Modified Social-Spider Optimization Algorithm Applied to Electromagnetic Optimization

Klein, Carlos Eduardo; Segundo, Emerson H. V.; Mariani, Viviana Cocco; Coelho, Leandro dos Santos

doi:10.1109/tmag.2015.2483059

Cited by 48 publications

(24 citation statements)

References 11 publications

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“…The first one is the spiders and the second one is a communal web. It represents the web by the domain of the searching field and representing the problem solutions [15]. SSO algorithm is applied in many optimization problems, but it is different from other optimization methods.…”

Section: Social Spider Optimization (Sso) Algorithmmentioning

confidence: 99%

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Design a Different Structures Controller for Controlled Systems Using a Spiking Neural Network

Hassan¹,

Mahmood²

2019

IJCCCE

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The design and simulation of the Spiking Neural Network (SNN) are proposed in this paper to control a plant without and with load. The proposed controller is performed using Spike Response Model. SNNs are more powerful than conventional artificial neural networks since they use fewer nodes to solve the same problem. The proposed controller is implemented using SNN to work with different structures as P, PI, PD or PID like to control linear and nonlinear models. This controller is designed in discrete form and has three inputs (error, integral of error and derivative of error) and has one output. The type of controller, number of hidden nodes, and number of synapses are set using external inputs. Sampling time is set according to the controlled model. Social-Spider Optimization algorithm is applied for learning the weights of the SNN layers. The proposed controller is tested with different linear and nonlinear models and different reference signals. Simulation results proved the efficiency of the suggested controller to reach accurate responses with minimum Mean Squared Error, small structure and minimum number of epochs under no load and load conditions.

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Section: Social Spider Optimization (Sso) Algorithmmentioning

confidence: 99%

“…, where ( ) is the spider position and ( ) is the fitness value that has been obtained for this position ( ), the values of bests and worsts are the minimum and the maximum values for the solution in the population (minim value problem) as defined in equations (14) and (15), respectively [6]:…”

Section: Social Spider Optimization (Sso) Algorithmmentioning

confidence: 99%

Design a Different Structures Controller for Controlled Systems Using a Spiking Neural Network

Hassan¹,

Mahmood²

2019

IJCCCE

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“…The classical SSO requires the random selection of parameters , , and ((4) and (5)) to control the movement of the spiders, which can affect the mentioned balance leading the algorithm to a premature convergence. With the aim of improving this balance, Carlos E. Klein et al proposed a modification for the SSO (MSSO) in 2016 [25], where the mentioned parameters are selected from a Beta distribution in the range [0-1] [26] instead of the use of random numbers. The use of this distribution helps to preserve diversity and avoid premature convergence, improving the algorithm exploration.…”

Section: Modified Sso Approach Based On Beta Distribution and Naturalmentioning

confidence: 99%

Social Spider Optimization Algorithm: Modifications, Applications, and Perspectives

Luque−Chang

Cuevas

Fausto

et al. 2018

Mathematical Problems in Engineering

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Swarm intelligence (SI) is a research field which has recently attracted the attention of several scientific communities. An SI approach tries to characterize the collective behavior of animal or insect groups to build a search strategy. These methods consider biological systems, which can be modeled as optimization processes to a certain extent. The Social Spider Optimization (SSO) is a novel swarm algorithm that is based on the cooperative characteristics of the social spider. In SSO, search agents represent a set of spiders which collectively move according to the biological behavior of the colony. In most of SI algorithms, all individuals are modeled considering the same properties and behavior. In contrast, SSO defines two different search agents: male and female. Therefore, according to the gender, each individual is conducted by using a different evolutionary operation which emulates its biological role in the colony. This individual categorization allows reducing critical flaws present in several SI approaches such as incorrect exploration-exploitation balance and premature convergence. After its introduction, SSO has been modified and applied in several engineering domains. In this paper, the state of the art, improvements, and applications of the SSO are reviewed.

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“…The SSO algorithm is different from most existing swarm algorithms which model individuals as unisex entities exhibiting virtually the same behavior [48]. The SSO algorithm has three steps [47][48][49][50][51] including initializing the population, cooperative operators, and mating operators. In the SSO algorithm, the search space is modeled as a spider's web; the optimization of the problem is equivalent to finding the final position of the spider after the collaboration.…”

Section: Social Spider Optimization Algorithmsmentioning

confidence: 99%

Community Detection Based on Differential Evolution Using Social Spider Optimization

Wang

Xue-jun

et al. 2017

Symmetry

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Community detection (CD) has become an important research direction for data mining in complex networks. Evolutionary algorithm-based (EA-based) approaches, among many other existing community detection methods, are widely used. However, EA-based approaches are prone to population degradation and local convergence. Developing more efficient evolutionary algorithms thus becomes necessary. In 2013, Cuevas et al. proposed a new differential evolution (DE) hybrid meta-heuristic algorithm based on the simulated cooperative behavior of spiders, known as social spider optimization (SSO). On the basis of improving the SSO algorithm, this paper proposes a community detection algorithm based on differential evolution using social spider optimization (DESSO/CD). In this algorithm, the CD detection process is done by simulating the spider cooperative operators, marriage, and operator selection. The similarity of nodes is defined as local fitness function; the community quality increment is used as a screening criterion for evolutionary operators. Populations are sorted according to their contribution and diversity, making evolution even more different. In the entire process, a random cloud crossover model strategy is used to maintain population diversity. Each generation of the mating radius of the SSO algorithm will be adjusted appropriately according to the iterative times and fitness values. This strategy not only ensures the search space of operators, but also reduces the blindness of exploration. On the other hand, the multi-level, multi-granularity strategy of DESSO/CD can be used to further compensate for resolution limitations and extreme degradation defects based on modular optimization methods. The experimental results demonstrate that the DESSO/CD method could detect the community structure with higher partition accuracy and lower computational cost when compared with existing methods. Since the application of the SSO algorithm in CD research is just beginning, the study is competitive and promising.

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Modified Social-Spider Optimization Algorithm Applied to Electromagnetic Optimization

Cited by 48 publications

References 11 publications

Design a Different Structures Controller for Controlled Systems Using a Spiking Neural Network

Design a Different Structures Controller for Controlled Systems Using a Spiking Neural Network

Social Spider Optimization Algorithm: Modifications, Applications, and Perspectives

Community Detection Based on Differential Evolution Using Social Spider Optimization

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