Hybrid Optimization Research Articles

Enhancement of AVR system performance by using hybrid harmony search and dwarf mongoose optimization algorithms

Innovations in control algorithms, integration of smart grid technologies, and advancements in materials and manufacturing techniques all push the boundaries of AVR performance. As the demand for power systems progresses with the complexity and variety of loads, conventional AVR designs may struggle to handle these ever-changing circumstances efficiently. Therefore, the need for new optimization methods is crucial to bolstering the efficiency, reliability, and adaptability of AVRs. Thus, this work aims to improve the performance of the AVR system controller by using a novel hybrid technique between the Harmony Search (HS) and Dwarf Mongoose Optimization (DMO) algorithms to tune the proportional-integral-derivative (PID) and proportional-integral-derivative acceleration (PIDA) parameters. The suggested hybrid approach ensures an accurate solution with balanced exploration and exploitation rates. The reliability of the proposed HS-DMOA is verified through comparison with different optimization techniques carried out on time and frequency performance indicators, disturbances in the form of changes to time constants, and dynamic input signals. The proposed hybrid HS-DMOA PID-based has better overshoot than PID-based HS, LUS, TLBO, SMA, RSA, and L-RSAM by 20.37%, 18.5%, 18.5%, 2.77%, 5.55%, and 2.77%, respectively. Regarding the phase margin, the proposed hybrid HS-DMOA PID-based is better than PID-based HS, LUS, and TLBO by 39%, 37%, and 38%, respectively. While the proposed hybrid HS-DMOA PIDA-based has a better overshoot than PIDA-based HS, LUS, and PID HS-DMOA-based by 14%, 17%, and 20%, respectively. Moreover, the robustness under dynamic disturbance proved the reliability of the proposed HS-DMOA PID and PIDA based through enhancement of overshoot around 0.3%~20% for different cases. Finally, the main contribution of the paper is to propose a relatively new hybrid optimization method to enhance the AVR PID and PIDA-based performance with detailed analysis in time and frequency domains under normal and dynamic disturbances.

Hybrid solar photovoltaic-wind turbine system for on-site hydrogen production: A techno-economic feasibility analysis of hydrogen refueling Station in South Korea's climatic conditions

The South Korean government has an ambitious policy to roll out hydrogen-powered vehicles across the country with 2000 hydrogen refueling stations (HRSs) infrastructure by 2050. However, the country currently lacks sufficient HRS infrastructure. In this context, this study proposes and investigates the technoeconomic feasibility and performance assessment of an optimal hybrid renewable energy system integrated with a vanadium redox flow battery for on-site hydrogen production. The system is designed to refuel a fleet of 20 fuel cell electric vehicles at seven South Korean locations with distinct climates. Based on the typical daily hydrogen and electric load profile of the HRS, the capital expenditure, net present cost, operation expenditure, levelized cost of hydrogen and levelized cost of energy were estimated to be, $5.95 - $13.2 M, $8.93 - $19.4 M, 82,670–202,184 $/yr, 8.77–19.1 $/kg and 2.1–4.58 $kWh, respectively.

A NEW HYBRID OPTIMIZATION METHOD USED IN PREDICTIVE CONTROL OF A NONLINEAR FRACTIONAL MODEL BASED ON FRACTIONAL HAMMERSTEIN STRUCTURE

Fractional Hammerstein models represent various nonlinear processes, such as thermal and mechanical. Their major drawback is the non-convex optimization problem in a nonlinear model predictive control scheme due to its static nonlinearity. Indeed, an efficient optimization algorithm is needed. This work proposes a hybrid optimization algorithm combining the Nelder Mead optimization method and the Honey Badger one to synthesize a predictive control algorithm based on fractional Hammerstein models. As illustrated through simulation results, the proposed method offers clear improvements in convergence and tracking performances.

A hybrid GRA-PCA-TOPSIS Technique Optimization of EDD Process Parameter on Drilling of AL8011/B4C/aloevera composites

A class of cutting-edge materials called hybrid aluminum metal matrix composites has been produced for applications such as the automobile, aerospace, and electronics industry and where weight and strengths are crucial factors. In the present research work, AA8011 composite reinforced with boron carbide particles and green aloevera has been fabricated using an economical stir casting process. In this 1% to 3% of boron carbide and 1% to 2% aloevera were used to fabricate the three different combinations of composites. The electrical discharge drilling process variables such as discharge current, pulse-on time, pulse-off time, and dielectric fluid pressure are investigated using the tubular copper electrode on drilling of hybrid composites. Performance aspects including drilling rate, roundness error, taper angle, and electrode wear rate have been analyzed, and the effect of process variables with the response surface methodology–central composite experimental design. The process parameters during the drilling of composites were optimized using a novel multi-criteria decision-making technique called the hybrid grey relational analysis–pulse component analysis–technique for order of preference by similarity to ideal solution method in order to maximize the drilling rate while concurrently minimizing other aspects. The optimum conditions were reached using the hybrid optimization method and a 1.5 mm tubular copper electrode with an input current of 9 Ampere , pulse on time 25 [Formula: see text]s, pulse off time 12 [Formula: see text]s, and pressure 60 kg/cm2. In comparison to the initial settings, the optimal condition shows a 25% faster drilling rate, with a 23% better roundness value, 14% lesser taper angle, and 7% lesser electrode wear rate.

A Reformulated-Vortex-Particle-Method-Based Aerodynamic Multi-Objective Design Optimization Strategy for Proprotor in Hover and High-Altitude Cruise

An improved multi-objective design optimization framework is proposed for the efficient design of proprotor blades tailored to specific high-altitude mission requirements. This framework builds upon existing methods by leveraging a reformulated Vortex Particle Method (rVPM) and incorporates three key stages: (1) rapid determination of overall proprotor parameters using a semi-empirical model, (2) optimized blade chord and twist distribution bounds based on minimum energy loss theory, and (3) global optimization with a high-fidelity rVPM-based aerodynamic solver coupled with a multi-objective hybrid optimization algorithm. Applied to a small high-altitude tiltrotor, the framework produced Pareto-optimal proprotor designs with a figure of merit of 0.814 and cruise efficiency of 0.896, exceeding mission targets by over 15%. Key findings indicate that large taper ratios and low twist improve hover performance, while elliptical blade planforms with high twist enhance cruise efficiency, and a tip anhedral further boosts overall performance. This framework streamlines the industrial customization of proprotor blades, significantly reducing the design space for advanced optimization while improving performance in demanding high-altitude environments.

Hybrid Optimization With Deep Spiking Equilibrium Neural Network for Software Defined Network Based Congestion Prevention Routing

Hybrid Optimization With Deep Spiking Equilibrium Neural Network for Software Defined Network Based Congestion Prevention Routing

Optimal Space Vector-Based Hybrid PWM Scheme in Terms of Common-Mode Voltage and Line Current Ripple

Optimal Space Vector-Based Hybrid PWM Scheme in Terms of Common-Mode Voltage and Line Current Ripple

Optimal scheduling of distributed generation in smart microgrids: A comprehensive model and efficient algorithm

Abstract This study proposes an optimal scheduling model for distributed generation (DG) within smart microgrids, incorporating various distributed energy resources (DERs) such as photovoltaic panels, wind turbines, biomass generators, and energy storage systems. To address the complexities of the scheduling problem, we design a hybrid optimization algorithm combining Genetic Algorithms (GA) and Particle Swarm Optimization (PSO). This hybrid algorithm leverages the global search capabilities of GA and the local search efficiency of PSO to achieve robust and efficient convergence to near-optimal solutions. A comprehensive case study based on a real-world smart microgrid system demonstrates the effectiveness of the proposed model and algorithm. The results indicate significant reductions in total operational costs, enhanced renewable energy utilization, reduced grid dependency, and improved system reliability. This research highlights the potential for broader implementation of the model, contributing to the advancement of smart grid technologies and the transition towards sustainable energy systems.

Optimization of Microwave-Assisted Polyphenol Extraction and Antioxidant Activity from Papaya Peel Using Response Surface Methodology and Artificial Neural Network

The objective of the present study is to determine the optimal conditions of microwave-assisted extraction (MAE) such as microwave power (MWP), irradiation time (I-time), ethanol concentration (EtOH%), and solvent-to-solid ratio (S/S) for maximizing total polyphenol content (TPC) and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity from papaya peel. Box-Behnken design (BBD) was used considering MWP 300–600 W, I-time 60-120 s, EtOH% 30-70%, and S/S 30-70 mL/g. Additionally, response surface methodology (RSM) and artificial neural networks (ANN) were employed for modelling, with cross-validation to enhance reliability. These models were combined with the desirability function (DF) and/or genetic algorithm (GA) optimization approaches. Maximizing TPC and DPPH activity while maintaining MWP, I-time, EtOH%, and S/S within their respective ranges using hybrid optimization approaches (RSM-DF: TPC = 1058 mgGAE/100g, and DPPH = 83%, RSM-GA: TPC = 1064 mgGAE/100g and DPPH = 79%, and ANN-GA: TPC = 1086 mgGAE/100g, and DPPH = 83%,) yielded consistent optimal results. An additional optimization approach (RSM-DF: TPC = 955 mgGAE/100g, and DPPH = 90%), aimed at minimizing MWP, I-time, EtOH%, and S/S while maximizing TPC and DPPH activity, showed a significant reduction of approximately 3 L in ethanol consumption for each 100 g of dry samples. The maximized TPC and DPPH activity in all four approaches showed a significant (p < 0.05) alignment with their corresponding validation tests, demonstrating the better performance of these modelling and optimization processes. In terms of assessing the effectiveness of microwave assistance, conventional solvent extraction was also performed, maintaining a 70 mL/g S/S ratio of 55% ethanol at 60°C for 2 h. This method resulted in lower values of TPC (857 ± 23 g GAE/100g) yield and DPPH activity (72 ± 5%) compared to the optimized condition of MAE.

Neural Observation Field Guided Hybrid Optimization of Camera Placement

Neural Observation Field Guided Hybrid Optimization of Camera Placement

Adaptive tunicate swarm optimization with partial transmit sequence for phase optimization in MIMO-OFDM

&lt;span&gt;Multiple-input multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) are widely utilized in wireless systems and maximum data rate communications. The MIMO-OFDM technology increases the efficiency of spectrum utilization. The peak-to-average-power ratio (PAPR) minimization in MIMO-OFDM is a complex task in wireless communications systems. In this research, an adaptive tunicate swarm optimization with partial transmit sequence (ATSO-PTS) algorithm is proposed for a reduction of PAPR in MIMO-OFDM. The nonsquare-matrix-based differential space time coding (N-DSTC) scheme is used for the encoding and decoding process of MIMO-OFDM. The N-DSTC encoding and decoding are linear error-correcting codes that are utilized for message transmission over noisy channels. The pre-specified quadrate phase shift keying (QPSK) symbol is deployed for the modulation and demodulation scheme. On the receiver side, the serial to parallel (S/P) conversion, and fast Fourier transform (FFT) are accomplished, alongside the received data bits being demodulated to obtain the output bits. The proposed ATSO-PTS method achieves better results according to performance metrices PAPR, bit error rate (BER) and signal-to-noise-ratio (SNR), with values of about 2.9, 0.01 and 0.025, respectively. This ensures superior results when compared to the existing methods of twin symbol hybrid optimization applied to partial transmit sequence (TSHO-PTS), selective level mapping and PTS (SLM-PTS), and particle swarm and grey wolf (PS-GW) with PTS, respectively.&lt;/span&gt;

Sustainable hybrid renewable energy management system for a community in island: A model approach utilising Hybrid Optimization of Multiple Energy Resources optimization and priority setting‐based Supervisory Control and Data Acquisition operation

Sustainable hybrid renewable energy management system for a community in island: A model approach utilising Hybrid Optimization of Multiple Energy Resources optimization and priority setting‐based Supervisory Control and Data Acquisition operation

Sign language recognition using modified deep learning network and hybrid optimization: a hybrid optimizer (HO) based optimized CNNSa-LSTM approach.

Speech impairment limits a person's capacity for oral and auditory communication. Improvements in communication between the deaf and the general public can be progressed by a real-time sign language detector. Recent studies have contributed to make progress in motion and gesture identification processes using Deep Learning (DL) methods and computer vision. But the development of static and dynamic sign language recognition (SLR) models is still a challenging area of research. The difficulty is in obtaining an appropriate model that addresses the challenges of continuous signs that are independent of the signer. Different signers' speeds, durations, and many other factors make it challenging to create a model with high accuracy and continuity. This study mainly focused on SLR using a modified DL and hybrid optimization approach. Notably, spatial and geometric-based features are extracted via the Visual Geometry Group 16 (VGG16), and motion features are extracted using the optical flow approach. A new DL model, CNNSa-LSTM, is a combination of a Convolutional Neural Network (CNN), Self-Attention (SA), and Long-Short-Term Memory (LSTM) to identify sign language. This model is developed for feature extraction by combining CNNs for spatial analysis with SA mechanisms for focusing on relevant features, while LSTM effectively models temporal dependencies. The proposed CNNSa-LSTM model enhances performance in tasks involving complex, sequential data, such as sign language processing. Besides, a Hybrid Optimizer (HO) is proposed using the Hippopotamus Optimization Algorithm (HOA) and the Pathfinder Algorithm (PFA). The proposed model has been implemented in Python, and it has been evaluated over the existing models in terms of accuracy (98.7%), sensitivity (98.2%), precision (98.5%), Word Error Rate (WER) (0.131), Sign Error Rate (SER) (0.114), and Normalized Discounted Cumulative Gain (NDCG) (98%) as well. The proposed model has recorded the highest accuracy of 98.7%.

Advanced Hybrid Optimization Algorithms for Energy Efficient Cloud Resource Allocation

AbstractCloud computing is highly sought after for its dynamic resource allocation capabilities and pay‐per‐use model. However, previous research has identified several challenges, such as lower coverage, high integration rates, longer computation times, and complex operators, all of which are associated with NP‐hard problems. These issues negatively impact the efficiency of resource allocation and scheduling, leading to slower processes, inefficiencies in multi‐objective optimization, lower throughput, and higher power consumption. To address these challenges, we propose a unique Hybridized Optimization Algorithm that integrates Crow Swarm Optimization (CSO), Cuckoo Search Optimization (CSO), and Cat Hunting Optimization (CHO). Initially, Particle Swarm Optimization (PSO) and the Crow Search Algorithm (CSA) handle the exploitation and exploration phases to balance task loads. Subsequently, Cuckoo Search Optimization (CSO) enhances resource utilization and addresses NP‐hard issues, while Cat Hunting Optimization (CHO) refines the search from global to local optimal spaces to achieve the best values. The results demonstrate that the proposed hybrid technique effectively reduces user request waiting times, lowers energy consumption, and decreases execution times on cloud servers compared to baseline approaches, thereby significantly improving overall system performance.

Hybrid sine cosine and spotted Hyena based chimp optimization for PI controller tuning in microgrids

In this paper, a novel hybrid sine-cosine and spotted Hyena-based chimp optimization algorithm (hybrid SSC) is adopted for the precise tuning of proportional-integral (PI) controllers in a microgrid system. The microgrid integrates multiple renewable energy sources, including photovoltaic (PV) panels, wind turbines, a fuel cell, and a battery storage system, all connected to a common DC bus. This DC bus interfaces with the main grid through a voltage source converter (VSC). The microgrid comprises a total of eight PI controllers distributed across various components: the boost converter in the wind system, the fuel cell system, the battery energy storage device, and the VSC controller. The hybrid SSC optimization algorithm effectively combines the exploration capabilities of the sine-cosine algorithm (SCA) with the exploitation strengths of the spotted Hyena optimizer (SHO) and Chimp optimization algorithm (ChOA), aiming to achieve optimal tuning of the PI controllers. This hybrid approach ensures an enhanced dynamic response and overall system performance by minimizing the integral of the time-weighted squared error (ITSE) for each controller. The simulation results, directed in a MATLAB/SIMULINK environment, demonstrate the efficacy of the hybrid SSC algorithm in improving the stability, response time and efficacy of the microgrid. The proposed technique significantly outperforms traditional tuning techniques, ensuring robust operation and seamless addition of renewable energy sources with the main grid. This study contributes to the advancement of intelligent control strategies for modern microgrids, emphasizing the importance of hybrid optimization algorithms in achieving optimal performance in complex energy systems.

Optimization of Hybrid Energy Systems Based on MPC-LSTM-KAN: A Case Study of a High-Altitude Wind Energy Work Umbrella Control System

This paper presents an optimization method for hybrid energy systems based on Model Predictive Control (MPC), Long Short-Term Memory (LSTM) networks, and Kolmogorov–Arnold Networks (KANs). The proposed method is applied to a high-altitude wind energy work umbrella control system, where it aims to enhance the stability and efficiency of energy utilization. The work umbrella system integrates wind and solar energy sources, with energy stored in a battery and used to control the umbrella’s operations. The MPC framework is employed to optimize control actions by solving a finite-horizon optimization problem, ensuring the battery State of Charge (SOC) remains within an optimal range. The LSTM network provides accurate predictions of environmental conditions, including wind speed and solar irradiance, which are essential for MPC’s decision-making process. To address complex nonlinearities in the system, the KAN is utilized to model and approximate these dynamics, refining the LSTM predictions. The integration of these advanced control strategies enables the system to handle varying operational conditions and maintain optimal performance. The case study demonstrates the effectiveness of the MPC-LSTM-KAN approach, revealing improvements in the SOC stability, energy efficiency, and operational endurance of the high-altitude wind energy work umbrella system. The results indicate that this hybrid optimization method offers a robust solution for managing hybrid energy systems in dynamic environments.

A novel hybrid information-based PF-WOA algorithm for gas source localization in 3D space

Abstract In recent years, dangerous gas leakage events occur frequently. Rapid and accurate location of gas leakage sources by mobile robots is the key to avoid the expansion of disasters. In order to solve the problem of discontinuous gas concentration gradient and sparse gas environment in three-dimensional space, particle filter, and whale swarm optimization algorithm are integrated to locate gas source. Firstly, the Z-shape search and comb search are used to locate the plume, and then, the particle filter algorithm is combined with the whale optimization method to guide the particle movement, and the random inertial disturbance term is designed to improve the convergence speed and search accuracy of the algorithm. Experimental results in three-dimensional environment show that the proposed information-driven particle filter whale optimization hybrid algorithm effectively guides the robot in localizing gas source within a certain range, significantly enhancing both the efficiency and accuracy of localization compared to other algorithms.

OSC-GOR: Optimal Security-Aware Cluster-Based Hybrid Geographical and Opportunistic Routing for Vehicular ad hoc Networks

Vehicular ad hoc networks (VANETs) are crucial for infrastructure-less vehicular communication. The dynamic and complex nature of VANETs necessitates efficient routing strategies. This paper introduces an optimal security-aware cluster-based hybrid geographical and opportunistic routing (OSC-GOR) for VANETs. By utilizing the improved bald eagle optimization (IBEO) algorithm, we achieve optimal clustering that enhances energy efficiency. Cluster heads (CHs) are elected based on trust, calculated from metrics like energy consumption and signal strength. A Poplar optimization algorithm (POA) further refines these metrics. Subsequently, an optimal min–max recurrent neural network (OMM-RNN) determines the next forwarder nodes, facilitating data transfer in VANETs. Implemented using the NS-2 tool and SUMO traffic generator, OSC-GOR showcases optimized clustering and superior network efficiency. Comparative simulations reveal OSC-GOR’s advantages in energy use, latency, delivery ratio, goodput, and routing overhead, outperforming existing methods. Notably, OSC-GOR effectively counters VANET attacks such as GPS spoofing and Sybil threats.

Energy management and demand side management framework for nano-grid under various utility strategies and consumer’s preference

This research proposes a day-ahead scheduling utilizing both demand side management (DSM), and Energy Management (EM) in a grid-tied nanogrid comprises of photovoltaic, battery, and diesel generator for optimizing the generation cost and the energy not supplied (at grid-outage). Wider terminology is introduced to combine both load controllability (considered in traditional DSM), and interval capability to accommodate additional loads defined as flexible, non-flexible, and semi-flexible intervals. Moreover, the user selection for EM or combined operation of EM with DSM at different degrees of interval flexibility is defined as user preference. In addition, three utility’s operations are considered denoted as fixed rate pricing (FRP), time-of-use (ToU) pricing, and FRP with grid-outage. Hence, the suggested framework utilizes the opportunities of generation diversity, the electricity pricing strategy, and the load flexibility. The obtained result show that, DSM with flexible intervals reduces the cost by 21.02%, 25.23%, and 18.15% for FRP, ToU, and FRP with grid-outage scenarios respectively. And cost reduction by 20.41%, 22.42%, and 17.81% for DSM with semi-flexible intervals and 16.24%, 21.15%, and 13.8% for DSM with non-flexible intervals. This cost reduction is associated with full utilization of renewable energy generation and reduction of the energy from/to battery which enhances its lifetime or reduces the required battery size during design stage for cost and provisions saving in flexible and semi-flexible intervals. A hybrid optimization technique of Moth-flame optimization algorithm, and Lagrange’s multiplier is proposed and confirms its effectiveness with detailed comparison with other techniques.

Enhancing hosting capacity for electric vehicles in modern power networks using improved hybrid optimization approaches with environmental sustainability considerations

The increasing adoption of electric vehicles (EVs) presents both opportunities and challenges for power networks. While EVs have the potential to reduce carbon emissions, accommodating their growing power demand requires careful planning to prevent overloading and mitigate environmental impacts. This paper introduces an integrated hosting capacity model to facilitate higher EV penetration while maintaining environmental standards. In addition to EV charging stations, the model incorporates transmission lines, reactive power compensators, energy storage systems, and thyristor-controlled series compensators to ensure a reliable power supply. The model aims to maximize EV charging station deployment, minimize greenhouse gas emissions, and optimize net present value through hosting capacity strategies. Three hosting capacity plans are proposed to analyze the impact of prioritizing one of these objectives over the others in network configurations. Accurate EV demand forecasting is critical for this model, and a swarm intelligence forecasting algorithm is proposed to explore various forecasting approaches. The model is complex and involves nonlinear multi-objective optimization. To solve it, a new hybrid optimization algorithm is introduced, combining the features of the Marine Predators Algorithm and the Honey Badger Algorithm. Three hybridization schemes—Series Hybrid Scheme, Population Division Scheme, and Switching Strategy Scheme—are developed to address the optimization challenges effectively. The results show that the first and second hybridization schemes are the most effective for solving the EV load forecasting models, with a robustness of at least 90%. In contrast, the robustness of the third scheme reaches only 30% in some models. Simulation studies on the IEEE 9-bus network and the IEEE 30-bus system validate the model’s effectiveness in integrating EVs while achieving environmental sustainability objectives. The findings show the superiority of the proposed hybrid schemes in solving the hosting capacity model in terms of finding optimal solutions. However, the third scheme required less computing time than the others, with its convergence time being at least 33.3% shorter.