Intelligent Optimization Techniques Research Articles

Resilient data-driven non-intrusive load monitoring for efficient energy management using machine learning techniques

The integration of smart homes into smart grids presents numerous challenges, particularly in managing energy consumption efficiently. Non-intrusive load management (NILM) has emerged as a viable solution for optimizing energy usage. However, as smart grids incorporate more distributed energy resources, the complexity of demand-side management and energy optimization escalates. Various techniques have been proposed to address these challenges, but the evolving grid necessitates intelligent optimization strategies. This article explores the potential of data-driven NILM (DNILM) by leveraging multiple machine learning algorithms and neural network architectures for appliance state monitoring and predicting future energy consumption. It underscores the significance of intelligent optimization techniques in enhancing prediction accuracy. The article compares several data-driven mechanisms, including decision trees, sequence-to-point models, denoising autoencoders, recurrent neural networks, long short-term memory, and gated recurrent unit models. Furthermore, the article categorizes different forms of NILM and discusses the impact of calibration and load division. A detailed comparative analysis is conducted using evaluation metrics such as root-mean-square error, mean absolute error, and accuracy for each method. The proposed DNILM approach is implemented using Python 3.10.5 on the REDD dataset, demonstrating its effectiveness in addressing the complexities of energy optimization in smart grid environments.

Trajectory Planning for UAV-Assisted Data Collection in IoT Network: A Double Deep Q Network Approach

Unmanned aerial vehicles (UAVs) are becoming increasingly valuable as a new type of mobile communication device and autonomous decision-making device in many application areas, including the Internet of Things (IoT). UAVs have advantages over other stationary devices in terms of high flexibility. However, a UAV, as a mobile device, still faces some challenges in optimizing its trajectory for data collection. Firstly, the high complexity of the movement action and state space of the UAV’s 3D trajectory is not negligible. Secondly, in unknown urban environments, a UAV must avoid obstacles accurately in order to ensure a safe flight. Furthermore, without a priori wireless channel characterization and ground device locations, a UAV must reliably and safely complete the data collection from the ground devices under the threat of unknown interference. All of these require the proposing of intelligent and automatic onboard trajectory optimization techniques. This paper transforms the trajectory optimization problem into a Markov decision process (MDP), and deep reinforcement learning (DRL) is applied to the data collection scenario. Specifically, the double deep Q-network (DDQN) algorithm is designed to address intelligent UAV trajectory planning that enables energy-efficient and safe data collection. Compared with the traditional algorithm, the DDQN algorithm is much better than the traditional Q-Learning algorithm, and the training time of the network is shorter than that of the deep Q-network (DQN) algorithm.

An Alternative Approach Using the Firefly Algorithm and a Hybrid Method Based on the Artificial Bee Colony and Cultural Algorithm for Reservoir Operation

In reservoir operation rule curves, it is necessary to apply rule curves to guide long-term reservoir management. This study proposes an approach to optimizing reservoir operation rule curves (RORCs) using intelligent optimization techniques from the firefly algorithm (FA) and a unique combination method utilizing the artificial bee colony and cultural algorithm (ABC-CA). The aim is to establish a connection with the simulation model to determine the optimal RORCs for flood control. The proposed model was used to determine the optimal flood control RORC for the Nam-Oon Reservoir (NOR) in northeastern Thailand. A minimum frequency and minimum average of excess water were provided as an objective function for assessing the efficiency of the search process. The evaluation of the effectiveness of flood control RORCs involved expressing water scarcity and excess water situations in terms of frequency, magnitude, and duration using historical inflow data synthesized from 1000 events. The results demonstrated that when using the obtained RORC to simulate the NOR system for reducing flooding in long-term operations, excess water scenarios were smaller than those using the current RORC. The results showed that the excess water scenario using the RORC obtained from the proposed model can reduce the excess water better than the current RORC usage scenario. In decreasing flood situations, the newly acquired RORC from the suggested FA and ABC-CA models performed better than the current RORC.

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.

Maximizing the output power of magnetically geared generator in low-speed applications using subdomain modeling and particle swarm optimization

This study presents an approach to optimizing the output power in a magnetically geared generator by employing subdomain modeling and particle swarm optimization. Accordingly, the subdomain analytical method is utilized to obtain the induced voltage, inductance which are used to predict output power. Afterward, the swarm intelligence optimization technique is applied to reach the maximum output power point. The 12/17/15 prototype evaluated the proposed model by comparing the calculated results and finite element method simulation.

Modeling of Dung Beetle Optimization-based Sink Node Localization Approach for Wireless Sensor Networks

Wireless sensor network (WSN) performs monitoring of each aspect of the area of interest by detecting the surrounding physical phenomena with sensor nodes and transferring the information to the gateway through the corresponding system. Several researcher workers have introduced localization methods to accomplish high accuracy of localization. An intelligent optimization technique has attracted various researcher workers due to its advantages such as strong optimization capability and few parameters to optimize the localization performance of the DV-Hop method. Sink node localization (NL) using metaheuristics in WSN includes applying optimization techniques inspired by human behavior or natural phenomena to define the geographical coordinates of the sink nodes within the network coverage region. WSNs can accomplish better localization performance, especially in dynamic or complex environments, improving the efficiency and reliability of network management and data transmission by leveraging metaheuristics. In this view, this manuscript develops a Dung Beetle Optimization based Sink Node Localization Approach (DBO-SNLA) for WSN. In the DBO-SNLA technique, the DBO algorithm involved is based on the social behavior of dung beetle populations and is developed with five updated rules to assist in finding high-quality solutions. In addition, the DBO-SNLA technique addresses the issues of defining the sink node location with lowest localization error once the data between the nodes is transferred wirelessly. Finally, the localization errors are calculated and the location of the different unknown nodes is computed. A detailed set of simulation takes place to examine the performance of the DBO-SNLA technique. The empirical analysis stated the betterment of the DBO-SNLA method than other techniques

Fractal Geometry Meets Computational Intelligence: Future Perspectives.

Characterizations in terms of fractals are typically employed for systems with complex and multiscale descriptions. A prominent example of such systems is provided by the human brain, which can be idealized as a complex dynamical system made of many interacting subunits. The human brain can be modeled in terms of observable variables together with their spatio-temporal-functional relations. Computational intelligence is a research field bridging many nature-inspired computational methods, such as artificial neural networks, fuzzy systems, and evolutionary and swarm intelligence optimization techniques. Typical problems faced by means of computational intelligence methods include those of recognition, such as classification and prediction. Although historically conceived to operate in some vector space, such methods have been recently extended to the so-called nongeometric spaces, considering labeled graphs as the most general example of such patterns. Here, we suggest that fractal analysis and computational intelligence methods can be exploited together in neuroscience research. Fractal characterizations can be used to (i) assess scale-invariant properties and (ii) offer numeric, feature-based representations to complement the usually more complex pattern structures encountered in neurosciences. Computational intelligence methods could be used to exploit such fractal characterizations, considering also the possibility to perform data-driven analysis of nongeometric input spaces, therby overcoming the intrinsic limits related to Euclidean geometry.

Exploring swarm intelligence optimization techniques for task scheduling in cloud computing: algorithms, performance analysis, and future prospects

Exploring swarm intelligence optimization techniques for task scheduling in cloud computing: algorithms, performance analysis, and future prospects

Synergy of random balance design method and intelligent optimization technique for model updating of the 4 MW offshore wind turbine benchmark

The technical challenges for model updating of real marine engineering structures include the extraction of modal parameters associated with incomplete measurement information and the correction of a large number of structural parameters. As it is difficult to verify the effectiveness of model updating methods for engineering applications, the representative models have to be adopted in finite element (FE) analysis or lab-scale tests, leading to the incomplete reflection of actual structural performances. Based on these factors, a practical model updating method is developed in this study to make full use of field measurement data from a 4 MW offshore wind turbine for the accurate estimation of structural parameters using the random balance designs-Fourier amplitude sensitivity test (RBD-FAST) strategy and an improved particle swarm optimization (IPSO). Leveraging the measured acceleration signals from offshore wind turbines under the conditions including operation, shutdown, collision and typhoon scenarios, the complex exponential decomposition method is applied to accurately extract the time-varying acceleration components (TVAC) for the construction of the frequency response function (FRF). Following that, RBD-FAST is implemented into IPSO with adaptive inertia weights and asynchronously varying learning factors to enable efficient selection of numerous updated physical parameters, thus improving time cost and computational accuracy. The correctness of the proposed method is verified by a numerical jacket platform model. Furthermore, the 4 MW offshore wind turbine benchmark is developed to assess the feasibility of the proposed model updating method using field measured data from different scenarios. Results show that the synergy of RBD-FAST and IPSO in the proposed method can accurately update parameters and minimize a maximum discrepancy of 0.8970% for the first-three orders of natural frequencies between the benchmark and the updated models in the collision scenario. Summarily, the present work provides a potential technique and practical engineering references for model updating of offshore wind turbines subject to harsh marine environments.

An Optimized Energy and Time Constraints-Based Path Planning for the Navigation of Mobile Robots Using an Intelligent Particle Swarm Optimization Technique

Mobile robots (MRs) typically require running for many hours on one charge of the battery. Electric autonomous mobile robots (AMRs) have become increasingly common in the manufacturing process in the last few years. MRs must often complete difficult assignments while gathering information across an unknown area involving energy constraints and time-sensitive preferences. This paper estimates the information collection assignment for surveillance as a multi-objective optimization dilemma with both energy and time constraints. In this study, three main objectives during acquiring data are taken into consideration, including the greatest quantity of data acquired for surveillance, following a path where obstacles are least likely to be experienced, and traveling the smallest feasible path. To obtain the optimal path for an MR by addressing the presented issue, this approach presents an intelligent particle swarm optimization (PSO) technique that determines fitness value by simplifying the optimization task for achieving the shortest path for MR navigation. It allows particles to execute variable operations while maintaining most of the prior search information. The findings of the simulation show that this technique of the PSO algorithm can realize swift convergence and high accuracy when compared with different benchmark functions derived for PSO. A comparative discussion on various energy-efficient navigation techniques for MRs is also provided. Lastly, this study describes the possible future research directions.

Research on automatic matching of online mathematics courses and design of teaching activities based on multiobjective optimization algorithm.

The teaching of the optimization algorithm is a new kind of swarm intelligence optimization technique, which is superior in optimizing many simple functions. Still, it is not evident in processing some complex problems (group and teaching classification). Achieving automatic matching and knowledge transfer in online courses is imperative in mathematics education. This study proposes a design scheme MTCBO-LR (multiobjective capability optimizer-logistic regression), based on multitask optimization, which enables precise knowledge transfer and data interaction among many educators. It incorporates the standard TLBO algorithm to optimize, provides a variety of learning tactics for students at different stages of mathematics instruction, and is capable of adaptively adjusting these strategies in response to actual teaching needs. Experimental results on various datasets reveal that the proposed method enhances searchability and group diversity in various optimization extremes and outperforms similar methods in resolving to multitask teaching problems.

Optimal Positioning of Distributed Energy using Intelligent Hybrid Optimization

The renewable based energy source integration into the smart grid has seen a rapid surge lately and so has the role of distributed energy. By reducing the power losses within distribution network, the appropriate location and size of the distributed energy or distributed generation sources could be found. In this paper, it has been proposed that where the distribution generating units should be placed. An intelligent optimization technique has been utilized to position different distribution generators in the best possible way. These advancements have both the advantages and disadvantages of their own. The research employs a novel optimization technique based on a hybrid of genetic algorithm and particle swarm optimization techniques. Placing the distribution generations properly lowers the power losses and improves the profile of the voltage in the distribution system.

Composite disc optimization using hunger games search optimization algorithm

Abstract It has been interesting in industrial applications due to the advantages provided by laminated composite applications. Composite materials are of great importance in design optimization due to the advantages they provide, such as lightweight, high strength value, and design flexibility. Finite element programs and artificial intelligence optimization techniques developed in recent years are effectively used in the design of composite materials. In order to optimize the angular orientations of the composite disc, analyses are made using the finite element program. Particle swarm optimization algorithm (PSO), genetic algorithm (GA), and hunger games search optimization algorithm(HGS) are used to determine the best stacking angle value on the disc plate using the analysis data obtained after the completion of the analyses. When the results obtained were examined, it was determined that the results obtained from particle swarm optimization, genetic algorithm, and hunger games search optimization techniques showed that hunger games search optimization algorithm gives better results as other and HGS could be used for the stacking of composite materials.

Snow Cover Mapping Based on SNPP-VIIRS Day/Night Band: A Case Study in Xinjiang, China

Detailed snow cover maps are essential for estimating the earth’s energy balance and hydrological cycle. Mapping the snow cover across spatially extensive and topographically complex areas with less or no cloud obscuration is challenging, but the SNPP-VIIRS Day/Night Band (DNB) nighttime light data offers a potential solution. This paper aims to map snow cover distribution at 750 m resolution across the diverse 1,664,900 km2 of Xinjiang, China, based on SNPP-VIIRS DNB radiance. We implemented a swarm intelligent optimization technique Krill Herd algorithm, which finds the optimal threshold value by taking Otsu’s method as the objective function. We derived SNPP-VIIRS DNB snow maps of 14 consecutive scenes in December 2021, compared our snow-covered area estimations with those from MODIS and AMSR2 standard snow cover products, and generated composite snow maps by merging MODIS and SNPP-VIIRS DNB data. Results show that SNPP-VIIRS DNB snow maps are capable of providing reliable snow cover maps superior to MODIS and AMSR2, with an overall accuracy level of 84.66%. The composite snow maps at 500 m spatial resolution provided 55.85% more information on snow cover distribution than standard MODIS products and achieved an overall accuracy of 84.69%. Our study demonstrated the feasibility of snow cover detection in Xinjiang based on SNPP-VIIRS DNB, which can serve as a supplementary dataset for MODIS estimations where clouded pixels are present.

Path Planning of an Unmanned Combat Aerial Vehicle with an Extended-Treatment-Approach-Based Immune Plasma Algorithm

The increasing usage of unmanned aerial vehicles (UAVs) and their variants carrying complex weapon systems, known as unmanned combat aerial vehicles (UCAVs), has triggered a global revolution in complex military and commercial operations and has attracted researcher attention from different engineering disciplines in order to solve challenging problems regarding these modern vehicles. Path planning is a challenging problem for UAV and UCAV systems that requires the calculation of an optimal solution by considering enemy threats, total flight length, fuel or battery consumption, and some kinematic properties such as turning or climbing angles. In this study, the immune plasma (IP or IPA) algorithm, one of the most recent nature-inspired intelligent optimization methods, was modified by changing the default plasma transfer operations with a newly proposed technique called the extended treatment approach; extended IPA (ExtIPA) was then introduced as a path planner. To analyze the solving capabilities of the ExtIPA, 16 cases from five battlefield scenarios were tested by assigning different values to the algorithm-specific control parameters. The paths calculated with ExtIPA were compared with the paths found by planners on the basis of other intelligent optimization techniques. Comparative studies between ExtIPA and other techniques allowed for stating that the extended treatment approach significantly contributes to both the convergence speed and qualities of the obtained solutions and helps ExtIPA in performing better than its rivals in most cases.

A novel reversible fragile watermarking method in DWT domain for tamper localization and digital image authentication

Medical images are extremely valuable and sensitive data because of their significance in research, diagnosis, and treatment. However, the transmission of medical images over an unsecured network can lead to security issues such as modification, removal, copying, and unauthorized access. For this reason, the protection of medical information is of great importance. This paper proposes a novel reversible fragile digital watermarking method for tamper localization and authenticity and integrity protection in medical images. In the proposed method, the host image is firstly divided into 8×8 non-overlapped blocks. Then the image is transformed to the frequency domain by applying a Discrete Wavelet Transform, and the watermark information is embedded. Finally, the image is converted from the frequency domain to the spatial domain using an inverse Discrete Wavelet Transform. During this transformation, an intelligent optimization technique is used to correct the rounding errors and obtain the watermarked image with the highest level of imperceptibility. The experimental results demonstrate that the proposed method offers a watermarked image with high imperceptibility, extracts the host image lossless during the watermark extraction phase, and performs tamper localization with high precision.

Integrating Solar Photovoltaic Power Source and Biogas Energy-Based System for Increasing Access to Electricity in Rural Areas of Tanzania

Renewable energy is the best option for the challenge of dwindling natural resources and energy scarcity. The utilization of solar photovoltaic (PV) systems is the best option for eliminating the energy deficit in Tanzania due to the available great potential of solar energy. Animal manure is a significant source of waste in rural locations which can be transformed into biogas fuel by an anaerobic process. Livestock and agriculture greatly support economically the majority of the sub-Saharan African (SSA) region’s rural population including Tanzania, and excreta from cattle are beneficial for biogas fuel production. Unfortunately, the high potential of animal waste for generating electricity is underutilized. Integrating solar energy sources and biogas fuel derived from animal manure is useful for mitigating energy shortage, power instability, and environmental issues. Off-grid solar PV biogas-based hybrid microgrid systems for rural electrification applications in the Tanzanian environment are limited, and also, most of the studies are extensively carried out using soft computing tools especially hybrid optimization of multiple energy resources (HOMER) software with limited applications of artificial intelligence (AI) optimization techniques. This paper presents technoeconomic viability analysis for a hybrid renewable energy supply system (HRESS) for the Simboya village in Mbeya region, Tanzania. Off-grid HRESS is designed and optimized to meet the load of the chosen location executed using HOMER software and the grey wolf optimization (GWO) method. The microgrid is anticipated to supply daily maximum demand of 63.41 kW. The residential load profile equals 30 kW representing 50% of the daily demand. Optimization results by the HOMER platform indicate that the system has a total net present cost (NPC) and levelized cost of energy (LCOE) of $106,383.50 and $0.1109/kWh, respectively. Furthermore, this paper presents the optimization and sensitivity analysis results acquired by the GWO method under varied values of Loss of Electrical Power Probability (LEPP). Total NPC and LCOE based on LEPP values of 0, 0.04, and 0.06 are $85,106.8, $79,545.99, and $71,747.36 and $0.0887/kWh, $0.0316/kWh, and $0.0102/kWh, respectively. HRESS is economically and environmentally beneficial for supplying electricity to the selected area and worldwide in similar situations.

Boosted arithmetic optimization algorithm with elite opposition-based pattern search mechanism and its promise to design microstrip patch antenna for WLAN and WiMAX

ABSTRACT This paper investigates the performance of a novel artificial intelligence optimization technique in terms of designing a small size antenna that can be used for WLAN and WiMAX applications. In this regard, a boosted version of the arithmetic optimization algorithm is constructed as a novel artificial intelligence optimization technique with the aid of pattern search and elite opposition-based learning mechanisms. The proposed boosted arithmetic optimization algorithm is demonstrated for its superior explorative and exploitative behavior using classical fixed-dimensional, multimodal, and unimodal benchmark functions. The performance of the boosted arithmetic optimization algorithm is then presented for a real-world engineering optimization problem. For the latter challenge, a small size antenna that can be used for WLAN and WiMAX applications is designed. The obtained simulation results show that a compact small-sized patch antenna operating at WLAN and WiMAX frequencies can successfully be designed with the proposed boosted arithmetic optimization algorithm. Comparative evaluation against the state-of-the-art shows that efficiency is increased significantly since a bandwidth increase of up to is achieved even with a more than reduction in size.

Optimal Configuration of Coupled Optical Storage Hydrogen Production System Considering Multi-body and Multi-objective Joint Optimization

With the development of siting and capacity setting methods for coupled optical-storage hydrogen production systems, difficulties such as the complexity of constraints in multi-body and multi-objective system planning models, the decline of electrolytic tank life, and non-linear and non-time-series have emerged one after another. To address these difficulties, this paper proposes a double-layer planning capacity configuration method for coupled optical-storage-grid hydrogen production for transmission and transformer systems, which solves the explicit and implicit coupled double-layer optimization problems under the mixed-integer optimization framework with traditional cone optimization techniques combined with swarm intelligent optimization techniques to achieve joint optimal planning configuration for coupled optical-storage-hydrogen production systems.

Improvement of ultrasound-assisted extraction of polyphenolic content of the plant Cytisus triflorus L'Her as a natural resource using artificial neural network modeling and multi-objective optimization

The improvement of environmental performance of production processes is a critical problem right now. Processes that are effective in terms of productivity and environmental effects are the center of interest of researchers. This study established such a method for an ultrasound-assisted extraction procedure. The plant Cytisus triflorus L'Her was used to extract polyphenolic chemicals using ultrasound-assisted extraction as a quick, less energy consuming, high-yield technology. This work offers a new experimental planning approach using Artificial Neural Networks, that enables the extraction process to be optimized using multi-objective optimization in order to identify the best extraction conditions to obtain the maximum polyphenolic compounds possible. Advanced modeling using Artificial intelligence and statistical optimization techniques was used to determine the best combinations of ethanol concentration, extraction temperature and time for the maximum extraction of total phenols, total flavonoids, total flavonols, and total tannins. An artificial neural network (ANN) was used to build the models for each of the four goals, then they were multi-objectively optimized using a genetic algorithm technique. The maximum polyphenolic contents including TPC (4.986 ± 0.006 mg GA/g DE), TFC (14.026 ± 0.070 mg Q/g DE), TFolC (5.225 ± 0.010 mg R/g DE), and TTC (6.240 ± 0.021 mg C/g DE) were obtained using an extraction process under optimal conditions, which included a solvent concentration of 78%, an extraction temperature of 52 °C, and an extraction time of 55 min which were obtained using the MOO Pareto optimum solution selection approach Gray Relational Analysis GRA.