Hybrid Optimization Algorithm Research Articles

Distributed model predictive control of fan coil system

To address the issues of large overshoot, long system stabilization time, and slow response associated with traditional PID-based and centralized model predictive control (MPC) of fan coil systems, this study proposes a distributed model predictive control (DMPC) method suitable for multi-zone building temperature regulation. Specifically, based on the thermal dynamic model of different zones, and aiming to constrain indoor temperature within set limits and minimize water system pressure drop and fan coil speed, a distributed MPC architecture for fan coil systems is developed. To solve the distributed optimization problem, the Nash equilibrium is introduced to find the global balance point within the distributed structure, and the Beetle Antennae Search-Particle Swarm Optimization (BAS-PSO) hybrid algorithm is used for online optimization of local MPCs. Experimental results show that compared to traditional PID control, the distributed MPC achieves coordinated multi-zone indoor temperature regulation with better dynamic performance, with the average absolute temperature error in each zone below 5 % and an energy savings rate of 16.23 %. Through semi-physical simulation, it is demonstrated that the proposed distributed MPC method has better applicability when the number of fan coils exceeds 10, compared to centralized MPC.

Identification of historical maximum load on concrete beams based on crack and deformation response using knowledge-embedded machine learning algorithms

This study introduces an innovative method for identifying the historical maximum load on concrete beams, utilizing a multi-layer back propagation (BP) neural network model with physically interpretable features as inputs. The method establishes a mapping relationship among four-dimensional features (boundary constraints, system parameters, cracks, and deformations) and the maximum load in the load history, using incremental load action and structural response. Moreover, a hybrid optimization algorithm, combining Hunter Prey Optimization (HPO) and Particle Swarm Optimization (PSO), is introduced to optimize the model's parameters. The method is compared with commonly used machine learning models, demonstrating its accuracy and reliability. Additionally, the study performs a sensitivity analysis of the input features using the proposed intelligent identification model. The results reveal that the proposed intelligent identification model exhibits small prediction errors on both the training and testing sets, with average absolute percentage errors of 16.46 % and 19.56 %, and determination coefficients of 0.922 and 0.8185, respectively, showcasing its strong robustness and generalization capability. Compared to the non-optimized multi-layer BP neural network model, the proposed model achieves a reduction in average absolute percentage errors of 39.59 % and 48.33 % on the training and testing sets, respectively.

Synthesis of Circular Antenna Arrays for Achieving Lower Side Lobe Level and Higher Directivity Using Hybrid Optimization Algorithm

Circular antenna arrays (CAAs) find extensive utility in a range of cutting-edge communication applications such as 5G networks, the Internet of Things (IoT), and advanced beamforming technologies. In the realm of antenna design, the side lobes levels (SLL) in the radiation pattern hold significant importance within communication systems. This is primarily due to its role in mitigating signal interference across the entire radiation pattern’s side lobes. In order to suppress the subsidiary lobe, achieve the required primary lobe orientation, and improve directivity, an optimization problem is used in this work. This paper introduces a method aimed at enhancing the radiation pattern of CAA by minimizing its SLL using a Hybrid Sooty Tern Naked Mole-Rat Algorithm (STNMRA). The simulation results show that the hybrid optimization method significantly reduces side lobes while maintaining reasonable directivity compared to the uniform array and other competitive metaheuristics.

Currents’-Physical-Component-Based Reactive Power Compensation Optimization in Three-Phase, Four-Wire Systems

In this paper, we aim to address the limited capacity of compensation devices by enhancing their utilization rate by applying the currents’ physical component (CPC) theory for reactive power optimization in three-phase four-wire systems. When reactive currents cannot be fully compensated for, we propose using CPC theory to generate reference currents for the compensation devices. Weight coefficients associated with different reactive current components are introduced, enabling flexible combinations of these independent current components. The maximum output amplitude of the three-phase current from the compensation device serves as a constraint condition, allowing for the calculation of reference currents under various compensation targets. Additionally, a reactive current optimization compensation scheme focusing on loss reduction is selected. The simulated annealing–particle swarm optimization (SA-PSO) hybrid algorithm is employed to solve the optimization mathematical model. The discussed calculations, current waveforms, and voltage waveforms are generated using the constructed mathematical model and then used for a theoretical explanation. The simulation verifies the feasibility of the proposed method.

Optimization of the One-Size-Fits-All Layout Problem Based on Preparing Material for Steel Bridges

Before the construction of a bridge begins, workers arrange the necessary parts and then cut and process them. The quality of the cutting layout directly affects the material utilization rate and the efficiency of the subsequent processes. During bridge construction, an intelligent part layout can improve work efficiency, save time, and reduce the labor intensity and production costs for the company. In this study, we studied a layout optimization algorithm, focusing on rectangular parts in the material preparation process. A mathematical model for the rectangular layout problem was constructed, and a hybrid genetic whale optimization algorithm is proposed that is a combination of the whale optimization algorithm and the genetic algorithm. Based on the “one size fits all” layout strategy, the materials are divided into strips, which are further divided into stacks, serving as the positioning strategy to determine the positional relationships of the parts. Test cases and actual engineering data were used to compare the layouts generated using different algorithms. The results show that the genetic whale algorithm proposed in this paper results in a high utilization rate and is highly effective.

Performance Analysis and Optimization of Solar-Coupled Mine Water-Source Heat Pump Combined Heating and Cooling System

To address the energy consumption issue in mining area buildings, this paper proposed a solar-coupled mine water-source heat pump combined heating and cooling (SMWHP-CHC) system, taking the employee dormitory building group of a coal mining enterprise in Tongchuan City, China, as a case study. The system utilizes renewable solar energy and waste heat recovered from mine water as composite heat sources, and utilizes the cold energy in mine water as a cooling source to meet the demands for space heating, space cooling, and annual domestic hot water of the building in a sustainable manner. The simulation model of the system was established by TRNSYS to analyze the system’s annual operational performance. The results indicated that the system exhibited a positive energy efficiency and environmental performance under different operating conditions. The heating coefficients of the performance of the system (COPsys) during the space heating season and transition season were 3.54 and 18.6, and the cooling energy efficiency ratio of the system (EERsys) was 3.79. In addition, aiming to minimize the annual cost of the system, multiple crucial device parameters were synchronously optimized employing the PSO-HJ hybrid optimization algorithm through the GenOpt 2 software. The annual cost of the optimized system was reduced by 8.82%, and the investment cost was significantly reduced, while the performance was also improved. This study can provide theoretical support for the sustainable engineering application of the SMWHP-CHC system in mining area buildings.

A hybrid deep learning optimization for predicting the spread of a new emerging infectious disease

<span lang="EN-US">In this study, a novel approach geared toward predicting the estimated number of coronavirus disease (COVID-19) cases was developed. Combining long short-term memory (LSTM) neural networks with particle swarm optimization (PSO) along with grey wolf optimization (GWO) employ hybrid optimization algorithm techniques. This investigation utilizes COVID-19 original data from the Ministry of Health of Indonesia, period 2020-2021. The developed LSTM-PSO-GWO hybrid optimization algorithm can improve the performance and accuracy of predicting the spread of the COVID-19 virus in Indonesia. In initiating LSTM initial weights with weaknesses, using the hybrid optimization algorithm helps overcome these problems and improve model performance. The results of this study suggest that the LSTM-PSO-GWO model can be utilized as an effective and reliable predictive tool to gauge the COVID-19 virus’s spread in Indonesia. </span>

Friction compensation control method for a typical excavator system based on the accurate friction model

Traditional identification methods make it difficult to accurately determine the parameters of the friction model. Based on the LuGre friction model, a hybrid optimization algorithm (HO) is presented to address such aspects. This algorithm combines the genetic algorithm (GA) and particle swarm optimization algorithm (PSO) to obtain the globally optimal solutions. It incorporates three main improvements: introducing selection and crossover operations into the PSO algorithm, utilizing a variable inertia weight coefficient, and applying variable learning coefficients for enhanced performance. Compared to the static optimal solutions of the GA and PSO algorithms, the HO algorithm can improve the optimal solution by 40.68 % and 35.89 % respectively. The HO algorithm can achieve the highest model accuracy: the maximum identified friction error with the HO algorithm is only 1.70 kN, compared to 3.69 kN with the GA method and 8.52 kN with the PSO algorithm. Furthermore, a novel adaptive friction compensation controller (AC) is introduced based on the identification results to improve the stability and accuracy of the electro-hydraulic proportional systems of a 23-ton robotic excavator. Comparisons with existing proportional integral differential (PID) and feedforward compensation controllers (FC) highlight the superiority of the proposed adaptive controller in terms of trajectory accuracy and robustness. In sinusoidal trajectory tracking experiments, the AC method can achieve the highest tracking performance among the three controllers, and the maximum tracking error is only 11.56 mm. Compared to the FC controller, the control accuracy with the AC controller can be improved by 43.38 %. Experiments and simulations have shown that the friction compensation controller can effectively mitigate friction and other disturbances. As a result, the phenomena of crawling and flat peak phenomena can be eliminated.

MeSat Mission: Exploring Martian Environment with THz Radiometer Payload and Optimal Trajectory

Space exploration presents vast prospects for scientific, industrial, and economic progress. This paper introduces the MeSat mission as a pioneering approach to Mars exploration. The MeSat aims to deepen our understanding of Martian conditions and resources by employing an optimized Earth-to-Mars trajectory, enabling a comprehensive study of the Martian atmosphere and surface. The mission comprises a cargo microsatellite hosting three 6U CubeSats and two 3U CubeSats, deployed into four separate Mars orbits to form a constellation. Each CubeSat carries distinct payloads: a THz radiometer for Martian water vapor atmospheric observation, a high-resolution surface camera, a high-tech spectrometer, and a Fourier transform spectrometer (FTS) for wind speed readings. This paper includes the majority of the key parameters; however, we focus our discussion more on two aspects of this pioneering mission: the first aspect contains the proposal of four distinct payloads for the study of Mars’ atmosphere and the second aspect proposes an optimal mission design algorithm that analyzes a fuel-efficient low-thrust trajectory from Earth to Mars. Regarding the payloads, the THz radiometer requires a specific design; hence, we explain this payload in more depth; the rest of the payloads, we suggest utilizing commercially available elements for the cost-effective manufacture of a whole system. For mission trajectory optimization, the study employs a dual-step hybrid optimization algorithm (PSO-homotopy) to analyze fuel-efficient low-thrust trajectories from Earth to Mars, incorporating the ephemeris dynamics model to account for gravitational perturbations in the entire Solar System. In practical mission design, crucial factors like hyperbolic excess velocity, diverse opportunities for Earth launch and Mars rendezvous, varied propulsion systems, and time of flight (TOF) play vital roles in trajectory optimization. In summary, for the MeSat mission, we propose a comprehensive Mars environmental mission design. We consider all aspects of the mission from trajectory design to engineering detail design, since we would like to inspire future Mars missions with a complete report.

A new hybrid optimization algorithm for maximizing area coverage in wireless sensor networks

ABSTRACT The rapid advancements in technology influence develop our lives day-by-day. However, the area coverage issue exhibited by sensor nodes in WSN still seems a complicated issue even if the area coverage enhancement models are developed. This research work is developed on the basis of implementing an efficient area coverage maximising approach with an advanced optimisation model, Coati Modernized Beluga Whale Optimisation (CMBWO). The whole process of the area coverage maximising approach revolves in two important steps: (i) Sensor node coverage model and (ii) Area coverage optimisation. In the first step, a 2D WSN monitoring area set-up is considered and in which target monitoring points are assumed to solve the coverage issue. Most importantly, the distance between the sensor node and the target monitoring point is calculated using weighted Manhattan distance evaluation instead of conventional Euclidean distance evaluation. This improvement is introduced to calculate the precise position of the target monitoring point from the position of the sensor node. The next, area coverage optimisation step is handled by the CMBWO algorithm for maximising area coverage in WSN. This hybridisation is possible by modernising BWO via COA. This efficient hybridisation led to maximising the area coverage of sensor nodes in WSN.

Implementing intelligent‐based sparse channel estimation in Multi User‐Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing system with hybridized optimization algorithm

SummaryThe channel plays a significant role as the multipath in Multiple Input Multiple Output‐Orthogonal Frequency Division Multiplexing (MIMO‐OFDM) systems. The precoding‐based channel estimation algorithms estimate the channel capacity at the receiver node and they provide efficient channel estimates under worst case channel scenarios. To estimate the channel capacity in the mobile node, semi‐blind scenario is a crucial task in Multi User (MU) MIMO systems. Therefore, a semi blind sparse channel estimation algorithm is demonstrated in the MU‐MIMO system to provide better performance. In the transmitter side, the signal modulation is performed with the help of Quadrature Phase Shift Keying (QPSK), and the modulated signal is given to Pulse Shaping Algorithm (PSA) to reduce the inter‐carrier interference as well as inter symbol interference. At each transmitter, the Inverse Fast Fourier Transforms (IFFT) technique is used for mapping the symbols and then the mapped signal is send to the receiver. In the receiver side, the FFT, pulse reshaping, and QPSK demodulation blocks are serially connected and perform inverse operations of the transmitter in the receiver. Then, the sparse channel is estimated at the receiver by using the semi blind sparse algorithm, where the parameters are optimized by using the Hybridized Drosophila with Virus Colony Search Optimization (HD‐VCSO). The objective function of the developed sparse channel estimation is the reduction of Minimum Mean Square Error (MMSE) in the channel. The performance of the developed sparse channel estimation algorithm is analyzed through different conventional algorithms to validate the effectiveness of the proposed algorithm.

An improved robust algorithm for optimisation-based colour medical image watermarking

Recently, significant attention has been focused on copyright protection of medical images, causing watermarking to become a prevalent research topic. In this paper, we propose an optimisation-based robust watermarking algorithm for the copyright protection of colour medical images. Initially, we decompose a host medical image by lifting wavelet transform (LWT), followed by discrete cosine transform (DCT) into transformed coefficients, and then embed multiple watermarks into the transformed host image by using hybrid optimisation algorithms. To further improve security and reduce the channel noise distortion, we then apply hashing and Hamming code to image and text mark, respectively, before the embedding process. Finally, we utilise 2D chaotic map to encrypt the marked media, aiming to achieve enhanced security and confidentiality. The experimental outcome shows satisfactory invisibility, watermark payload and robustness against image-processing attacks. Furthermore, through numerous comparative experiments with nine state-of-the-art algorithms on two standard datasets, the effectiveness of the proposed algorithm is demonstrated.

A hybrid optimization algorithm for energy-aware multi-objective task scheduling in heterogeneous multiprocessor systems

A hybrid optimization algorithm for energy-aware multi-objective task scheduling in heterogeneous multiprocessor systems

A hybrid optimization algorithm to identify unknown parameters of photovoltaic models under varying operating conditions

Enhancing the performance of optimization algorithms to accurately extract parameters for photovoltaic modules is a significant challenge in the field. While several metaheuristic algorithms have been explored, achieving a balance between exploration and exploitation capabilities remains elusive. In this context, hybrid metaheuristics have emerged as a promising approach to address this issue and improve optimization efficiency. In our study, we propose a novel hybrid algorithm that integrates the backtracking search optimization algorithm (BSA) with the differential evolution (DE) algorithm. This hybridization aims to enhance the precision of parameter extraction for various photovoltaic models, including single-diode, dual-diode, and commercially available modules such as the ST40 Thin Film, SM55 Monocrystalline, and S75 Polycrystalline. The BSA is initially employed to leverage historical experiences and guide the evolutionary process, thereby enhancing the algorithm’s exploration capabilities. Subsequently, the DE algorithm is introduced to accelerate convergence during iterations and improve exploitation of the search space. By combining these two algorithms, we aim to achieve superior accuracy and efficiency in parameter estimation. To validate the effectiveness of our approach, extensive experiments are conducted, and the results demonstrate its superiority over other sophisticated algorithms. The proposed algorithm achieves remarkable performance, as evidenced by the lowest mean square error values obtained: 9.86021877891501E-04 for the single-diode model, 9.82484851785148E-04 for the dual-diode model, 2.42507486809511E-03 for Photowatt-PWP201, 1.72981370994069E-03 for STM6-40/36, and 1.66006031250855E-02 for STP6-120/36. This highlights the algorithm’s exceptional accuracy and efficiency in extracting parameters for photovoltaic modules.

XVC scheme-based secret sharing model with hybrid optimization algorithm for visual cryptography

XVC scheme-based secret sharing model with hybrid optimization algorithm for visual cryptography

IoT-based waste management: hybrid optimal routing and waste classification model.

Internet of Things (IoT) makes connectivity between physical devices which are embedded with sensors, software, and connectivity that let them to communicate and transfer data. This technology makes it possible to collect and transfer data from a vast network device, opening the door to the development of automatic and more efficiency systems. The term "waste management" refers to all of the responsibilities essential to regulate trash, from the point of gathering through reusing and monitoring. Reducing the hazardous consequences of such garbage on the environment and human health is the goal of waste management. By considering these hazardous consequences, this research work is interested in working on an efficient waste management system. The utilization of IoT devices enables municipalities to optimize waste management operations by leveraging data insights. This information aids in scheduling waste collections more effectively and planning optimal routes. Therefore, the research work proposes an IoT-based waste management system with two vital processes such as IoT routing and waste management. At first, routing in IoT is done by proposing hybrid optimization algorithm named Snake Optimization Updated Beluga Whale Optimization algorithm (SOUBWO) under constraints such as distance, energy, link quality, delay, and trust. Secondly, waste management is worked on by following steps including pre-processing, segmentation, feature extraction, and classification. The waste images collected by IoT devices are transmitted from source node (SN) to destination node (DN) by optimal routing. Those transmitted waste images are pre-processed by Wiener filtering process. Consequently, the pre-processed images are segmented by employing proposed Balanced Iterative Reducing and Clustering Using Hierarchies-Altered Distance Metrics (BIRCH-ADM) algorithm. Subsequently, features such as multi-text on histogram feature, proposed Local Gabor XOR Pattern (LGXP)-based feature using novel image processing techniques, and statistical features are extracted. Finally, these extracted features are efficiently classified by hybrid classification model which is formed by integrating conventional deep maxout and Bidirectional-Long Short Term Memory (Bi-LSTM) networks. The effectiveness of the proposed approach is validated through various analyses, including performance and statistical analyses. Moreover, the proposed scheme demonstrates minimal energy consumption, with a recorded value of 0.123. In contrast, conventional methodologies exhibit higher energy consumption, with values such as SOA = 0.237, BWO = 0.146, BES = 0.183, SMO = 0.158, CHOA = 0.174, and PSO = 0.189, respectively. By this hybrid classification model, the process of classification on waste is effectively done and moreover its effectiveness is proved by various analyses.

A hybrid algorithm based on the proposed square strategy and NSGA-II for ship pipe route design

Ship pipe route design (SPRD) is a difficult and time-consuming job due to the complexity of the routing space and the large number of pipes. This paper introduces a hybrid optimization algorithm called SQNSGA-II based on the proposed Square strategy and NSGA-II to efficiently solve SPRD problems in complex situations. The details of the hybrid algorithm are introduced, including the principle of the Square strategy, genetic operators and pipe repair operators et al. Additionally, a new method called Intercept Search strategy is proposed for optimizing branch pipe design in complex scenarios. Finally, we conducted three cases of simulation experiments to demonstrate that the proposed algorithm and strategy can efficiently and accurately optimize the design of pipes, highlighting their pipe routing capabilities in complex engineering projects.

A hybrid optimization algorithm for multi-agent dynamic planning with guaranteed convergence in probability

The paper aims to solve the problem of multi-agent path planning in complex environment using optimization algorithm. To address the issue of local optimum and premature convergence, a new method is proposed based on the whale optimization algorithm, combining the chaotic initialization, the reverse search and the differential evolution methods. It is theoretically proved that this algorithm is globally convergent in probability. When applied to path planning problems, the proposed optimization algorithm can effectively find a globally optimal and smoother path. Through simulation experiments with multi-UAVs, it is demonstrated that the proposed algorithm has better performance than the state-of-the-art methods in environment with both static and dynamic obstacles, reflecting the global convergence and robustness of the proposed algorithm.

Groundwater level prediction using an improved ELM model integrated with hybrid particle swarm optimisation and grey wolf optimisation

Groundwater, the world's most abundant source of freshwater, is rapidly depleting in many regions due to a variety of factors. Groundwater resources (GWR) play a crucial role in daily life, economic progress, and agricultural crop production, which makes groundwater level (GWL) prediction much more significant for enhanced management. However, one of the major challenges in investigating and estimating groundwater reduction and managing its resources is the absence of complete and reliable data. Therefore, the application of artificial intelligence (AI) models with the necessity for less data and better prediction accuracy is inevitable. The performances of hybrid models including the combination of particle swarm optimisation and grey wolf optimisation with extreme learning machine (ELM-PSOGWO) in estimating the GWL are investigated and evaluated against ELM-GWO, ELM-PSO, ELM, Twin-support vector regression (T-SVR) and SVR models based on the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), and Nash-Sutclife efficiency coefficient (NSC). Outcomes of this study showed that applied hybridised ELM-PSOGWO (R2 = 0.9969, RMSE = 0.0088, NSC = 0.9961, MAE = 0.0047) performed best, followed by ELM-GWO, ELM-PSO, ELM, TSVR, and SVR in predicting GWL. Based on the obtained results, it is evident that the ELM-PSOGWO model illustrates an excellent agreement with observed GWL data. To this end, this work reveals that hybrid optimisation algorithms can enhance performance of ELM model significantly in GWL prediction. Utilization of advanced AI techniques can well estimate groundwater systems, which saves resources and labor, employed conventionally.

Re-Entrant Green Scheduling Problem of Bearing Production Shops Considering Job Reworking

To solve various reworking and repair problems caused by unqualified bearing product quality inspections, this paper introduces a green re-entrant scheduling optimization method for bearing production shops considering job reworking. By taking into account quality inspection constraints, this paper establishes an integrated scheduling mathematical model based on the entire processing–transportation–assembly process of bearing production shops with the goals for minimizing the makespan, total carbon emissions, and waste emissions. To solve these problems, the concepts of the set of the longest common machine routes (SLCMR) and the set of the shortest recombination machine combinations (SSRMC) were used to propose the re-entrant scheduling optimization method, based on system reconfiguration, to enhance the system stability and production scheduling efficiency. Then, a multi-objective hybrid optimization algorithm, based on a neighborhood local search (MOOA-LS), is proposed to improve the search scope and optimization ability by constructing a multi-level neighborhood search structure. Finally, this paper takes a bearing production shop as an example to carry out the case study and designs a series of experimental analyses and comparative tests. The final results show that in the bearing production process, the proposed model and algorithm can effectively realize green and energy-saving re-entrant manufacturing scheduling.