Parameter Estimation-Based Slime Mold Algorithm of Photocatalytic Methane Reforming Process for Hydrogen Production

When it comes to improving the performance of a wireless mesh network, the positioning of routers is critical. In order to provide the greatest network accessibility, it is essential that mesh routers be installed in appropriate locations. One way to improve the network’s performance is to increase the coverage while utilizing an optimum number of routers. The network coverage may be optimised using a number of different techniques that have been developed over time in the scientific literature. An algorithm called Harris Hawk’s Optimization (HHO) is being used in this study to optimally locate mesh routers in a mesh network in order to optimise coverage and reduce network congestion. The HHO identifies the best positions to place the routers in order to have the best possible network topology by removing the overlapping routers to minimize the number of routers while keeping the coverage maximized. In this research, 60, and 100 mesh routers are deployed in two different network topologies. The simulation results are analysed and compared with Sine Cosine Algorithm (SCA), Gray Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) algorithm. With regard to network coverage and congestion reduction, simulation results show that HHO surpasses the benchmark algorithms by having 98% coverage and 34% reduction in the network size.

Modified swarm intelligence algorithms for the pharmacy duty scheduling problem

The dynamic voltage restorer's (DVR) transient, steady-state, and dynamic responses are essential requirements for protecting sensitive loads against upstream voltage disturbances via the DVR's ride-through capabilities. DVRs also look after transient oscillation at the instant of entering, and /or exiting by the DVR. This paper presents an enhanced, optimized, and less complex DVR control system structure, which is capable of improving the transient, steady-state, and dynamic responses as well as eliminating inherent transient oscillations. The control system comprises a closed-loop feedback control signal and feedforward upstream disturbance detection signal. Incorporating the feedforward term helps, dramatically, in improving the system response and eliminating the transient oscillations in the load voltage. The error signals are adapted using a PI controller to make the load voltage faithfully track its predefined reference waveform. The controller is implemented in the dq synchronous rotating reference frame. The parameters of the PI controller are selected using modern population-based optimization called the Harris Hawks Optimization (HHO) technique. The results obtained using the HHO technique are compared with two other optimization algorithms, namely Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA). The results show that the HHO gives the best system response. The system is simulated using MATLAB/Simulink and the validation via Typhoon HIL402 real-time emulator. Both HIL402 validation and simulation results show that the proposed control scheme recovers normal operation against voltage disturbance within approximately 1.2 milliseconds without overshoot with steady-state error near zero and significantly dampens the inherent voltage oscillation that occurs at the instant of DVR entrance and/or exit.

The damaged areas of brain tissues can be extracted by using segmentation methods, most of which are based on the integration of machine learning and data mining techniques. An important segmentation method is to utilize clustering techniques, especially the fuzzy C-means (FCM) clustering technique, which is sufficiently accurate and not overly sensitive to imaging noise. Therefore, the FCM technique is appropriate for multiple sclerosis diagnosis, although the optimal selection of cluster centers can affect segmentation. They are difficult to select because this is an NP-hard problem. In this study, the Harris Hawks optimization (HHO) algorithm was used for the optimal selection of cluster centers in segmentation and FCM algorithms. The HHO is more accurate than other conventional algorithms such as the genetic algorithm and particle swarm optimization. In the proposed method, every membership matrix is assumed as a hawk or an HHO member. The next step is to generate a population of hawks or membership matrices, the most optimal of which is selected to find the optimal cluster centers to decrease the multiple sclerosis clustering error. According to the tests conducted on a number of brain MRIs, the proposed method outperformed the FCM clustering and other techniques such as the k-NN algorithm, support vector machine, and hybrid data mining methods in accuracy.

With the rapid development of the Internet of Things (IoT), the curse of dimensionality becomes increasingly common. Feature selection (FS) is to eliminate irrelevant and redundant features in the datasets. Particle swarm optimization (PSO) is an efficient metaheuristic algorithm that has been successfully applied to obtain the optimal feature subset with essential information in an acceptable time. However, it is easy to fall into the local optima when dealing with high-dimensional datasets due to constant parameter values and insufficient population diversity. In the paper, an FS method is proposed by utilizing adaptive PSO with leadership learning (APSOLL). An adaptive updating strategy for parameters is used to replace the constant parameters, and the leadership learning strategy is utilized to provide valid population diversity. Experimental results on 10 UCI datasets show that APSOLL has better exploration and exploitation capabilities through comparison with PSO, grey wolf optimizer (GWO), Harris hawks optimization (HHO), flower pollination algorithm (FPA), salp swarm algorithm (SSA), linear PSO (LPSO), and hybrid PSO and differential evolution (HPSO-DE). Moreover, less than 8% of features in the original datasets are selected on average, and the feature subsets are more effective in most cases compared to those generated by 6 traditional FS methods (analysis of variance (ANOVA), Chi-Squared (CHI2), Pearson, Spearman, Kendall, and Mutual Information (MI)).

The rapid development of artificial intelligence and modern communication technology provides a powerful opportunity to realize the transformation, upgrading and high-quality development of exhibition management. To solve the optimal coverage problem of wireless network in the exhibition area, a new generation of artificial intelligence algorithm is introduced to propose a novel method for solving the network coverage problem based on Harris Hawk optimization (HHO) algorithm. From the perspective of maximizing coverage and minimizing coverage redundancy, the multi-objective coverage optimization problem of wireless network nodes is optimally extracted into a function and solved using the improved HHO (IHHO) algorithm. Through the simulation of the wireless network coverage in the exhibition area, the experimental results show that the comprehensive evaluation index score of the optimization algorithm reaches 98.03%, which is higher than the traditional particle swarm optimizer (PSO) and the standard HHO. The proposed IHHO algorithm can be effectively applied to the network coverage problem and has a greater advantage over the traditional optimization methods in terms of improving node coverage, and has great practicality.

In the context of the global low-carbon energy development strategy, the massive influx of renewable energy leads to the instability of the power system, so it is necessary to carry out effective scheduling strategy and management mechanism for energy. On this basis, the Integrated Energy System including Electricity/Heat/Storage (EHSIES) is discussed in this paper. First, an ordered clustering method using a combination of simulated annealing and improved profile coefficients for load evolution number approximation is proposed. Second, a hybrid load clustering algorithm based on hierarchical clustering algorithm and K-means++ is presented and four types of load scenarios are obtained. Third, an Improved Harris Hawk Optimization (IHHO) algorithm based on the hybrid strategy is proposed and solved to the model of EHSIES. Finally, under the premise of the dynamic carbon trading mechanism considering historical compliance and load aggregator management, the scheduling strategies of flexible electric loads and thermal loads participating in EHSIES are discussed and solved by CPLEX. The results show that the solution time of IHHO is 50% and 33.3% lower than that of particle swarm optimization (PSO) and Harris hawk optimization (HHO), and the number of iterations is 76.6% and 56.25% lower than that of PSO and HHO, respectively. In addition, considering a flexible load aggregator and a compliance-based dynamic carbon trading mechanism in EHSIES scheduling can effectively reduce the actual operating cost by 27.37%, the source-side energy spill rate by 68.75%, and the carbon emission by 51.92% and improve the operating efficiency and economic benefits of EHSIES.

Stability of Clostridium butyricum in biohydrogen production from non-sterile food waste

Owing to the depletion of the thermal resources and negative environmental implications related to the operation of the thermal generators, it has become increasingly important to optimize the system in such a way that a reliable, cost-effective and environmental friendly power system operation is achieved. This issue is tackled in this paper by introducing a hybrid Artificial Intelligence (AI) based approach in which Harris Hawk Optimization (HHO) algorithm is fused with the Particle Swarm Optimization (PSO). The proposed Hy(HHO-PSO) algorithm utilizes the effectiveness of HHO and avoids its tendency to get stuck in local optima by fusing it with PSO. The proposed algorithm was used to perform Optimal Power Flow (OPF) on IEEE 30 bus system, initially, by individually minimizing the fuel cost and emission; and then simultaneously minimizing both of these objectives. The results are compared with other modern solution methods which shows the superiority of the proposed algorithm over its contemporaries.

Voltage unbalance factor in three-phase power systems adversely affects induction motor performance and efficiency, particularly in industrial settings. Conventional efficiency tests based on IEEE 112 are intrusive and impractical for in-situ applications. This paper proposes a non-intrusive efficiency estimation approach for three-phase induction motors operating under unbalanced voltages, combining positive/negative sequence modeling, IEEE 112-F1/Form F2 loss calculations, and heuristic optimization. Four equivalent circuit parameters (stator leakage reactance, magnetizing reactance, core-loss resistance, and rotor resistance) are identified from terminal measurements and nameplate data. The framework is implemented using three heuristic algorithms, Particle Swarm Optimization (PSO), Harris Hawks Optimization (HHO), and Fox Optimization Algorithm (FOX), and experimentally validated on a 1.1 kW induction motor for four load factors (25%, 50%, 75%, 100%) and Voltage Unbalance Factor (VUF) levels between 0 and 5%. IEEE 112-A is used as the reference method. The results show that PSO and HHO reproduce IEEE 112-A efficiencies with high accuracy for medium and high load factors (LF ≥ 50%), with full-load deviations typically below 1% for all VUF levels, while FOX exhibits larger errors, particularly at light load. Furthermore, the experiments confirm that efficiency degradation due to VUF is most pronounced at low load factors, even when VUF remains within the 5% NEMA limit. The proposed framework enables practical in-situ efficiency monitoring under realistic unbalanced supply conditions without motor shutdown.

One of the main problems of power generation systems (either isolated or interconnected) is related to the frequency regulation and the continuous effort to keep the frequency as stable as possible with respect to various load fluctuations. The subject of this paper is the optimization of load-frequency controllers of single-area multi-source power generating systems. Specifically, a specific model which emerge from the international literature is presented to be studied. The main goal is to tune the controller of each generating source i.e. find their optimal gains through bio-inspired optimization algorithms. The algorithms which were applied are the particle swarm optimization (PSO) algorithm-used as a benchmark- and the Harris Hawk Optimization (HHO) algorithm, as the proposed one. The integral-in-time absolute error (ITAE) performance index is the corresponding objective function. The systems are being studied in the environment of Matlab/ Simulink and the derivation of the (sub)-optimal gains of the controllers result from the proper use of a custom written Matlab script which implements the optimization procedure. The results obtained show that the HHO performs better than those obtained through PSO, particularly in the case where the nonlinearity of the system under consideration increases.

The corona virus search optimizer for solving global and engineering optimization problems

In this paper, Particle Swarm Optimization (PSO) technique is applied to tune the Adaptive Neuro Fuzzy Controller (ANFIS) for vehicle suspension system. LQR controller is used to obtain the training data set for the vehicle suspension system. Subtractive clustering technique is used to formulate ANFIS which approximates the actuator output force as a function of system states. PSO algorithm search for optimal radii for subtractive clustering based ANFIS. Training is done off line and the cost function is based on the minimization of the error between actual and approximated output. Simulation results show that the PSO-ANFIS based vehicle suspension system exhibits an improved ride comfort and good road holding ability.

Optimizing feedforward neural networks using a modified weighted mean of vectors: Case study chemical datasets

Recycling tasks are the most effective method for reducing waste generation, protecting the environment, and boosting the overall national economy. The productivity and effectiveness of the recycling process are strongly dependent on the cleanliness and precision of processed primary sources. However, recycling operations are often labor intensive, and computer vision and deep learning (DL) techniques aid in automatically detecting and classifying trash types during recycling chores. Due to the dimensional challenge posed by pre-trained CNN networks, the scientific community has developed numerous techniques inspired by biology, swarm intelligence theory, physics, and mathematical rules. This research applies a new meta-heuristic algorithm called the artificial hummingbird algorithm (AHA) to solving the waste classification problem based on feature selection. However, the performance of the AHA is barely satisfactory; it may be stuck in optimal local regions or have a slow convergence. To overcome these limitations, this paper develops two improved versions of the AHA called the AHA-ROBL and the AHA-OBL. These two versions enhance the exploitation stage by using random opposition-based learning (ROBL) and opposition-based learning (OBL) to prevent local optima and accelerate the convergence. The main purpose of this paper is to apply the AHA-ROBL and AHA-OBL to select the relevant deep features provided by two pre-trained models of CNN (VGG19 & ResNet20) to recognize a waste classification. The TrashNet dataset is used to verify the performance of the two proposed approaches (the AHA-ROBL and AHA-OBL). The effectiveness of the suggested methods (the AHA-ROBL and AHA-OBL) is compared with that of 12 modern and competitive optimizers, namely the artificial hummingbird algorithm (AHA), Harris hawks optimizer (HHO), Salp swarm algorithm (SSA), aquila optimizer (AO), Henry gas solubility optimizer (HGSO), particle swarm optimizer (PSO), grey wolf optimizer (GWO), Archimedes optimization algorithm (AOA), manta ray foraging optimizer (MRFO), sine cosine algorithm (SCA), marine predators algorithm (MPA), and rescue optimization algorithm (SAR). A fair evaluation of the proposed algorithms’ performance is achieved using the same dataset. The performance analysis of the two proposed algorithms is applied in terms of different measures. The experimental results confirm the two proposed algorithms’ superiority over other comparative algorithms. The AHA-ROBL and AHA-OBL produce the optimal number of selected features with the highest degree of precision.