Political Optimizer: A novel socio-inspired meta-heuristic for global optimization

Askari, Qamar; Younas, Irfan; Saeed, Mehreen

doi:10.1016/j.knosys.2020.105709

Cited by 341 publications

(160 citation statements)

References 82 publications

Supporting

Mentioning

158

Contrasting

Unclassified

Order By: Relevance

“…In the second term and which concerns the execution time, BPO-V and BPO-S showed poor results according to Table 9. is fact can be explained by the complexity of the algorithm proposed in [31] and its large number of functions to execute and large number of conditions to verify.…”

Section: Resultsmentioning

confidence: 99%

“…For that, during this work, we use the Policy Optimizer (PO) [31] as the main resolution technique since it is a newly introduced metaheuristic which is human behavior-based algorithm. Moreover, as mentioned in [31], PO produces better solutions for dealing with optimization problems than other well-known metaheuristics in the literature. In this paper, a novel binary version is proposed to find the most representative subset of a given dataset.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Binary Political Optimizer for Feature Selection Using Gene Expression Data

Manita

Korbaa

2020

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

DNA Microarray technology is an emergent field, which offers the possibility of obtaining simultaneous estimates of the expression levels of several thousand genes in an organism in a single experiment. One of the most significant challenges in this research field is to select high relevant genes from gene expression data. To address this problem, feature selection is a well-known technique to eliminate unnecessary genes in order to ensure accurate classification results. This paper proposes a binary version of Political Optimizer (PO) to solve feature selection problem using gene expression data. Two transfer functions are used to design a binary PO. The first one is based on Sigmoid function and will be noted as BPO-S, while the second one is based on V-shaped function and will be noted as BPO-V. The proposed methods are evaluated using 9 biological datasets and compared with 8 binary well-known metaheuristics. The comparative results show the prevalent performance of the BPO methods especially BPO-V in comparison with other techniques.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Binary Political Optimizer for Feature Selection Using Gene Expression Data

Manita

Korbaa

2020

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…The proposed PO technique is considered as a physicsbased algorithm combined with swarm-based characteristics which are concerned mainly with finding global optima. The PO has been implemented by Q. Askari et al [25], and it has been inspired by the different phases of the Politics process. Politics process can be divided into two main attitudes: every individual optimizes its good-will to win the election, and every party attempt to find the maximum number of seats in parliament to form a government.…”

Section: Pomentioning

confidence: 99%

“…Politics process can be divided into two main attitudes: every individual optimizes its good-will to win the election, and every party attempt to find the maximum number of seats in parliament to form a government. However, the PO is created as a consequence of five-phases involving party formation and constituency allocation, election campaign, party switching, inter-party election, and parliamentary affairs [25].…”

Section: Pomentioning

confidence: 99%

See 1 more Smart Citation

Fuel Cell Parameters Estimation via Marine Predators and Political Optimizers

et al. 2020

View full text Add to dashboard Cite

Proton Exchange Membrane Fuel Cells (PEMFC) is considered a propitious solution for an environmentally friendly energy source. A precise model of PEMFC for accurate identification of its polarization curve and an in-depth understanding of all its operating characteristics attracted the interest of many researchers. In this paper, recent meta-heuristic optimization methods have been successfully applied to evaluate the unknown parameters of PEMFC models, particularly Marine Predators Algorithm (MPA) and Political Optimizer (PO) techniques. The proposed optimization algorithms have been tested on three different commercial PEMFC stacks, namely BCS 500-W, SR-12PEM 500 W, and 250 W stack under various operating conditions. The sum of square errors (SSE) between the results obtained by the application of the estimated parameters and the experimentally measured results of the fuel cell stacks was considered as the objective function of the optimization problem. In order to validate the effectiveness of the proposed methods, the results are compared with those obtained in the literature. Moreover, the I/V curves obtained by the application of MPA and PO showed a clear matching with datasheet curves for all the studied cases. Statistical analysis has been performed to evaluate the robustness of the MPA and PO techniques. Finally, the PEMFC model based on the MPA technique surpasses all compared algorithms in terms of the solution accuracy and the convergence speed. The obtained results confirmed the superiority and reliability of the applied approach of the MPA algorithm. The results prove that the MPA algorithm has a superior performance based on its reliability. INDEX TERMS Fuel cell modelling, Parameter estimation, metaheuristic algorithms.

show abstract

Attack detection and mitigation using optimal deep maxout network in cloud computing

Patil¹,

Patil²

2022

Adaptive Control & Signal

View full text Add to dashboard Cite

Summary Cloud computing is a promising technique and the majority of information technology based services function in this infrastructure, which addresses the need of today's data center. There involve privacy and security problems in the cloud, which affected the utility of the cloud. Various mitigation solutions are developed in existing, in which the attacks continue to elevate in frequency and severity. This calls for a model for addressing the needs of challenging security threats. This article devises novel attack detection and mitigation technology using an optimization‐driven deep learning model. Here, the system comprises five modules, like data, data owner, cloud server, cloud user, and thin provision virtual volume. Here, each entity performs its own operations. The proposed technique includes five stages, namely, authentication phase, data sharing and communication phase, recorded log file creation phase, attack detection phase, and attack mitigation phase. An initial phase is the authentication phase where an authentication protocol is modeled using different mathematical functionalities. Once the authentication is done, the data sharing and communication are initiated for sharing the sensitive data. Thereafter the log file creation phase is initiated for storing the log entries. Then the attack detection is performed utilizing deep maxout network that is tuned by the developed gray wolf political optimizer (GWPO). The GWPO is the integration of gray wolf optimizer (GWO) and political optimizer (PO). Finally, attack mitigation is performed by reducing the data rates of attack nodes. The developed GWPO‐enabled deep maxout network provided improved performance with the highest accuracy of 97.9%, true positive rate of 97.3%, and true negative rate of 97.7%.

show abstract

Political Optimizer: A novel socio-inspired meta-heuristic for global optimization

Cited by 341 publications

References 82 publications

Binary Political Optimizer for Feature Selection Using Gene Expression Data

Binary Political Optimizer for Feature Selection Using Gene Expression Data

Fuel Cell Parameters Estimation via Marine Predators and Political Optimizers

Attack detection and mitigation using optimal deep maxout network in cloud computing

Contact Info

Product

Resources

About