A Hybrid Meta-Heuristic Feature Selection Method Using Golden Ratio and Equilibrium Optimization Algorithms for Speech Emotion Recognition

Speech is the most important media of expressing emotions for human beings. Thus, it has often been an area of interest to understand the emotion of a person out of his/her speech by using the intelligence of the computing devices. Traditional machine learning techniques are very much popular in accomplishing such tasks. To provide a less expensive computational model for emotion classification through speech analysis, we propose a meta-heuristic feature selection (FS) method using a hybrid of Golden Ratio Optimization (GRO) and Equilibrium Optimization (EO) algorithms, which we have named as Golden Ratio based Equilibrium Optimization (GREO) algorithm. The optimally selected features by the model are fed to the XGBoost classifier. Linear Predictive Coding (LPC) and Linear Prediction Cepstral Coefficients (LPCC) based features are considered as the input here, and these are optimized by using the proposed GREO algorithm. We have achieved impressive recognition accuracies of 97.31% and 98.46% on two standard datasets namely, SAVEE and EmoDB respectively. The proposed FS model is also found to perform better than their constituent algorithms as well as many well-known optimization algorithms used for FS in the past. Source code of the present work is made available at: https://github.com/arijitdey1/Hybrid-GREO

Modern optimal controllers for hybrid active power filter to minimize harmonic distortion

Nowadays, AC distributed power networks are facing many challenges in guaranteeing and improving the required level of power quality indices in power networks with increasing nonlinear, time-variable and unbalanced loads. Power networks can benefit from avoiding and minimizing different AC problems, such as frequency fluctuation and Total Harmonic Distortions (THDs), by using power filters, such as Hybrid Active Power Filters (HAPFs). Therefore, attention towards responsible power quality indices, such as Total Harmonic Distortion (THD), Power Factor (P.F) and Harmonic Pollution (HP) has increased. THD and HP are important indices to show the level of power quality at the network. In this paper, modern optimization techniques have been employed to optimize HAPF parameters, and minimize HP, by using a nature-inspired optimization algorithm, namely, Whale Optimization Algorithm (WOA). The WOA algorithm is compared to the most competitive powerful metaheuristic optimization algorithms: Manta Ray Foraging Optimization (MRFO), Artificial Ecosystem-based Optimization (AEO) and Golden Ratio Optimization Method (GROM). In addition, the WOA, and the proposed modern optimization algorithms, are compared to the most competitive metaheuristic optimization algorithm for HAPF from the literature, called L-SHADE. The comparison results show that the WOA algorithm outperformed all other optimization algorithms, in terms of minimizing harmonic pollution, through optimizing parameters of HAPF; therefore, this paper aims to present the WOA as a powerful control model for HAPF

Modern Optimal Controllers for Hybrid Active Power Filter to Minimize Harmonic Distortion

Nowadays, AC distributed power networks are facing many challenges in guaranteeing and improving the required level of power quality indices in power networks with increasing nonlinear, time-variable and unbalanced loads. Power networks can benefit from avoiding and minimizing different AC problems, such as frequency fluctuation and Total Harmonic Distortions (THDs), by using power filters, such as Hybrid Active Power Filters (HAPFs). Therefore, attention towards responsible power quality indices, such as Total Harmonic Distortion (THD), Power Factor (P.F) and Harmonic Pollution (HP) has increased. THD and HP are important indices to show the level of power quality at the network. In this paper, modern optimization techniques have been employed to optimize HAPF parameters, and minimize HP, by using a nature-inspired optimization algorithm, namely, Whale Optimization Algorithm (WOA). The WOA algorithm is compared to the most competitive powerful metaheuristic optimization algorithms: Manta Ray Foraging Optimization (MRFO), Artificial Ecosystem-based Optimization (AEO) and Golden Ratio Optimization Method (GROM). In addition, the WOA, and the proposed modern optimization algorithms, are compared to the most competitive metaheuristic optimization algorithm for HAPF from the literature, called L-SHADE. The comparison results show that the WOA algorithm outperformed all other optimization algorithms, in terms of minimizing harmonic pollution, through optimizing parameters of HAPF; therefore, this paper aims to present the WOA as a powerful control model for HAPF

The Using of Conspicuous of Body Angularities Type Traits to Milk Yields as Dairy Cattle Selection Preferences

Numerous papers have been published on body cattle angularities subject in a few backward decades. However, the preeminent body angularities to the milk yield are unstipulated assertively. Hence, the current odyssey was to determine the transcendence body angular of dairy cattle interrelated with the milk yield for selection preferences. In total, 121 head of Holstein cows and seven reputable cattle body angularities were engaged as samples and measured variables for investigation. The software R version 4.2.1 and RStudio was operated simultaneously to facilitate statistical analysis. Later, the principal components (PCA), correlation, and regression analysis were carried out in that order. The PCA specified the thurl angle (TLA), hock side view angle (HSA), hock back views angle (HBA), and fore udder angle (FUA) as crucial factors of body cattle angularities. Then, the correlation analysis appointed HBA and TLA in series as the best trait related to milk yields. The regression analysis was merely entrusted to the HBA as a factor for prognosticating milk yield potency. Thus, the upshot of the ongoing exploration prompted the HBA as the main priority for milk yield selection preferences, followed by TLA. Both were usable on the calf, heifer, and cow selection scheme but should be enforced regularly

Hybridization of modified sine cosine algorithm with tabu search for solving quadratic assignment problem

Sine Cosine Algorithm (SCA) is a population-based metaheuristic method that widely used to solve various optimization problem due to its ability in stabilizing between exploration and exploitation. However, SCA is rarely used to solve discrete optimization problem such as Quadratic Assignment Problem (QAP) due to the nature of its solution which produce continuous values and makes it challenging in solving discrete optimization problem. The SCA is also found to be trapped in local optima since its lacking in memorizing the moves. Besides, local search strategy is required in attaining superior results and it is usually designed based on the problem under study. Hence, this study aims to develop a hybrid modified SCA with Tabu Search (MSCA-TS) model to solve QAP. In QAP, a set of facilities is assigned to a set of locations to form a one-to-one assignment with minimum assignment cost. Firstly, the modified SCA (MSCA) model with cost-based local search strategy is developed. Then, the MSCA is hybridized with TS to prohibit revisiting the previous solutions. Finally, both designated models (MSCA and MSCA-TS) were tested on 60 QAP instances from QAPLIB. A sensitivity analysis is also performed to identify suitable parameter settings for both models. Comparison of results shows that MSCA-TS performs better than MSCA. The percentage of error and standard deviation for MSCA-TS are lower than the MSCA which are 2.4574 and 0.2968 respectively. The computational results also shows that the MSCA-TS is an effective and superior method in solving QAP when compared to the best-known solutions presented in the literature. The developed models may assist decision makers in searching the most suitable assignment for facilities and locations while minimizing cost

Optimal controllers and configurations of 100% PV and energy Storage systems for a microgrid : the case study of a small town in Jordan

Renewable energy systems such as Photovoltaic (PV) have become one of the best options for supplying electricity at the distribution network level. This is mainly because the PV system is sustainable, environmentally friendly, and is a low-cost form of energy. The intermittent and unpredictable nature of renewable energy sources which leads to a mismatch between the power generation and load demand is the challenge to having 100% renewable power networks. Therefore, an Energy Storage System (ESS) can be a significant solution to overcome these challenges and improve the reliability of the network. In Jordan, the energy sector is facing a number of challenges due to the high energy-import dependency, high energy costs, and the inadequate electrification of rural areas. In this paper, the optimal integration of PV and ESS systems is designed and developed for a distribution network in Jordan. The economic and energy performance of the network and a proposed power network under different optimization algorithms and power network operation scenarios are investigated. Metaheuristic optimization algorithms, namely: Golden Ratio Optimization Method (GROM) and Particle Swarm Optimization (PSO) algorithms, are employed to find the optimal configurations and integrated 100% PV and ESS for the microgrid