Solar-Based DG Allocation Using Harris Hawks Optimization While Considering Practical Aspects

Most of the generated electricity is lost in power loss while transmitting and distributing it to the consumer end. The power losses occurring in the distribution network cause deviation in voltage and lower stability due to increased load demand. The integration of multiple Distributed Generation (DG) will enable the existing radial electrical distribution network efficient by minimizing the power losses and improving the voltage profile. Metaheuristic optimization techniques provide a favorable solution for optimal location and sizing of DG in the distribution network. A novel modern metaheuristic Transient Search Optimization (TSO) algorithm, inspired by the electrical network’s transient response of storage components implemented in the proposed work. The TSO formulated optimal DGs allocation to minimize total active power loss, voltage deviation and enhance voltage stability index as minimization optimization problem satisfying various equality and inequality constraints. The installation of multiple DG units at unity, fixed, and optimal power factors were examined. The TSO algorithm’s effectiveness was tested on standard IEEE 33-bus and 69-bus radial distribution networks, including various operating events developed in the form of single and multi-objective fitness functions. The active power loss reduced to 94.29 and 94.71% for IEEE 33 and 69 bus distribution systems. The obtained results trustworthiness is confirmed by comparison with well-known optimization methods like Genetic Algorithm (GA), Particle Swarm Optimization (PSO), combined GA/PSO, Teaching Learning Based Algorithm (TLBO), Swine influenza model-based optimization with quarantine (SIMBO-Q), Multi-Objective Harris Hawks optimizer (MOHHO) and other provided in the literature. The presented numerical studies represent the usefulness and out-performance of the proposed TSO algorithm due to its exploration and exploitation optimization mechanisms for the DG allocation problem meticulously.

Survival exploration strategies for Harris Hawks Optimizer

Power losses and voltage deviations in distribution power networks (DPNs) are high since they carry more power demand than transmission power networks. Also, voltage deviation beyond the allowable range causes voltage stability problems in the DPN. The power loss (PL) in the DPN should be kept at the minimum level for the economic operation of the electric grid. Integrating distributed generation (DG) in appropriate sites of the power networks can minimize the power losses and voltage drops. An integrated optimization approach is proposed in this paper, by combining an analytical and metaheuristic algorithm to optimize the placement and sizing of multiple DGs. The active power loss sensitivity (APLS) index is an analytical mathematical computation approach used to identify the optimal bus locations for DG placement. The modified ant lion optimization (MALO) algorithm is applied to optimize the ratings of the DG systems. The MALO algorithm is proposed by adopting the Lévy flights (LF) pattern in the random walk process (RWP). LF representation of RWPs enhances the exploration phase of the ALO algorithm and helps to obtain the near-optimal solution. The proposed integrated approach optimizes multiple units of photovoltaic (PV) and wind turbine (WT) units to minimize the multi-objective function, including AP loss and voltage deviation (VD) minimizations. The effectiveness of the proposed integrated approach is validated on the IEEE 69-bus, 85-bus, and 118-bus radial DPNs. Besides, the simulation study is extended for ant lion optimization (ALO), BAT, and artificial bee colony (ABC) algorithms-based techniques. The integrated approach has reduced the total AP loss of the IEEE 69-bus and 85-bus radial DPN from 225 kW to 70.51 kW and 316.12 kW to 162.80 kW, respectively, for the optimized three PV DG units allocation. Likewise, the total AP loss of the 118-bus radial DPN is cut down from 1296.3 kW to 432.3 kW after the optimized five PV DG units allocation. Meanwhile, the total AP LOSS of the 69-bus, 85-bus, and 118-bus radial DPNs is reduced to 4.78 kW, 53.87 kW, and 112.2 kW, respectively, after the optimized WT DG allocation. Additionally, the optimized inclusion of multiple DG units significantly minimized the VD of the DPNs. The minimum VD of the 69-bus, 85-bus, and 118-bus test systems is reduced from 0.0908 p.u., 0.1297 p.u., and 0.1312 p.u. to 0.0174 p.u., 0.0384 p.u., and 0.0201 p.u., respectively, for the multiple PV unit allocations. Similarly, the minimum VDs of the 69-bus, 85-bus, and 118-bus radial DPNs are minimized to 0.0048 p.u., 0.0190 p.u., and 0.0093 p.u., respectively, following the multiple WT DG unit allocations. The simulation findings of the APLS-MALO integrated approach are related to the various optimization techniques. The comparative study reveals that the proposed integrated approach gives a more effective and efficient solution than ALO, BAT, ABC, and other optimization techniques. Finally, the simulation findings of the APLS-MALO integrated technique are verified via the calculation of conventional statistical metrics and the conduction of a non-parametric Wilcoxon test.

The ambulance routing problem is one of the capacitated ambulance routing problem forms. It deals with injuries and their requests for saving. Therefore, the main aim of the ambulance routing problem is to determine the minimum (i.e., optimum) required distances of between: 1) accident places and the ambulance station; 2) the location of the nearest hospital and the accident places. Although of the efforts proposed in the literature, determining the optimum route is crucial. Therefore, this article seeks to tackle ambulance vehicle routing in smart cities using Harris Hawks Optimization (HHO) algorithm. It attempts to take the victims as quickly as possible and confidently. Several engineering optimization problems confirm that HHO outperforms many well-known Swarm intelligence approaches. In our system, let’s use the node approach to produce a city map. Initially, the control station receives accident site information and sends it to the hospital and the ambulance. The HHO vehicle routing algorithm receives data from the driver; the data includes the location of the accident and the node position of the ambulance vehicle. Then, the driver’s shortest route to the accident scene by the HHO. The locations of the accident and hospital are updated by the driver once the car reaches the accident site. The fastest route (which results in the least travel time) to the hospital is then determined. The HHO can provide offline information for a potential combination of the coordinates of destination and source. Extensive simulation experiments demonstrated that the HHO can provide optimal solutions. Furthermore, performance evaluation experiments demonstrated the superiority of the HHO algorithm over its counterparts (SAODV, TVR, and TBM methods). Furthermore, for ten malicious nodes, the PDF of the algorithm was 0.91, which is higher than the counterparts

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.

An adaptive Harris hawks optimization (HHO) algorithm is proposed to solve the non-linearly constrained optimization problems. The algorithm, namely, instinctive reaction strategy based on HHO (IRSHHO), combines the instinctive reaction strategy (IRS) with the HHO algorithm. In IRSHHO, the increment of step length decreases with the increase in the iteration. Moreover, the energy of the prey is also considered to speed up the convergence in each iteration. The performance of the IRSHHO is investigated on five benchmark numerical examples. The results of IRSHHO provide very competitive results compared to other well-known algorithms. Furthermore, the IRSHHO is applied to optimize the auto drum fashioned brake problem. Results show that the IRSHHO can obtain the optimal solution with the braking efficiency factor that is increased by 27.872% from the initial design with the proposed IRSHHO method.

Abstract. The optimization of aerospace propulsion systems plays a pivotal role in enhancing performance and efficiency. This paper presents a comprehensive analysis of a 2D bypass dual throat nozzle, with a primary focus on optimizing two critical design parameters: expansion ratio and bypass width, to maximize the mass flow rate and facilitate thrust vectoring capabilities. An improved Harris Hawks Optimization (HHO) algorithm was leveraged in this research to achieve an optimal configuration for the nozzle. Furthermore, the complex relationship between design parameters and mass flow rate is approximated using a Radial Basis Function (RBF) surrogate model, allowing for effective optimization. Through extensive simulations and analyses, the effectiveness of the proposed methodology is demonstrated, showcasing significant improvements in nozzle performance compared to conventional approaches. Furthermore, the optimized configuration obtained through the HHO algorithm is systematically compared with other metaheuristic optimization algorithms, highlighting its superiority in achieving optimal solutions for thrust vectoring applications. This study advances aerospace propulsion technology by providing valuable insights into the optimization of bypass dual throat nozzles, thereby paving the way for enhanced performance and maneuverability in aerospace systems.

The world is transitioning to utilise distributed generation (DG) to reduce social, economic and environmental effects. As DG penetration increases, determining the associated impacts on distribution networks is vital. This paper proposes a concept around sphere of influence of single and multiple DG units through line current analysis and integrated Sankey and Geospatial visualisation approaches. With the increasing integration of renewable energy sources and the need for grid stability, it becomes imperative to analyse the sphere of influence of DGs in geographic context. The main contribution of the paper is to present the visualisation techniques to determine the sphere of influence of DGs within power networks by considering single and multiple DGs. The proposed study combines the power of Sankey visualisation, a flow diagram technique, with geospatial data to gain insights into the DG sphere. The advantage of the proposed method is it acts as a visual lens to determine DG clusters, flow of power, coherency analysis, voltage support, resource allocation, operational planning, and grid resilience assessment. The proposed method is tested using 33 bus distribution network and visualisation on a representative geospatial map is carried out using Python library and Tableau.

Segmentation of brain MRI using an altruistic Harris Hawks’ Optimization algorithm

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.

In order to solve the low-voltage problem of three-phase four-wire system in the low-voltage distribution network by using distributed generation (DG), this paper establishes the mathematical model of the DG configuration, and presents the DG placing method and configuration principle, considering the influence of unbalanced load, reactive power compensation and the existing DG of the network. For the phase voltage deviation and the phase power flow calculation, the mathematical model and the calculation method are given in this paper. According to the voltage deviation and the power flow under the maximum load condition, the placing site of DG is determined. Based on the expectation of the node voltage deviation percentage wanted to improved, the maximum current and maximum capacity of DG are deduced. In order to ensure the normal operation of the low-voltage distribution network, the voltage distributions are checked under the minimum load after conFigure the DG, and the minimum injection node current and minimum capacity of DG are determined in the case of minimum load. Taking the practical 0. 4kV three-phase four-wire network as an example, the DGs are configured to solve the low voltage problem. By the analysis and comparison of voltage distribution, the proposed placing DG method can be applied to different load cases, and it is an effective method to solve the low-voltage problem.

Several extreme weather disasters have demonstrated that grid resilience is a significant aspect of modern power systems to work upon. In event of grid failure, back up power sources must support the network. Thus, optimal planning of distributed generations (DGs) in power distribution networks need to be done so that in addition to grid power support, ancilliary services such as minimized real power losses and voltage deviations are attained. In the present work, combined resilience evaluation and analytic operational enhancement strategy has been studied in a radial power distribution network. To achieve overall minimal power losses and better voltage control, the Marine Predator (MPA) meta-heuristic optimization technique is adopted. The MPA is utilised to discover the best DG location and size at the same time. The proposed method is applicable to both single and multiple DG unit sizing and siting. Furthermore, to calculate the best size of DG unit, the suggested method just requires the results of the base case load flow. The proposed approach is tested on a IEEE 33-bus radial distribution test system.

In this paper, we consider the NP-hard problem of finding the minimum connected resolving set of graphs. A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the vertices in B. A resolving set B of G is connected if the subgraph induced by B is a nontrivial connected subgraph of G. The cardinality of the minimal resolving set is the metric dimension of G and the cardinality of minimum connected resolving set is the connected metric dimension of G. The problem is solved heuristically by a binary version of an enhanced Harris Hawk Optimization (BEHHO) algorithm. This is the first attempt to determine the connected resolving set heuristically. BEHHO combines classical HHO with opposition-based learning, chaotic local search and is equipped with an S-shaped transfer function to convert the continuous variable into a binary one. The hawks of BEHHO are binary encoded and are used to represent which one of the vertices of a graph belongs to the connected resolving set. The feasibility is enforced by repairing hawks such that an additional node selected from V\B is added to B up to obtain the connected resolving set. The proposed BEHHO algorithm is compared to binary Harris Hawk Optimization (BHHO), binary opposition-based learning Harris Hawk Optimization (BOHHO), binary chaotic local search Harris Hawk Optimization (BCHHO) algorithms. Computational results confirm the superiority of the BEHHO for determining connected metric dimension.

SummaryThe large integration of distributed generation (DG) and electric vehicles (EVs) facilitates daily life and reduces carbon emissions. However, it brings problems such as increased uncertainty and power electronic permeability to the distribution network. In this context, it is important to solve the line loss optimization problem as closely as possible to the real situation. Therefore, this paper solves the line loss optimization problem based on a probabilistic model of three‐phase unbalanced modern power electrical distribution network. Firstly, the paper proposes a frequency domain model of the distribution network and the optimization problem model. Then, to deal with the uncertainty of the system, the point estimation method (PEM) and Monte Carlo Simulation (MCS) are used. Finally, this paper proposes two optimization schemes considering the power quality when single and multiple DGs are integrated. The simulation results demonstrate that the PEM using the Gram–Charlier series expansion of the fourth‐order statistical moments to estimate the probability density function (PDF) is significantly less time consuming than the traditional MCS method while maintaining accuracy. The comparative analysis reveals that under the optimization scheme with single DG integration, the expected value of system line loss and expected line loss rate are reduced from 283.95 kW to 218.96 kW (22.89% reduction) and from 13.44% to 10.68%, respectively. Under the dual DG integrated optimization scheme, the expected value of line losses and expected line loss rate are reduced from 283.95 kW to 208.88 kW (26.4% reduction) and from 13.44% to 10.25%, respectively. However, after taking the uncertainty into account, the operational reliability of the system has been enhanced under different scenarios. In addition, it is found that the optimization approach incorporating multiple DG units can effectively mitigate the system line loss under diverse operational scenarios while enhancing the system's DG integration capability.

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.