as some advantages of this modified method compared to classic SSA.

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