A novel multi-hybrid differential evolution algorithm for optimization of frame structures.

Differential evolution (DE) is a robust optimizer designed for solving complex domain research problems in the computational intelligence community. In the present work, a multi-hybrid DE (MHDE) is proposed for improving the overall working capability of the algorithm without compromising the solution quality. Adaptive parameters, enhanced mutation, enhanced crossover, reducing population, iterative division and Gaussian random sampling are some of the major characteristics of the proposed MHDE algorithm. Firstly, an iterative division for improved exploration and exploitation is used, then an adaptive proportional population size reduction mechanism is followed for reducing the computational complexity. It also incorporated Weibull distribution and Gaussian random sampling to mitigate premature convergence. The proposed framework is validated by using IEEE CEC benchmark suites (CEC 2005, CEC 2014 and CEC 2017). The algorithm is applied to four engineering design problems and for the weight minimization of three frame design problems. Experimental results are analysed and compared with recent hybrid algorithms such as laplacian biogeography based optimization, adaptive differential evolution with archive (JADE), success history based DE, self adaptive DE, LSHADE, MVMO, fractional-order calculus-based flower pollination algorithm, sine cosine crow search algorithm and others. Statistically, the Friedman and Wilcoxon rank sum tests prove that the proposed algorithm fares better than others

Evolutionary modelling of the COVID-19 pandemic in fifteen most affected countries.

COVID-19 or SARS-Cov-2, affecting 6 million people and more than 300,000 deaths, the global pandemic has engulfed more than 90% countries of the world. The virus started from a single organism and is escalating at a rate of 3% to 5% daily and seems to be a never ending process. Understanding the basic dynamics and presenting new predictions models for evaluating the potential effect of the virus is highly crucial. In present work, an evolutionary data analytics method called as Genetic programming (GP) is used to mathematically model the potential effect of coronavirus in 15 most affected countries of the world. Two datasets namely confirmed cases (CC) and death cases (DC) were taken into consideration to estimate, how transmission varied in these countries between January 2020 and May 2020. Further, a percentage rise in the number of daily cases is also shown till 8 June 2020 and it is expected that Brazil will have the maximum rise in CC and USA have the most DC. Also, prediction of number of new CC and DC cases for every one million people in each of these countries is presented. The proposed model predicted that the transmission of COVID-19 in China is declining since late March 2020; in Singapore, France, Italy, Germany and Spain the curve has stagnated; in case of Canada, South Africa, Iran and Turkey the number of cases are rising slowly; whereas for USA, UK, Brazil, Russia and Mexico the rate of increase is very high and control measures need to be taken to stop the chains of transmission. Apart from that, the proposed prediction models are simple mathematical equations and future predictions can be drawn from these general equations. From the experimental results and statistical validation, it can be said that the proposed models use simple linkage functions and provide highly reliable results for time series prediction of COVID-19 in these countries

IN VITRO PLANT REGENERATION STUDIES USING HYPOCOTYL EXPLANT OF BRINJAL (SOLANUM MELONGENA)

The present study was done to standardize the protocol of embryo rescue technique in which the seeds of indigenous collection of Solanum melongena were used as explants. The explants hypocotyle was cultured on MS media supplemented with different combinations of auxins and cytokinins such as BAP (1 to 2.5 mg/l)+ IAA (0.2 to 0.4 mg/l) and Kinnetin (1 to 2.5 mg/l)+ IAA (0.2 to 0.4 mg/l) for shoot regeneration. Callus initiation and shoot initiation was observed after 15 - 20 days and 25days of inoculation, respectively on media supplemented with BAP-IAA. While, it took 25 and 30-35 days for callus initiation and shoot initiation, respectively on media supplemented with Kinnetin-IAA. The highest shoot regeneration (65.12%) was observed on MS medium supple- mented with 2mg/l BAP+ 0.3mg/l IAA after 25 days of inoculation. For root regeneration, regenerated shoots were transferred to root regeneration medium having MS medium supplemented with different concentration of auxins IAA (0.5-1mg/l) and IBA (0.5-1mg/l), so as to obtain the complete plantlets. The highest root regeneration was observed when MS medium was supplemented with IBA (1mg/lt). Hence the plant regeneration from hypocotyle of brinjal was observed to be influenced by different combinations and concentrations of auxins and cytokinins

RGN: A Triple Hybrid Algorithm for Multi-level Image Segmentation with Type II Fuzzy Sets

This paper presents a study focused on enhancing the effectiveness of cuckoo search (CS). The goal is to improve its performance in avoiding local optima, improve the exploration and exploit potentially new solutions. To achieve this, we incorporate three additional algorithms – grey wolf optimizer (GWO), red panda optimization (RPO), and naked mole rat algorithm (NMRA) – into the basic CS framework to strengthen its exploration and exploitation capabilities. The resulting hybrid algorithm is named RGN, standing for red panda, grey wolf and naked mole-rat. To make the parameters of the RGN algorithm adaptable, six new mutation operators and inertia weights are added to the proposed RGN algorithm. The proposed algorithm is tested on CEC 2005, CEC 2014, and CEC 2022 benchmark problems to prove its effectiveness. Friedman test and Wilcoxon rank-sum tests, are done to analyse the significance of the proposed RGN algorithm statistically. It has been found that the proposed RGN is significantly better with respect to LSHADE-SPACMA, SaDE, SHADE, CMA-ES, extended GWO, hierarchical learning particle swarm optimization (FHPSO), Kepler optimization algorithm (KOA), improved chef-based optimization algorithm (CBOADP), improved symbiotic herding optimization (IMEHO), blended-biogeography based optimization (B-BBO), and Laplacian BBO (LX-BBO), among others. Application of the proposed algorithm RGN for Multilevel Image Thresholding with Type II Fuzzy Sets, shows that it is better than other algorithms over various performance matrices including mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similitude index (SSIM). Experimentally and statistically, it has been proved that the proposed RGN algorithm can be considered as a better alternative for optimization research

In Vitro Doubled Haploid Production of Bacterial Blight Resistant Plants from BC2F1 Plants (Ranbir Basmati X Pau148) Through Anther Culture

Doubled haploid plants are very important for the development of complete homozygous plants from heterozygous parents in one generation as they possess duplicate copy of haploid chromosome. Haploid production is easily obtained from in vitro anther culture. The present study was undertaken with the objective to develop doubled haploids using anthers for in vitro induction of callus on N6 medium supplemented with various combinations and concentrations of 2,4-dichlorophenoxy acetic acid (2,4-D) (0.5-2.5 mg/L), Kinetin (0.5-1.0 mg/L) and Naphthalene acetic acid (NAA) (2.0 mg/L) as callus induction medium (CIM). The highest callus induction frequency was obtained when N6 medium fortified with 2,4-D (2.5 mg/L), Kinetin (0.5 mg/L) and NAA (2 mg/L) of 10.07 per cent. The induced callus was sub cultured for shoot regeneration on Murashige and Skoog medium (MS) supplemented with growth regulators: Kinetin and NAA (0.5 mg/L each) in combination with BAP (0.0 - 2.5 mg/L). MS medium supplemented with NAA (0.5 mg/L), Kinetin (0.5 mg/L) and BAP (1.5 mg/L) was most responsive exhibiting regeneration frequency of 28.1 per cent which resulted in maximum regeneration of green plantlets and only 5.21 per cent of albinos. Individual plantlets were separated and immersed in liquid MS medium augmented with NAA (0.5-1.0 mg/L) and BAP (0.5-1.0 mg/L). Maximum rooting was observed in MS medium with NAA (0.5 mg/L) and BAP (1.0 mg/L). The survival rate of in-vitro raised plants was 51.51 per cent. Of these surviving plants, 21 plants were observed to have the sterility percentage above 50 percent and hence can be considered as the doubled haploid plants. Plant DH8 is susceptible and DH20 is heterozygous for gene Xa21. Two plants are susceptible for gene xa1

In Vitro Doubled Haploid Production of Bacterial Blight Resistant Plants from BC2F1 Plants (Ranbir Basmati X Pau148) Through Anther Culture

Doubled haploid plants are very important for the development of complete homozygous plants from heterozygous parents in one generation as they possess duplicate copy of haploid chromosome. Haploid production is easily obtained from in vitro anther culture. The present study was undertaken with the objective to develop doubled haploids using anthers for in vitro induction of callus on N6 medium supplemented with various combinations and concentrations of 2,4-dichlorophenoxy acetic acid (2,4-D) (0.5-2.5 mg/L), Kinetin (0.5-1.0 mg/L) and Naphthalene acetic acid (NAA) (2.0 mg/L) as callus induction medium (CIM). The highest callus induction frequency was obtained when N6 medium fortified with 2,4-D (2.5 mg/L), Kinetin (0.5 mg/L) and NAA (2 mg/L) of 10.07 per cent. The induced callus was sub cultured for shoot regeneration on Murashige and Skoog medium (MS) supplemented with growth regulators: Kinetin and NAA (0.5 mg/L each) in combination with BAP (0.0 - 2.5 mg/L). MS medium supplemented with NAA (0.5 mg/L), Kinetin (0.5 mg/L) and BAP (1.5 mg/L) was most responsive exhibiting regeneration frequency of 28.1 per cent which resulted in maximum regeneration of green plantlets and only 5.21 per cent of albinos. Individual plantlets were separated and immersed in liquid MS medium augmented with NAA (0.5-1.0 mg/L) and BAP (0.5-1.0 mg/L). Maximum rooting was observed in MS medium with NAA (0.5 mg/L) and BAP (1.0 mg/L). The survival rate of in-vitro raised plants was 51.51 per cent. Of these surviving plants, 21 plants were observed to have the sterility percentage above 50 percent and hence can be considered as the doubled haploid plants. Plant DH8 is susceptible and DH20 is heterozygous for gene Xa21. Two plants are susceptible for gene xa1

A Contemporary Systematic Review on Meta-heuristic Optimization Algorithms with Their MATLAB and Python Code Reference

Optimization is a method which is used in every field, such as engineering, space, finance, fashion market, mass communication, travelling, and also in our daily activities. In every field, everyone always wants to minimize or maximize something called the objective function. Traditional and modern optimization techniques or Meta-Heuristic (MH) optimization techniques are used to solve the objective functions. But the traditional optimization techniques fail to solve the complex and real-world optimization problem consisting of non-linear objective functions. So many modern optimization techniques have been proposed exponentially over the last few decades to overcome these challenges. This paper discusses a brief review of the different benchmark test functions (BTFs) related to existing MH optimization algorithms (OA). It discusses the classification of MH algorithms reported in the literature regarding swarm-based, human-based, physics-based, and evolutionary-based methods. Based on the last half-century literature, MH-OAs are tabulated in terms of the proposed year, author, and inspiration agent. Furthermore, this paper presents the MATLAB and python code web-link of MH-OA. After reading this review article, readers will be able to use MH-OA to solve challenges in their field

Positivity-Preserving Rational Cubic Fractal Interpolation Function Together with Its Zipper Form

In this paper, a novel class of rational cubic fractal interpolation function (RCFIF) has been proposed, which is characterized by one shape parameter and a linear denominator. In interpolation for shape preservation, the proposed rational cubic fractal interpolation function provides a simple but effective approach. The nature of shape preservation of the proposed rational cubic fractal interpolation function makes them valuable in the field of data visualization, as it is crucial to maintain the original data shape in data visualization. Furthermore, we discussed the upper bound of error and explored the mathematical framework to ensure the convergence of RCFIF. Shape parameters and scaling factors are constraints to obtain the desired shape-preserving properties. We further generalized the proposed RCFIF by introducing the concept of signature, giving its construction in the form of a zipper-rational cubic fractal interpolation function (ZRCFIF). The positivity conditions for the rational cubic fractal interpolation function and zipper-rational cubic fractal interpolation function are found, which required a detailed analysis of the conditions where constraints on shape parameters and scaling factor lead to the desired shape-preserving properties. In the field of shape preservation, the proposed rational cubic fractal interpolation function and zipper fractal interpolation function both represent significant advancement by offering a strong tool for data visualization.</jats:p

A Novel Approach to Predict the Asian Exchange Stock Market Index Using Artificial Intelligence

This study uses real-world illustrations to explore the application of deep learning approaches to predict economic information. In this, we investigate the effect of deep learning model architecture and time-series data properties on prediction accuracy. We aim to evaluate the predictive power of several neural network models using a financial time-series dataset. These models include Convolutional RNNs, Convolutional LSTMs, Convolutional GRUs, Convolutional Bi-directional RNNs, Convolutional Bi-directional LSTMs, and Convolutional Bi-directional GRUs. Our main objective is to utilize deep learning techniques for simultaneous predictions on multivariable time-series datasets. We utilize the daily fluctuations of six Asian stock market indices from 1 April 2020 to 31 March 2024. This study’s overarching goal is to evaluate deep learning models constructed using training data gathered during the early stages of the COVID-19 pandemic when the economy was hit hard. We find that the limitations prove that no single deep learning algorithm can reliably forecast financial data for every state. In addition, predictions obtained from solitary deep learning models are more precise when dealing with consistent time-series data. Nevertheless, the hybrid model performs better when analyzing time-series data with significant chaos

A robust automatic generation control system based on hybrid Aquila Optimizer-Sine Cosine Algorithm

Data availability: Data will be made available on request.Additional material is available online at: https://www.sciencedirect.com/science/article/pii/S0166046224001145#appendix .The fluctuating frequency in a power grid is the major stability challenge duo to the unpredictable power demand of costumers during the time. To address this issue, automatic generation controller (AGC) is employed. The AGC based on a proportional integral derivative (PID) approach is popularly utilised owing to its soft implementation and lower expenditure. However, it ripples to handle the standard frequency of a multi-area power grid that occurs in a competitive load-demand case, because of the high sensitivity of its uncertain parameters. In this paper, a Hybrid Aquila Optimizer-Sine Cosine algorithm (HSCAO) is designed for addressing the sensitivity of the PID-AGC parameters specifically for the multi-area power system network. The suggested algorithm is assessed based on CEC-2019, and classical benchmark issues with various dimensions to validate its performance and address the better fits of the algorithm parameters adequately. Also, a statistical analysis technique is conducted using Wilcoxon's test and Friedman test to demonstrate the supervise performance of the HSCAO optimisation regarding to other relative optimal algorithms. A two-area power system network is simulated using MATLAB environment to implement the proposed AGC system. The outcomes prove that the optimal PID-AGC method based on HSCAO technique demonstrates its ability to address the simple and complex fluctuations of load demands quickly. Also, it is the most robust to supervise the frequency response under fault condition test, resulting in, achieving the lowest ITAE index of 5.2s compared to the conventional fuzzy logic control-AGC and the conventional PID-AGC of 10.9s and 17.4s respectively