Survival exploration strategies for Harris Hawks Optimizer

Joint Opposite Selection (JOS): A premiere joint of selective leading opposition and dynamic opposite enhanced Harris’ hawks optimization for solving single-objective problems

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

Efficient fractional-order modified Harris hawks optimizer for proton exchange membrane fuel cell modeling

The restructuring of power systems and the ever-increasing demand for electricity have given rise to congestion in power networks. The use of distributed generators (DGs) may play a significant role in tackling such issues. DGs may be integrated with electrical power networks to regulate the drift of power in the transmission lines, thereby increasing the power transfer capabilities of lines and improving the overall performance of electrical networks. In this article, an effective method based on the Harris hawks optimization (HHO) algorithm is used to select the optimum capacity, number, and site of solar-based DGs to reduce real power losses and voltage deviation. The proposed HHO has been tested with a complex benchmark function then applied to the IEEE 33 and IEEE 69 bus radial distribution systems. The single and multiple solar-based DGs are optimized for the optimum size and site with a unity power factor. It is observed that the overall performance of the systems is enhanced when additional DGs are installed. Moreover, considering the stochastic and sporadic nature of solar irradiance, the practical size of DG has been suggested based on analysis that may be adopted while designing the actual photovoltaic (PV) plant for usage. The obtained simulation outcomes are compared with the latest state-of-the-art literature and suggest that the proposed HHO is capable of processing complex high dimensional benchmark functions and has capability to handle problems pertaining to electrical distribution in an effective manner.

Bearing is one of the most fundamental components of rotary machinery, and its fatigue life is a crucial factor in designing. The design optimization of tapered roller bearing (TRB) is a complex design problem because various arrays of designing parameters and functional requirements should be fulfilled. Since there are many design variables and nonlinear constraints, presenting an optimal design of TRBs poses some challenges for metaheuristic algorithms. The Harris hawks optimization (HHO) algorithm is a robust nature-inspired method with unique exploitation and exploration phases due to its time-varying structure. However, this metaheuristic algorithm may still converge to local optima for more challenging problems such as the design of TRBs. Therefore, this study aims to improve the accuracy and efficiency of the shortcomings of this algorithm. The performance of the proposed algorithm is first evaluated for the TRB optimization problem. The TRB optimization design has nine design variables and 26 constraints because of geometrical dimensions and strength conditions. The productivity of the proposed method is compared with diverse metaheuristic algorithms in the literature. The results demonstrate the significant development of dynamic load capacity in comparison to the standard value. Furthermore, the enhanced version of the HHO algorithm presented in this study is benchmarked with various well-known engineering problems. For supplementary materials regarding algorithms in this research, readers can refer to https://aliasgharheidari.com.

Feature selection is a popular issue in machine learning, which involves selecting the most effective features from the original features to reduce the dimensionality of the dataset. It is an important means to improve the performance of learning algorithms and a key data preprocessing step in pattern recognition. An improved feature selection algorithm for Harris Hawk Optimization (HHO) is proposed to address the shortcomings of the algorithm. First, self-adaptive elite opposition-based learning strategy is introduced in the exploration phase to initialize the position of Harris hawk population. Secondly, using logarithmic inertia weight to improve the update formula of escape energy, introducing the number of iterations into the jump distance, adjusting the search distance of HHO using step size adjustment parameters, and balancing exploration and development capabilities; On this basis, an improved HHO algorithm was designed to avoid the HHO algorithm falling into local optima. Then, a quadratic transfer function was introduced to update the binary and population positions of the improved HHO algorithm, and an IHHO algorithm was designed to solve feature selection problems. Experiments were conducted on five common benchmark datasets including Wine, Segment, Spectfheart, Sonar, and Ionosphere, and the results showed that the improved algorithm can effectively improve classification accuracy and select the optimal features.

IBJA: An improved binary DJaya algorithm for feature selection

In this article, an enhanced arithmetic optimization algorithm (EAOA) is utilized to resolve the optimal reactive power dispatch problem (ORPD) of power plants, which is a non‐linear, non‐smooth, complex optimization problem. Typically, it is formulated as a constrained optimal power flow problem. This paper utilizes the gamma distribution to generate initial random solutions, which improves the accuracy of the arithmetic optimization algorithm (AOA). The original mode of deviation of math optimizer accelerated (MOA) is replaced with the improved pattern of change in the prey's energy in Harris Hawk Optimization (HHO), which creates a superior balance between the exploration and exploitation of AOA. This paper implements logarithmic and exponential operators in place of division and multiplication operators, which improves the exploration ability of AOA and also utilizes the gamma distribution in the generation of random numbers, which are essential for the selection of operators in both search phase which enlarges the search area and improves the global optimum solution. A parametric analysis is performed on 10 benchmark functions for nine possible values before selecting the algorithm's initial parameters. A comparative statistical analysis along with Friedman's test is carried out on 23 benchmark functions to test the overall performance as compared to Sine‐Cosine Algorithm (SCA), Swarm‐Salp Algorithm, Grey Wolf Optimization (GWO), and basic Arithmetic Optimization Algorithm (AOA). EAOA is applied to the IEEE 57 bus system for optimal reactive power control. EAOA reports a significant reduction in a power loss of 23.2 MW, which is 18.596% smaller than the system's base case power loss of 28.5 MW as compared to Artificial Bee Colony (ABC), Firefly Algorithm (FA), Bat‐inspired Algorithm (BAT), Cuckoo Search Algorithm (CSA), and Modified Ant Lion Algorithm (MALO) which reports a reduction in a power loss of 14.73%, 15.43%, 15.43%, 16.14%, and 16.14% respectively. EAOA reduces the TVD to 0.76 pu, compared to ABC, FA, BAT, CSA, and MALO, which minimizes the TVD to 0.84, 0.82, 0.88, 0.82, and 0.79, respectively. This proves that EAOA performs better in resolving ORCP problems regarding the quality of solutions and convergence. A detailed comparative statistical analysis of EAOA with CSA, BAT, and WOA (Whale Optimization Algorithm) is conducted in terms of Probability Distribution Functions (PDF) and Cumulative Distribution Functions (CDF), along with the Wilcoxson sign rank test, which proves that EAOA gives the solution with a higher degree of precision.

An effective design of hybrid renewable energy system using an improved Archimedes Optimization Algorithm: A case study of Farafra, Egypt

Snake Optimizer: A novel meta-heuristic optimization algorithm

In order to assure accurate modelling, this study presents a new technique for appropriately modelling and simulating a proton exchange membrane fuel cell (PEMFC) system. The PEMFC is a cleaner and more sustainable energy source as compared to fossil fuels. The fundamental idea is to minimize the sum of squared error (SSE) between the estimated and measured output voltage for the Ballard Mark V model in order to identify the model parameters of PEMFC stacks as efficiently as possible using a newly developed meta-heuristic called enhanced efficient optimization algorithm (EINFO). The proposed optimizer is considered an enhanced version of the original INFO algorithm. By balancing the exploration and exploitation phases better, the EINFO algorithm is intended to improve the performance of the original INFO approach and prevent local optima. The new method was tested on 23 benchmark functions and compared to the original INFO algorithm as well as other recently evolved optimizers. The algorithm is examined and compared with some literature meta-heuristics, including the particle swarm optimization, sine cosine algorithm, dragonfly algorithm, atom search optimization, Harris hawks optimization, and efficient optimization algorithm, using 50 independent runs, in terms of convergence speed and least SSE. When compared to other methods, the final findings show that, the suggested technique achieves the fastest convergence speed.

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

ABSTRACTThree‐dimensional path planning for UAV in complex terrains and obstacle‐limited areas is one of the major challenges faced during mission execution, requiring a simple yet effective algorithm. To solve such problems, an improved Sand Cat Swarm Optimization (SCSO) is proposed, addressing the issue where traditional SCSO is prone to getting stuck in local optima. In this improved approach, a nonlinear adjustment mechanism based on a dynamic factor k is introduced to better balance the exploration and exploitation phases. Additionally, the predatory attack strategy of Harris's Hawks was introduced to improve the position update formula during the exploration phase of the SCSO, thus enhancing the algorithm's convergence speed. A new variant of the SCSO, named HKSCSO, is proposed and applied to UAV path planning. Cost functions are introduced to evaluate path length, flight altitude, and angle comprehensively. HKSCSO's performance was tested in three 3D urban environments, showing faster convergence and safer paths compared to SCSO, Harris's Hawks Optimization (HHO), Particle Swarm Optimization (PSO), Seagull Optimization Algorithm (SOA), Whale Optimization Algorithm (WOA), Parrot Optimizer (PO), and Mantis Search Algorithm (MSA). These results indicate HKSCSO's potential as an effective solution for UAV three‐dimensional path planning.

The rates at which strikes, riots and protests are increasing these days is alarming and a matter of grave concern. In order to understand the intensity of a protest and eventually take steps in order to minimize massive possible damage, it is critical to analyze the sentiments towards that particular event. Previous works in this field have majorly used traditional machine learning approaches. However, we believe that incorporating nature-inspired algorithms along with BERT for feature selection can greatly improve the accuracy. In this work, we have proposed a novel approach wherein we use an amalgamation of BERT and nature-inspired algorithms to perform sentiment analysis of major protests in India. For this, we combine the feature extraction capabilities of BERT with both swarm intelligence as well as evolutionary algorithms. For comparative study, we have taken a model which only uses BERT along with KNN and compared it with models which use nature inspired algorithms as an additional step to perform feature selection. We have achieved remarkable improvement in accuracy by incorporating nature inspired algorithms with the highest improvement of 17.78% in classifying sentiments by using the Grey-Wolf optimizer. Significant improvements in accuracy were also observed by using Particle Swarm Optimization (PSO), Harris Hawk Optimization (HHO) and Genetic Algorithm (GA). Snscrape has been used to extract tweets of major protests in India. Our results accurately correspond to the actual intensity of devastation that was observed during those protests.

Development of adaptive neuro fuzzy inference system –Evolutionary algorithms hybrid models (ANFIS-EA) for prediction of optimal groundwater exploitation