Social spiders optimization and flower pollination algorithm for multilevel image thresholding: A performance study

Ouadfel, Salima; Taleb‐Ahmed, Abdelmalik

doi:10.1016/j.eswa.2016.02.024

Cited by 137 publications

(55 citation statements)

References 54 publications

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“…In the early stage of SSO, spiders fall easily into local exploration because of a lack of prior information [51]. Therefore, we should increase the dynamics of the global optimal individuals and stochastic learning, so as to maintain the population diversity and avoid the algorithm being trapped in local convergence.…”

Section: Improvement Of Differential Evolution Cooperative Operatorsmentioning

confidence: 99%

“…Compared to existing EAs, SSO is more competitive [47,48,51,52]. Cuevas has experimentally tested SSO, considering 19 benchmark functions, and using comparisons with PSO and ABC algorithm performance.…”

Section: Social Spider Optimization Algorithmsmentioning

confidence: 99%

“…In recent works, SSO has been used to solve different sorts of engineering problems. A multilevel image threshold is proposed based on the SSO algorithm [51]. The SSO is used in solving high-dimensional dataset clustering [52].…”

Section: Social Spider Optimization Algorithmsmentioning

confidence: 99%

“…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%

“…In the later stage, we should enhance the local search ability of the population and reduce the matching radius to ensure the fine exploration of local community and further optimize classification accuracy [50]. On the other hand, for poor individuals, we should increase the global search to move closer to the nearest optimal community, while elite individuals should try to retain good genes and strengthen the local search [22,36,51]. To avoid population degradation, we propose a random cloud crossover model to disrupt the population strategy.…”

Section: Improvement Of Differential Evolution Cooperative Operatorsmentioning

confidence: 99%

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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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Section: Improvement Of Differential Evolution Cooperative Operatorsmentioning

confidence: 99%

Section: Social Spider Optimization Algorithmsmentioning

confidence: 99%

Section: Social Spider Optimization Algorithmsmentioning

confidence: 99%

Section: Social Spider Optimization Algorithmsmentioning

confidence: 99%

Section: Improvement Of Differential Evolution Cooperative Operatorsmentioning

confidence: 99%

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Community Detection Based on Differential Evolution Using Social Spider Optimization

Wang

Xue-jun

et al. 2017

Symmetry

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Toward text psychology analysis using social spider optimization algorithm

Hosseinalipour

Gharehchopogh

Màsdàrí

et al. 2021

Concurrency and Computation

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Different nature‐inspired meta‐heuristic algorithms have been proposed to solve optimization problems. One of these algorithms is called social spider optimization (SSO) algorithm. Spiders' natural behaviors have inspired them to find the bait position by detecting vibrations in their web. Although the SSO algorithm has good accuracy in achieving optimal solutions, it suffers from a low convergence rate. In this paper, we attempted to improve SSO by changing its motion and mating parameters. To provide a practical example of using the new proposed algorithm, we based it on multi‐objective opposition‐based SSO, named MOPSSO. We used this algorithm in a feature selection process for analyzing text psychology, which is a multi‐objective problem. Textual psychology analysis is used in various fields, including collecting and analyzing people's views on various products, topics, social and political events. After selecting features, in order to classify the text, we used a new hybrid method that hybrids fuzzy C‐MEANS data clustering technique, a decision tree (DT), and Naïve Bayes (NB). Experimental results show that the improved SSO algorithm performs better than SSO, social spider algorithm, and CMA‐ES algorithms. Additionally, the performance of the proposed hybrid classification method is better than those of NB and DT.

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CCGPA‐MPPT: Cauchy preferential crossover‐based global pollination algorithm for MPPT in photovoltaic system

Sundararaj

Anoop

Dixit

et al. 2020

Progress in Photovoltaics

176

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In general, the photovoltaic (PV) is considered as the best selection among renewable energy resources due to its nonpolluted operation and good flexibility condition. The PV system is affected because of the partial shading conditions (PSCs), which reduce the generated power. During steady-state operating conditions, there occurs a time delay in tracking the Global maximum power point (GMPP) and Local maximum power point (LMPP) under PSCs using the perturb and observe (P&O) method. In order to overcome such shortcomings, this paper proposed a hybrid algorithm with a P&O technique to improve the maximum power point tracking (MPPT) for the PV system under PSC. In addition to this, the P&O technique is utilized to achieve the LMPP in the first section, and the hybrid algorithm is utilized to achieve the GMPP in the second section. Here, the hybrid technique is the integration of Cauchy preferential crossover (CC) with the flower pollination algorithm (FPA). Furthermore, the exploitation ability of the FPA is enhanced by the CC, and the combined hybrid algorithm has the ability to produce the optimal duty cycle for the DC-DC boost converter for MPPT. Then the proposed method will be executed in MATLAB/Simulink model, and it is contrasted with the existing methods such as CC, current sensorless (CS), and FPA, respectively. The experimental results and analysis reveal that the proposed approach provides better performances when compared with several other metaheuristic algorithms. K E Y W O R D S crossover, DC to DC boost converter, global maximum power point, global pollination, local maximum power point, photovoltaic, PSC 1 | INTRODUCTION Due to the rapid growth and expansion in business as well as the consistently increasing refinement of existing ways of living, the world supply energy is exposed to a huge strain. This wonder creates uncertainties over energy security as well as environmental sustainability. 1,2 Besides, the problem of climatic changes and it is necessary to reduce carbon impressions has added to the solid force for organizations and countries to put resources into elective energy sources, especially renewable energy (RE). Solar energy is a standout among the most vital RE sources, rather than regular unrenewable assets, for example, fuel and coal. 3 Solar energy is spotless, limitless, and free. The primary utilization of photovoltaic (PV) frameworks is said to be a stand-alone (military, street and residential lighting, electric vehicles space applications, and water pumping). Despite these

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Social spiders optimization and flower pollination algorithm for multilevel image thresholding: A performance study

Cited by 137 publications

References 54 publications

Community Detection Based on Differential Evolution Using Social Spider Optimization

Community Detection Based on Differential Evolution Using Social Spider Optimization

Toward text psychology analysis using social spider optimization algorithm

CCGPA‐MPPT: Cauchy preferential crossover‐based global pollination algorithm for MPPT in photovoltaic system

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