Intelligent Optimization Approach Research Articles

Intelligent decision-making approach for rapid optimization of double-wall cooling structures under varying cooling demands

Double-wall structures are widely employed for cooling turbine blades. When manufacturing materials and application conditions vary, the design of double-wall structures should be adapted to meet different cooling demands. However, because of inevitable repetitive iterations, conventional evolutionary algorithms exhibit low efficiency in optimizing double-wall structures when cooling demands change. This paper presents a reinforcement learning-based method to optimize double-wall cooling structures facing various cooling demands rapidly. A decision network established a forward mapping from cooling demands to optimized structural designs and was trained using historical decision-making experiences generated from numerical simulations. The historical experiences were quantified uniformly based on the cooling performance gains or losses resulting from changes in geometric parameters. The physical understanding that cooling performance evaluations differ with varying cooling demands was incorporated into the training dataset. The performance of the trained decision network was validated by varying the cooling optimization objectives. Results indicate that the decision network can achieve optimal double-wall cooling units that meet given objectives with a single decision. The decision process took 5 ms, nearly 90 times faster than traditional genetic algorithms. Moreover, a partitioning, multi-cycle application strategy for the decision network was employed to optimize double-wall cooling plates under non-uniform inlet temperature distributions, resulting in a 13.7K reduction in overall average temperature and improved radial temperature uniformity.

Generalized SmartScan: An Intelligent LPBF Scan Sequence Optimization Approach for Reduced Residual Stress and Distortion in Three-Dimensional Part Geometries

Abstract Laser powder bed fusion (LPBF) is an additive manufacturing technique that is gaining popularity for producing metallic parts in various industries. However, parts produced by LPBF are prone to residual stress, deformation, cracks, and other quality defects due to uneven temperature distribution during the LPBF process. To address this issue, in prior work, the authors have proposed SmartScan, a method for determining laser scan sequence in LPBF using an intelligent (i.e., model-based and optimization-driven) approach, rather than using heuristics, and applied it to simple 2D geometries. This paper presents a generalized SmartScan methodology that is applicable to arbitrary 3D geometries. This is achieved by (1) expanding the thermal model and optimization approach used in SmartScan to multiple layers, (2) enabling SmartScan to process shapes with arbitrary contours and infill patterns within each layer, (3) providing the optimization in SmartScan with a balance of exploration and exploitation to make it less myopic, and (4) improving SmartScan’s computational efficiency via model order reduction using singular value decomposition. Sample 3D test artifacts are simulated and printed using SmartScan in comparison with common heuristic scan sequences. Reductions of up to 92% in temperature inhomogeneity, 86% in residual stress, 24% in maximum deformation, and 50% in geometric inaccuracy were observed using SmartScan, without significantly sacrificing print speed. An approach for using SmartScan for printing complex 3D parts in practice, by integrating it as a plug-in to a commercial slicing software, was also demonstrated experimentally, along with its benefits in significantly improving printed part quality.

Optimization of operational parameters of marine methanol dual-fuel engine based on RSM-MOPSO

Methanol and natural gas, as green fuels for environmental protection, have shown great potential in the field of maritime transportation. This study explores how the methanol energy fraction (MEF), exhaust gas recirculation (EGR), excess air ratio (λ), and engine injection timing (EIT) influence combustion and emission traits in marine dual-fuel (DF) engine. Initially, a DF engine model was formulated through experiments using CONVERGE software, while the CHEMKIN program devised a chemical mechanism involving 100 compounds and 512 reactions. The response surface methodology (RSM) was subsequently utilized to design the experiment and develop the regression model. The improved multi-objective particle swarm optimization (MOPSO) algorithm was applied to optimize three critical variables: brake thermal efficiency (BTE), carbon monoxide (CO), and nitrogen oxide (NOx) emissions. Ultimately, feature-optimized scenarios from the Pareto front were chosen for comparative evaluation to derive the optimal configuration. The results show that when the decision variables MEF, EGR, λ and EIT are 22.33 %, 10 %, 1.81 and 11°CA BTDC respectively, there is a better trade-off between the three target variables. Compared with the original case, the change of ITE, NOx and CO emissions in the optimal case are +0.97 %, −39.53 % and −14.51 %, respectively. Overall, based on the intelligent optimization approach, the rational selection of DF engine parameters improved the ITE and most NOx and CO emissions were effectively suppressed.

Intelligent accounting optimization method based on meta-heuristic algorithm and CNN.

The evolution of social intelligence has led to the adoption of intelligent accounting practices in enterprises. To enhance the efficiency of enterprise accounting operations and improve the capabilities of accountants, we propose an intelligent accounting optimization approach that integrates meta-heuristic algorithms with convolutional neural networks (CNN). First, we enhance the CNN framework by incorporating document and voucher information into accounting audits, creating a multi-modal feature extraction mechanism. Utilizing these multi-modal accounting features, we then introduce a method for assessing accounting quality, which objectively evaluates financial performance. Finally, we propose an optimization technique based on meta-heuristic principles, combining genetic algorithms with annealing models to improve the accounting system. Experimental results validate our approach, demonstrating an accuracy of 0.943 and a mean average precision (mAP) score of 0.812. This method provides technological support for refining accounting audit mechanisms.

Thermo-enviro-economic analyses of a landfill biogas-fed polygeneration process combined with a liquefied natural gas cold energy utilization unit

Landfill biogas offers a promising opportunity to supply society’s energy requirements, following the waste-to-energy approach, which issuitable for sustainable production. However, structural modification for its utilization is the main gap, requiring additional research efforts. Considering this issue, the current study aims to design an eco-friendly polygeneration method for landfill biogas utilization for a cutting-edge heat recovery system that integrates a cold energy recovery and utilization unit for liquefied natural gas. The liquefied natural gas recovery section, the organic Rankine cycle with a zeotropic mixture, and the Kalina cycle are all started using the flue gas from the combustion of biogas in a cascade process. In addition, the waste heat of the Kalina cycle is recovered by a single-effect desalination cycle and liquefied natural gas recovery section. Also, a water electrolysis process is integrated into the whole system. As a result, the products include hydrogen, electricity, natural gas, hot and cold water, and freshwater. The Aspen HYSYS software is utilized to design the system and investigate its thermodynamic, environmental, and economic aspects. The results demonstrate an energy efficiency of 57.54 % and an exergy efficiency of 18.78 %. Also, the carbon dioxide footprint associated with the system equals 0.34 kg/kWh, and the specific cost of products is 73.96 $/GJ. An optimal exergy efficiency of 25.8 % and a specific cost of products of 69.7 $/GJ are obtained by means of an intelligent optimization approach that ultimately makes use of non-dominated sorting genetic algorithm-II and artificial neural networks.

Enhanced Indoor Path Loss and RSRP of 5G mmWave Communication System with Multi-objective Genetic Algorithm

The signal strength in 5G mobile communication systems is significantly influenced by the surroundings, with key factors including the path difference, operating frequency, and obstructions at specific locations. Consequently, planning a communication system that can deliver improved signal strength becomes highly challenging. To address this issue, indoor path loss models are employed to estimate signal loss in different environments, frequencies, and distances. This paper introduces an intelligent multi-objective genetic algorithm aimed at enhancing path loss and received signal power. A comparative analysis is conducted to evaluate the performance of the proposed intelligent optimization algorithm against the traditional approach. The path loss and received power of various scenarios are estimated using various path loss models. The 5GCM indoor officce, 5GCM InH shopping mall, 3GPP TR 38.91 InH office, mmMAGIC InH office, METIS InH shopping mall, and IEEE 802.11 ad InH office indoor path loss models estimates the path loss of 62.37 dB, 62.15 dB, 63.12 dB, 50 dB, 55.18 dB, and 52.89 dB in traditional approach and 36.87 dB, 35.86 dB, 36.84 dB, 68.80 dB, 36.23 dB and 33.94 dB using GA approach and received powers of -12.17dBm,-11.37dBm,-12.17dBm,-5.80dBm,\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-12.17~dBm, -11.37~dBm, -12.17~dBm, -5.80~dBm,$$\\end{document}-12.24dBm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-12.24~dBm$$\\end{document} and -8.68dBm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-8.68~dBm$$\\end{document} in traditional approach and 26.13 dBm, 27.14 dBm, 26.15 dBm, -5.80dBm\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-5.80~dBm$$\\end{document}, 26.75 dBm and 29.05 dBm using GA approach repectively. The 5GCM and 3GPP models produces the path loss difference above 25 dB and mmMAGIC, METIS and IEEE models produces a path loss below 19 dB. Except mmMAGIC model, all models produces the recceiver power difference above 37 dBm. Therefore, the highest path loss difference of 26 dB is observed in 5GCM InH shopping mall model and the highest reccieved power difference of 39.01 dBm is observed in METIS InH shopping mall model. The results clearly demonstrate that the proposed intelligent optimization approach outperforms the traditional approach across various indoor scenarios.

Discovering ABO3-type perovskite with different dielectric constants via intelligent optimization algorithm

Nowadays, dielectric materials are playing an increasingly important role in various fields. A high dielectric constant (D) can store more charge per unit volume, improving performance, reducing device size, lowering D limit cross communication, and enabling better packaging of devices. Differentiating high D and low D has been recognized as a significant concern in electronics. However, calculating the dielectric constant from first principles is notoriously difficult. Therefore, it is essential to find important descriptors for predicting the dielectric constant (D) of different dielectric materials. In this work, a novel intelligence optimization approach was proposed based on data-driven methods to predict the dielectric constant (D) of ABO3-type perovskites. By applying the machine learning (ML) algorithm, key features strongly correlated with D were identified. To reduce feature dimension, Random Forest Regression-Gradient Boosting Regressor (RFR-GBR) feature screening, sure independence screening, and the sparsifying operator approach were employed to compress the feature set for creating valid descriptors. Furthermore, the Shapley additive explanation technique was used to reveal the scaling relation between the dielectric constant and the identified descriptors for predicting the D of ABO3-type perovskites. In addition, a hybrid artificial rabbits optimization algorithm and random forest regression were developed for predicting D, achieving remarkable predictive performance with an R2 score of 0.95, MAE of 0.23, and RMSE of 0.108 using five-fold cross-validation. Ultimately, from a pool of 300 candidate materials, we screened and identified two potential dielectric perovskites with different D values. The proposed framework will facilitate D prediction technology for the discovery of dielectric perovskite materials with favorable performance.

Performance enhancement attempts on the photovoltaic/thermal module and the sustainability achievements: A review

Scientists initiated a pursuit of more resilient and long-lasting energy sources due to the continuous deterioration of the environment and the substantial rise in the expense of conventional energy sources. Solar energy is one of the most cost-effective, environmentally friendly, and easily accessible sustainable energy sources. Photovoltaic/thermal (PV/T) solar modules are gaining popularity due to their ability to utilise more solar radiation to generate electric power and heat gain simultaneously. This study reviews recent advancements in PV/T modules, focusing on the dual conversion of solar radiation into electrical and thermal energy. The review highlights significant improvements, such as the use of nanoparticles, bi-fluid circulation, and innovative cooling techniques. The study examined the main categories of PV/T systems, including air-cooled, water-cooled, bi-fluid-cooled, nano-fluid-cooled, and ternary nanofluid-based units. This paper offers an in-depth analysis of various components within PV/T modules, covering aspects such as concepts, materials, and review techniques. However, challenges remain, including the trade-off between thermal and electrical efficiency and integration into existing infrastructure. Despite these limitations, PV/T systems show great potential for contributing to sustainable energy solutions and achieving global sustainable development goals. Future research directions include integrating an artificially intelligent approach for system optimization and expanding hybrid PV/T technologies.

A multi-objective planning and scheduling model for elective and emergency cases in the operating room under uncertainty

Hospitals are paramount hubs for delivering healthcare services, with their Operating Rooms (ORs) as a pivotal and financially substantial component. Efficient surgery ward planning is crucial in healthcare institutions, aiming to improve medical service quality while reducing costs. This research delves into the intricacies of integrated OR planning and scheduling, focusing on elective and emergency patients in an uncertain environment. To address these challenges, a mixed integer programming (MIP) framework is developed to minimize inactivity and patient wait times while optimizing high-priority resource allocation. Both upstream and downstream units of the ward, the Pre-operative Holding Unit (PHU), Post Anesthesia Care Unit (PACU), and Intensive Care Unit (ICU) are included. The inherently uncertain aspects of surgery, including surgical duration, Length of Stay (LOS), and the influx of emergency patients, demand an intelligent optimization approach. Consequently, a robust optimization strategy is harnessed to effectively grapple with this pervasive uncertainty. A deterministic model is introduced and improved using an enhanced epsilon constraint method. The culmination of this analytical journey yields a collection of Pareto-optimal solutions. Empirical results, supported by managerial insights, highlight the superiority of the proposed method over the traditional weighting approach.

Optimal operation and control of hybrid power systems with stochastic renewables and FACTS devices: An intelligent multi-objective optimization approach

This paper delves into the increasingly complex domain of Optimal Power Flow (OPF) within modern power systems, enhanced by the integration of unpredictable renewable energy sources. The research originally integrates stochastic photovoltaic and wind energy sources, along with a suite of Flexible AC Transmission System (FACTS) components – including thyristor-controlled series compensators, static VAR compensators, and thyristor-controlled phase shifters. The primary objective is to solve the OPF problem by reducing generation costs while accommodating the variable nature of renewable energy sources and load demands. This study prioritizes the examination of both constant and fluctuating load requirements. The inherent variability of PV and wind energy, along with load demand, is captured through the modelling of probability density functions. This approach enables a more detailed optimization process, incorporating not just the cost of thermal energy generation but also the scheduling costs of renewable sources and associated penalty costs. Moreover, the study examines the strategic placement and sizing of FACTS components, an aspect essential in minimizing the overall cost of power production. Employing both single- and multi-objective optimization algorithms, the research addresses the OPF problem in a modified IEEE-30 bus system through various case studies. The application of the recently developed flow direction algorithm, including its multi-objective variant with an ε-based constraint-handling mechanism to OPF problem is the primary contributions of this work. The results, benchmarked against several advanced metaheuristic algorithms, reveal the proposed algorithm's superior performance. This comprehensive study not only underscores the potential of integrating renewable energy sources into the grid but also highlights the efficacy of intelligent optimization strategies in managing the complexities of modern power systems.

Advancing capacitive deionization via intelligent optimization approach part 1: Development of tunable low-cost carbon electrodes

This work introduces a novel approach based on utilizing intelligent data-based optimization techniques to fabricate high-quality and low-cost carbon electrodes for capacitive deionization (CDI). The effects of synthesis parameters of peanut shell-derived porous carbon (PSPC) (KOH to Raw ratio, pyrolysis temperature and time) on five factors (production yield, BET surface area, electrode specific capacitance, and ion transfer resistances, including interface and diffusion) were investigated using combined design-of-experiment (DOE), response-surface-methodology (RSM), and Sobol's sensitivity analysis (SA). Optimal scenarios providing maximum production yield, surface area, and specific capacitance, and minimum ion transfer resistances were found using a multi-objective genetic algorithm (GA). Results uncover that increasing the impregnation ratio, pyrolysis temperature and time enhances the PSPC porosity that causes the carbon surface area and electrode specific capacitance to promote, the production yield to reduce and ion transfer resistances to raise. From Sobol's SA, pyrolysis temperature was the most impacting parameter on all decision factors. The optimal PSPC prepared at 750 °C and minimum KOH:Raw = 1 and time = 30 min offered a developed mesoporous structure with BET surface area of 685 m2/g and the resulting electrode had an outstanding specific capacitance (127.13 at 5 mV/s), with reliable durability after 100 cycles and showing the lowest possible ion transfer resistances.

Multi-agent intelligent optimization approach for optimal wireless rapid charging and SOC estimation of hybrid electric vehicle

Multi-agent intelligent optimization approach for optimal wireless rapid charging and SOC estimation of hybrid electric vehicle

A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization

A Survey on Intelligent Optimization Approaches to Boiler Combustion Optimization

Walrus optimizer: A novel nature-inspired metaheuristic algorithm

Metaheuristic algorithms are intelligent optimization approaches that lead the searching procedure through utilizing exploitation and exploration. The increasing complexity of real-world optimization problem has prompted the development of more metaheuristic algorithms. Hence, this work proposes a novel swarm intelligence algorithm, Walrus optimizer (WO). It is inspired by the behaviors of walruses that choose to migrate, breed, roost, feed, gather and escape by receiving key signals (danger signals and safety signals). To test the capability of the proposed algorithm, 23 standard functions and the benchmark suite from the IEEE (Institute of Electrical and Electronics Engineers) Congress on Evolutionary Computation (CEC) 2021 are used. In addition, to evaluate the practicability of the proposed algorithm to solve various real-world optimization problems, 6 standard classical engineering optimization problems are examined and compared. For statistical purposes, 100 independent optimization runs are conducted to determine the statistical measurements, including the mean, standard deviation, and the computation time of the program, by considering a predefined stopping criterion. Some well-known statistical analyses are also used for comparative purposes, including the Friedman and Wilcoxon analysis. The results demonstrate that the proposed algorithm can provide special stability features and very competitive performance in dealing with high-dimensional benchmarks and real-world problems. The proposal of WO promotes the continuous development and application expansion of artificial intelligence, improves the efficiency of optimization calculation, and provides powerful tools for solving complex problems in the real world. The source code of WO is publicly availabe at https://ww2.mathworks.cn/matlabcentral/fileexchange/154702-walrus-optimizer-wo.

A novel control scheme for automatic voltage regulator using novel modified artificial rabbits optimizer

Automatic voltage regulators (AVRs) are essential components in electrical systems to maintain stable voltage output, ensuring optimal performance and equipment protection. The effectiveness of AVRs rely on key parameters such as voltage regulation, response time, stability, and efficiency. Integrating controllers with AVRs offers centralized monitoring and regulation, enhancing voltage output efficiency. This study therefore first discusses various controller types which showcase distinctive capabilities based on their tuning strategies and then introduces an intelligent optimization approach using fractional-order proportional-integral-derivative with double derivative (FOPIDD2) controller tailored for AVRs. The study adopts the artificial rabbits optimization (ARO) algorithm, fortified with an adaptive local search (ALS) mechanism and experience-based perturbed learning (EPL) strategy. The modified version (m-ARO) enhances solution diversity and navigational efficacy, resulting in improved optimization quality. The study's core objective is to demonstrate the superior performance of the m-ARO-based FOPIDD2 controller in addressing the multifaceted challenges of AVR control, outperforming conventional techniques in stability, speed of response, robustness, and efficiency. To validate the method's efficacy, a comparative analysis is conducted using existing controllers with different tuning algorithms. Results indicate that the proposed m-ARO-based FOPIDD2 controller achieves superior performance metrics, demonstrating its capability. The study also includes a wider perspective by considering nineteen different controllers, reported in the literature, for comprehensive comparison. Notably, the proposed method exhibits the best stability, further affirming its effectiveness.

An intelligent technique for network resource management and analysis of 5G-IoT smart healthcare application

<p>The transformation of traditional hospitals to a smart healthcare system is a patient-centric, reliable, and smarter way to enhance healthcare facilities for individuals. These healthcare devices are based on the Internet of Things (IoT) and conventional network structures such as 3rd generation (3G) or 4th generation (4G). That did not provide the patient health data in the real-time scenario. Therefore, the integrated 5G-IoT system offers distinct benefits such as remote monitoring, surgery, and real-time data analysis of healthcare system that is operated with slice of dedicated network. It generates huge data with the billions of healthcare equipment that can be evaluated for decision making. This enormous data requires low latency, high-security, capacity, and more reliable techniques for evaluation. In this scenario, network slicing is one of the key solutions that can be considered for isolated end-to-end network structures. Network slicing enables logical and independent network virtual networks to be multiplexed for the same physical network. In this article, we propose an intelligent approach for network resource optimization and analysis of a 5G-IoT based smart healthcare network. We examine the performance of the machine learning algorithm by an automatic model, named A Fast Library for automated machine learning (FLAML). In addition, we perform network slicing to obtain resource optimization in 5G-IoT-based healthcare application.<strong></strong></p>

When architecture meets RL+EA: A hybrid intelligent optimization approach for selecting combat system-of-systems architecture

In recent decades, various domains such as military, aerospace and supply chain are becoming increasingly complex, forming system-of-systems (SoSs). The effectiveness of SoSs is mainly determined by their architecture, thus selecting proper architecture for SoSs is crucial to maximize their effectiveness. While most studies focus on specific issues, such as mission planning and SoSs meta-architecture selection, few papers deal with the two issues together. This paper introduces the task oriented SoSs architecture selection problem (TSASP) and proposes an combination of reinforcement learning (RL) and evolutionary algorithm (EA) (CRE) to address this problem. CRE comprises two layers: the inner layer utilizes a DQN-based algorithm to solve the mission planning problem, and the outer layer with improved genetic algorithm optimizes the SoSs architecture selection based on the output of inner one. A synthetic air and missile defense (AMD) example is conducted to test the effectiveness of proposed method, and results show that CRE can improve the mission planning effect by select a feasible architecture.

HYBRID PARTICLE SWARM OPTIMIZATION AND GREY WOLF OPTIMIZER FOR SETTING PID PARAMETERS OF BLDC MOTORS

BLDC (Brushless DC) motors have advantages over asynchronous motors and dc motors in various aspects. Particularly in electric bicycles and flying cars, BLDC motors are utilized widely. Electric bicycles and flying cars are becoming increasingly popular, and as a result, the significance of BLDC motors and their cost-effective and efficient utilization has been growing rapidly. PID (Proportional Integral Derivative) controllers are generally used in motor control because they are cheap and perform well. Many methods have been used to adjust PID parameters. Although methods such as Ziegler-Nichols, Cohen-Coon etc. are widely used, there are also new methods such as optimization algorithms PSO (Particle Swarm Optimization), Whale Optimization Technique, Gray Wolf Optimization technique etc. The hybrid method: HPSOGWO (Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer) is a combination of PSO and GWO (Grey Wolf Optimizer) techniques, and it can be used for tuning PID parameters. As associated with this, the aim of this study is to show the superiority of HPSOGWO algorithm in optimizing the PID parameters. In the content of this study, the essentials regarding the optimization background, and details of the BLDC motor modeling was explained first. After that, the methodology of the hybrid solution was expressed and then the application phase was explained in detail, by including the results generally. In the context of the intelligent optimization approach of this study, the results were obtained in the MATLAB Simulink environment. The application of the used solution method revealed its superiority over the study conducted solely with GWO in various parameters.

Intelligent Optimization Approach to Cell Formation and Product Scheduling for Multifactory Cellular Manufacturing Systems Considering Supply Chain and Operational Error

This study designs a joint decision model to solve cell formation and product scheduling problems together in multifactory cellular manufacturing systems. If a product is processed in a factory with a small logistics cost of its raw material, the logistics cost of the product delivered to the distributor may be large. If managers prefer the pair of equipment unit and worker with a high production rate, operational error rate may be large. Meanwhile, it influences the labor cost, raw material cost, and completion time of each product. Managers can assign the products of a type to several cells for processing. If a cell receives fewer products of a type, it can complete their processing and becomes available to process the products of other types. However, some cells receiving more products of this type may be delayed to handle the products of other types. If a type of product in a cell has a priority to be processed, its backorder cost may be none or reduced, but this schedule may delay other product types with lower priority in the cell, and give rise to their backorder cost. For solving the intertwined optimization problem, an improved bacterial foraging algorithm with a priority rule-based heuristic (IBFAP) is developed to maximize profit. Experiments are conducted to show that IBFAP outperforms the improved bacterial foraging algorithm with no heuristic, the classical bacterial foraging algorithm, genetic algorithm, and simulated annealing.

GA-BiLSTM: an intelligent energy prediction and optimization approach for individual home appliances

GA-BiLSTM: an intelligent energy prediction and optimization approach for individual home appliances