This paper proposes a multi-strategy, multiple algorithms based hybrid strategy using flower pollination algorithm (FPA), grey wolf optimizer (GWO), INFO, and naked mole rat algorithm (NMRA). The proposed algorithm, n...
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This paper proposes a multi-strategy, multiple algorithms based hybrid strategy using flower pollination algorithm (FPA), grey wolf optimizer (GWO), INFO, and naked mole rat algorithm (NMRA). The proposed algorithm, named the Flower Grey INFO Naked (FGIN) algorithm, incorporates the most effective equations from the FPA, GWO, INFO, and NMRA. Here FPA's basic structure following global and local search is used, GWO is meant for providing extensive exploration, whereas INFO and NMRA both contribute towards exploitative search. Dynamic iterative search and population segmentation strategies are incorporated for enhanced performance of FGIN algorithm. For enhanced self-adaptivity, six mutation weight operators are applied to the three parameters of the proposed strategy. FGIN is also subjected to higher dimensional and variable population analysis, and has been found to be highly effective. A deeper analysis using CEC 2005, CEC 2017, CEC 2019 and CEC 2022 benchmark data set is also performed to validate the superiority of the proposed algorithm with respect to success history based differential evolution (SHADE), self-adaptive DE (SaDE), NL-LSHADE-LBC, DE with active archive (JADE), LSHADE-SPACMA, evolutionary algorithms with eigen crossover (EA4eig), extended GWO (GWO-E), NL-LSHADE-RSP-MID, jDE100, and others. The proposed FIGN algorithm is then used for the parametric identification of proton exchange membranes in fuel cells (PEMFC). The optimization challenge of PEMFC is to minimize the sum of squared error (SSE) between the experimental and measured voltage. And also determine, the optimal values of seven unknown parameters for the PEMFC stack's. To illustrate the potential of the FGIN algorithm is validated by utilizing five well-known commercial PEMFCs, namely NedStack PS6, BCS 500 W, Stack 250 W, Ballard Mark V, and Horizon H-12 Stack. In order to check the effectiveness of the FGIN algorithm, both parametric and non-parametric statistical tests have been conduct
This study aims to develop an effective tool by integrating traditional analysis methods with advanced sensor array technology and multiple algorithms, to achieve rapid characterization, qualitative identification, an...
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This study aims to develop an effective tool by integrating traditional analysis methods with advanced sensor array technology and multiple algorithms, to achieve rapid characterization, qualitative identification, and flavor quality evaluation of fermented bean products. Firstly, the flavor compounds and flavor profiles of fermented bean curd from different manufacturers were identified using headspace solid-phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC/MS) and quantitative descriptive analysis (QDA). A total of 63 volatile compounds were identified, of which 13 volatile compounds were identified as the key differential compounds in selected samples. Eight descriptors were selected to represent the aroma characteristics of fermented bean curd. Based on the key differential compounds, a low-cost colorimetric sensor array (CSA) was designed and constructed. Furthermore, the feasibility of CSA combined with multiple algorithms was investigated for brand discrimination and flavor quality assessment. Linear discriminant analysis (LDA) exhibited a better performance in distinguishing different flavor quality samples, and the recognition accuracy of its prediction set was 97.22%. The study suggests that CSA combined with multiple algorithms can be an effective tool for flavor characterization and qualitative discrimination of fermented bean curd, and can provide a new multidimensional analysis method for food analysis.
This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wol...
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This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative division for extensive exploration and incorporates major parametric enhancements for improved exploitation operation. To counter the local optima problems, a stagnation phase using CS and GWO is added. Six new inertia weight operators have been analyzed to adapt algorithmic parameters, and the best combination of these parameters has been found. An analysis of the suitability of DHPN towards population variations and higher dimensions has been performed. For performance evaluation, the CEC 2005 and CEC 2019 benchmark data sets have been used. A comparison has been performed with differential evolution with active archive (JADE), self-adaptive DE (SaDE), success history based DE (SHADE), LSHADE-SPACMA, extended GWO (GWO-E), jDE100, and others. The DHPN algorithm is also used to solve the image fusion problem for four fusion quality metrics, namely, edge-based similarity index (Q(AB/F)), sum of correlation difference (SCD), structural similarity index measure (SSIM), and artifact measure (N-AB/F). The average Q(AB/F) = 0.765508, SCD = 1.63185, SSIM = 0.726317, and N-AB/F = 0.006617 shows the best combination of results obtained by DHPN with respect to the existing algorithms such as DCH, CBF, GTF, JSR and others. Experimental and statistical Wilcoxon's and Friedman's tests show that the proposed DHPN algorithm performs significantly better in comparison to the other algorithms under test.
This paper presents and discusses a method of generating encryption algorithms using neural networks and evolutionary computing. Based on the application of natural noise sources obtained from data that can include at...
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ISBN:
(纸本)9781479919611
This paper presents and discusses a method of generating encryption algorithms using neural networks and evolutionary computing. Based on the application of natural noise sources obtained from data that can include atmospheric noise (generated by radio emissions due to lightening, for example), radioactive decay, electronic noise and so on, we 'teach' a system to approximate the input noise with the aim of generating an output nonlinear function. This output is then treated as an iterator which is subjected to a range of tests to check for potential cryptographic strength in terms of metric such as a (relatively) large positive Lyapunov exponent, high information entropy, a high cycle length and key diffusion characteristics, for example. This approach provides the potential for generating an unlimited number of unique Pseudo Random Number Generator (PRNG) that can be used on a 1-to-1 basis. Typical applications include the encryption of data before it is uploaded onto the Cloud by a user that is provided with a personalized encryption algorithm rather than just a personal key using a 'known algorithm' that may be subject to a 'known algorithm attack' and/or is 'open' to the very authorities who are promoting its use.
We present a method of generating encryptors, in particular, Pseudo Random Number Generators (PRNG), using evolutionary computing. Working with a system called E_(ureqa), designed by the Cornell Creative Machines Lab,...
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ISBN:
(纸本)9781629935881
We present a method of generating encryptors, in particular, Pseudo Random Number Generators (PRNG), using evolutionary computing. Working with a system called E_(ureqa), designed by the Cornell Creative Machines Lab, we seed the system with natural noise sources obtained from data that can include atmospheric noise generated by radio emissions due to lightening, for example, radioactive decay, electronic noise and so on. The purpose of this is to 'force' the system to output a result (a nonlinear function) that is an approximation to the input noise. This output is then treated as an iterated function which is subjected to a range of tests to check for potential cryptographic strength in terms of a positive Lyapunov exponent, maximum entropy, high cycle length, key diffusion characteristics etc. This approach provides the potential for generating an unlimited number of unique PRNG that can be used on a 1-to-1 basis. Typical applications include the encryption of data before it is uploaded onto the Cloud by a user that is provided with a personalised encryption algorithm rather than just a personal key using a 'known algorithm' that may be subject to a 'known algorithm attack' and/or is 'open' to the very authorities who are promoting its use.
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