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Chiller systems hold a critical role in upholding comfort and energy efficiency within commercial buildings. Precise prediction of chiller energy consumption is imperative for operational optimization and the reduction of energy expenditures. This paper introduces an innovative methodology that integrates deep learning (DL), specifically Fixed Forward Neural Networks (FFNN), with Teaching-Learning-Based Optimization (TLBO) to enhance the accuracy of chiller energy consumption forecasts. Drawing on a diverse dataset from a commercial building, encompassing vital input parameters such as Chilled Water Rate, Building Load, Cooling Water Temperature, Humidity, and Dew Point, the study conducts a comprehensive comparison of metaheuristic algorithms (Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Barnacles Mating Optimizer (BMO), Harmony Search Algorithm (HSA), Differential Evolution (DE), Ant Colony Optimization (ACO), and the latest RIME algorithm). TLBO's adept navigation of the intricate parameter space of DL yields highly precise predictions for chiller energy consumption. The study's outcomes underscore TLBO's potential, along with other metaheuristics, in optimizing DL and refining energy management practices in commercial buildings. This research significantly contributes to the evolving discourse on the symbiosis between DL, particularly FFNNs, and metaheuristic optimization, offering a robust framework for chiller energy consumption prediction, thereby advancing sustainability and cost-effectiveness in building operations. • Hybrid TLBO-DL is used in predicting the chiller energy consumption. • Real data from a commercial building is used for training and testing of TLBO-DL. • TLBO-DL shows potential in predicting chiller energy consumption compared to others. [ABSTRACT FROM AUTHOR]

· Consideration of reactive power tariffs in optimizing the sizing and siting of distributed generation systems (DGs) for improvement of efficiency. · Applying water flow optimization (WFO) method on the problem of optimal sizing and siting of DGs for the first time. · Taking into account power factor limitations for DGs capable of injecting both active and reactive power. · Examination of multi-objective functions, including DG allocation to minimize economic losses and annual active/reactive power loss. Distributed generation (DG) systems are gaining popularity because they generate electricity closer to the point of consumption. This reduces transmission and distribution losses and enhances system reliability. For the efficient and cost-effective use of these systems, optimal sizing and siting of DGs is crucial. This paper introduces a Water Flow Optimization (WFO)-based method for the optimal placement and sizing of DG systems capable of injecting both active and reactive power simultaneously. This study assesses both the technical and economic facets of the system, taking into account power losses and the comprehensive annual economic costs, including capital investment, deployment, operation, and maintenance. A significant aspect of this research is the DG units' capability to enhance voltage and minimize losses, factoring in the reactive power tariff. The findings show that the WFO method surpasses other optimization techniques, such as particle swarm optimization, whale optimization algorithm, genetic algorithm and gray wolf optimization. Furthermore, the study emphasizes the cost advantages of injecting reactive power. The minimum annual economic losses (AEL) recorded in this study are $19,207 and $13,852 for IEEE-33 and IEEE-69 bus systems, respectively. In comparison, for DGs only injecting active power, the AEL increases to $24,749 and $41,381 for the corresponding systems. [ABSTRACT FROM AUTHOR]

Due to the benefits of metaheuristic optimization techniques, in this paper, we introduced flower pollination optimization algorithms for finding optimal bands in airborne hyperspectral images and classification using a modified wavelet Gabor filter (MGFNet) convolutional neural network. The proposed flower pollination optimization algorithm has been investigated to select optimal bands from hyperspectral images with deep wavelet features, which are nonlinear, discriminant, and invariant. These bands are effective for hyperspectral image classification, object detection. Moreover, in demand to maintain the widespread issue of imbalanced samples for the classification of Hyperspectral image, we have selected only optimized bands rather than whole bands of hyperspectral images. More importantly, we proposed a modified Gabor based wavelet filter which helps to extract exact information from the spatial and spectral features of hyperspectral imagery. The proposed approaches are carried out on three hyperspectral datasets: Indian pines, Pavia University, and Salinas scene hyperspectral images. In addition, the proposed FPO and MGFNet open up a new frame for further research. [ABSTRACT FROM AUTHOR]

Enhancing the accuracy of breast cancer recurrence prediction is crucial, mainly when dealing with genomics data, which presents challenges such as high dimensionality, noise, non-linearity, and limited sample sizes. This paper introduces Semi-Supervised Survival Laplacian Regression (S3LR), a novel feature selection algorithm designed to improve breast cancer recurrence prediction by effectively handling censored, event, and unlabeled data. S3LR modifies the Laplacian Score (LS) by incorporating a distance matrix to calculate the weight matrix, and it integrates heuristic and metaheuristic optimization algorithms to optimize the weighted matrix. These enhancements refine feature selection and overall performance. In our evaluations using three datasets and comparisons with state-of-the-art techniques, S3LR combined with Particle Swarm Optimization (PSO) demonstrates significant improvements in C-index and mean absolute error (MAE). Average C-index values reach 68.80 %, 59.49 %, and 67.66 %, with average MAE values of 15.98, 7.87, and 8.65 months, respectively. These results showcase S3LR's effectiveness in predicting recurrence, even with censored data, for more precise and reliable outcomes. Furthermore, the framework's versatility extends beyond breast cancer and can readily be applied to address other survival and recurrence problems. [ABSTRACT FROM AUTHOR]

Flue gas water-alternating gas (flue gas-WAG) is a promising technique for enhancing oil production and reducing greenhouse gas emissions. The effective utilization of this technique relies on identifying the optimal factors, often determined through numerous numerical simulations. This paper introduces a cost-effective optimization framework that integrates machine learning models and diverse optimization algorithms to identify the best parameters for injecting flue gas-WAG into the "Gullfaks" reservoir. Robust Machine learning models, including Multilayer Perceptron, Cascade Forward Neural Network (CFNN), Radial Basis Function, and Generalized Regression Neural Network, were employed as proxy models. The CFNN model, with satisfactory agreement with actual data (average absolute relative error of 0.4543 % for oil recovery factor (RF) and 1.9366 % for CO 2 storage), is selected for optimization through metaheuristic algorithms, including Non-dominated sorting genetic algorithm version II (NSGA-II), Pareto envelope-based selection Algorithm version-II (PESA_II), Multi-objective particle swarm optimization (MOPSO), and Multi-objective gray wolf optimization (MOGWO(. Among the optimization algorithms, MOGWO outperforms others in terms of speed and accuracy, yielding Pareto-optimal solutions with an RF of 0.8284 and a CO 2 storage of 1.5314 × 107 (kg-mol). The proposed approach uses Pareto dominance for insightful field development planning, enabling decision-makers to choose flue gas-WAG parameters based on future circumstances. [Display omitted] • A robust optimization framework is proposed for flue gas storage and enhanced oil recovery. • NSGA-II, MOPSO, MOGWO and PESA-II algorithms are utilized to optimize the water-alternating flue gas process. • CFNN, MLP, RBFNN, and GRNN are developed as proxy models that imitate the numerical simulator. • CFNN outperforms all developed models with AAPRE of 0.4543 % and 1.9633 % to estimate oil recovery factor and CO 2 storage, respectively. • The created Pareto fronts allow flexible applicability conditions to examine the design factors of the process. [ABSTRACT FROM AUTHOR]

In this study, we propose an ultra-broadband, high-efficiency absorber based on the particle swarm optimization (PSO) algorithm. By coupling multi-layer planar structures with pyramid structures and optimizing structural parameters using code, the absorber can achieve efficient absorption in the ultra-broadband band. According to the simulation results, the average absorptance of the absorber exceeded 99.98% in the wavelength range from 300 nm to 2000 nm. Due to the gradual geometric structure of the pyramid, excellent anti-reflection ability can be achieved, and it has a high degree of symmetry, making the absorber insensitive to the incident angle and polarization state. The simulation results also show that the absorption efficiency can reach more than 90% when the incidence angle varies from 0° to 60°. Due to the high thermal and chemical stability of SiO 2 , Ti, and TiN materials, the absorber can be used in a wider range of fields. This study not only proposes an efficient absorption structure, but also provides an idea for the design optimization of absorbers. • Propose an ultra-broadband absorption structure based on Particle Swarm Optimization Algorithm. • The coupling of multi-layer planar structures and pyramid structures brings higher absorption. • The algorithm in this paper provides new strategies and ideas for future R&D. [ABSTRACT FROM AUTHOR]

Partial Shading Condition (PSC) is a common issue when implementing the Photovoltaic (PV) system. Conventional Maximum Power Point Tracking (MPPT) algorithms are not able to track the Global Maximum Power Point (GMPP) under PSC as the Power–Voltage (P–V) characteristic curve has multiple Local Maximum Power Points (LMPPs). In the literature, meta-heuristic algorithms have been implemented in the Global Maximum Power Point Tracking (GMPPT) applications. However, meta-heuristic algorithms often face initialization and parameter tuning issues. In this paper, a novel evolutionary-based method called Artificial Location Selection Optimization (ALSO) is proposed for GMMPT under PSC. The proposed ALSO replicates the artificial selection activity widely used in agriculture. Compared to the conventional Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), it does not involve random numbers so that the PSC issue can be solved effectively. In addition, our proposed algorithm is able to vary its tracking speed according to the complexity of the PSC. The performance of the ALSO has been tested by simulations and experiments by exploiting 252 non-repeating PSC test cases. Both simulations and experiments demonstrate that the ALSO algorithm performs better than the PSO and GA algorithms in terms of speed, efficiency, accuracy, power convergence, and duty convergence. • A novel maximum power point tracking algorithm called ALSO is proposed. • ALSO adeptly adjusts minimal tracking speeds to match PSC complexity. • ALSO outperforms PSO and GA in terms of accuracy, efficiency, and speed. • Particle powers and positions converge with increased accuracy and efficiency. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we present a strategy for integrating photovoltaic systems into power distribution networks to improve the technical, economic, and environmental aspects of these networks. Taking into account variations in photovoltaic generation and energy demand, the proposed strategy uses a mathematical model that is subject to the technical and operational constraints of the system under analysis. The proposed strategy employs a master–slave methodology based on metaheuristic algorithms, in which the master stage determines the optimal locations for the photovoltaic generators and the slave stage estimates the optimal power injection. We proposed three different configurations of the methodology, each using the Particle Swarm Optimization (PSO) algorithm for the slave stage and the following algorithms for the master stage: the Chu and Beasley's Genetic Algorithm (CBGA), the Monte Carlo (MC) algorithm, and the PSO algorithm. These proposed configurations were evaluated in two test scenarios tailored to the characteristics of two regions in Colombia. This allowed us to analyze the impact of the methodologies on grid-connected (urban network) and off-grid systems (rural network). According to the results, the configuration that used two PSO algorithms achieved the most favorable outcomes in the urban scenario, reducing energy losses by 32.88%, operating costs by 42.41%, and pollutant gas emissions by 40.37%. Similarly, the same configuration obtained the best results in the rural scenario, reducing energy losses by 22.12%, operating costs by 43.35%, and pollutant gas emissions by 41.94%. These results were obtained in a typical day of operation for each system. • A strategy for integrating photovoltaic energy into distribution electrical grids. • The strategy assesses distributed generation's impact on urban and rural grids. • 32.88% urban, 22.12% rural electrical loss reductions. • 42.41% urban, 43.35% rural operating cost reductions achieved • 40.37% urban, 41.94% rural CO 2 emission reductions achieved. [ABSTRACT FROM AUTHOR]

Proton exchange membrane fuel cell (PEMFC) models are conventionally established with a set of parameters identified under steady-state operating conditions. However, such an approach is insufficient to accurately capture the dynamic characteristics of multi-parameter changes in real-world scenarios. This paper develops a semi-empirical model for a 110-kW commercial PEMFC system based on its dynamic operation data to remedy the defects. To improve the fitting accuracy of the semi-empirical PEMFC model, an improved grey wolf optimization (IGWO) algorithm is proposed for model parameter identification. The IGWO algorithm adopts chaotic mapping to optimize the initial population distribution, and a random walk strategy is incorporated to boost the local search ability of the traditional grey wolf optimization (GWO) algorithm. The effectiveness of this IGWO algorithm in optimizing the semi-empirical model is experimentally verified on the 110-kW PEMFC system under highly dynamic operating conditions. Results show that the proposed IGWO algorithm can effectively identify the semi-empirical model's parameters, establishing a stable and robust model that outperforms those based on traditional metaheuristic algorithms such as GWO, particle swarm optimization, and genetic algorithm. The demonstrated improvement renders it as better suited for optimizing PEMFC semi-empirical models under real-world operating conditions. • Dynamic operating conditions. • Improved grey wolf optimization method. • Chaotic mapping and random walk strategy. [ABSTRACT FROM AUTHOR]

Multi-region combined heat and power economic dispatch (MRCHPED) is a brand new optimization issue in power system operation. The MRCHPED uses the multiple regional structure to integrate power-only units, CHP units and heat-only units to achieve optimal economic dispatch while ensuring the supply of heat and power within each region. In this paper, an educational achievement guided group teaching optimization algorithm (EAGTOA) is developed and applied to solve the MRCHPED problem. In EAGTOA, aiming to improve the search efficiency, two historical archives are introduced to collect the valuable information of educational achievements from students and teachers, and are used in the update process of the EAGTOA. The EAGTOA algorithm is applied to solve three MRCHPED problems, including a small-scale MRCHPED with 2 regions and 9 units, a medium-scale MRCHPED with 3 regions and 17 units, and a large-scale MRCHPED with 3 regions and 96 units. The performance of EAGTOA is compared with several classical and recently-developed optimization algorithms. The simulation results show that the proposed EAGTOA can achieve better results in terms of solution accuracy and convergence speed for the MRCHPED problems. • Mathematical model of multi-region CHP economic dispatch (MRCHPED) is provided. • New group teaching optimization algorithm called EAGTOA is developed. • The EAGTOA is utilized to solve three MRCHPED with 9,17 and 96 units. • Simulation results show that EAGTOA achieves excellent results for the MRCHPED. [ABSTRACT FROM AUTHOR]

Cloud data center comprises various physical and virtual machines, alongside storage datacenter services provided by cloud providers. Effectively mapping tasks to optimize resource utilization and load balance is essential for efficient task scheduling. This process, referred to as scheduling constraints, can significantly enhance overall efficiency. However, to harness the benefits of this scheduling, one must address the challenges arising during task execution. The interdependencies between tasks and the diverse resources available in the datacenter pose significant hurdles to efficient resource allocation. To address these challenges, this paper introduces the Hybrid HEFT-PSO-GA algorithm (HEPGA), aiming to efficiently allocate tasks to available resources across the datacenter. The HEPGA algorithm builds upon prior research by integrating the strengths of PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) to optimize task scheduling in cloud computing environments. Through the fusion of PSO, GA, and HEFT-based Initialization, the algorithm strives to efficiently allocate tasks to processors, thereby minimizing the makespan. This approach capitalizes on parallel processing capabilities to further enhance resource utilization in the cloud environment. By varying the weights of the fitness function and considering the resources within the datacenter, we meticulously analyze the algorithm's performance concerning both makespan and Resource Utilization (RU). The results of these tests underscore the algorithm's consistent and robust resource utilization across diverse weight configurations, highlighting its adaptability to varying priorities. Moreover, the observed variations in makespan performance based on different weights emphasize the algorithm's potential for excellence when tailored to specific optimization goals. [ABSTRACT FROM AUTHOR]