A Computational Intelligent Analysis Scheme for Optimal Engine Behavior by Using Artificial Neural Network Learning Models and Harris Hawk Optimization

Proton exchange membrane fuel cell (PEMFC) is considered as propitious solution for an environmentally friendly energy source. A precise model of PEMFC for accurate identification of its polarization curve and in-depth understanding of all its operating characteristics attracted the interest of many researchers. In this paper, novel meta-heuristic optimization methods have been successfully applied to evaluate the unknown parameters of PEMFC models, particularly Harris Hawks’ optimization (HHO) and atom search optimization (ASO) techniques. The proposed optimization algorithms have been tested on three different commercial PEMFC stacks, namely BCS 500-W PEM, 500W SR-12PEM and 250W stack, under various operating conditions. The sum of square errors (SSE) between the results obtained by the application of the estimated parameters and the experimentally measured results of the fuel cell stacks was considered as the objective function of the optimization problem. In order to validate the effectiveness of the proposed methods, the results are compared with that obtained in studies. Moreover, the I/V curves obtained by the application of HHO and ASO showed a clear matching with data sheet curves for all the studied cases. Finally, PEMFC model based on HHO technique surpasses all compared algorithms in terms of the solution accuracy and the convergence speed.

Survival exploration strategies for Harris Hawks Optimizer

An efficient hybrid sine-cosine Harris hawks optimization for low and high-dimensional feature selection

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 paper deals with an optimization problem in an electricity distribution network with different types of distributed generation and a battery storage system in terms of a smart grid concept. The optimization problem considers two objectives, namely, the annual energy losses and the exchange of energy with the higher-level power grid. The decision variables of the problem are the allocation of the different distributed generation units and the battery storage system, the annual power profiles of the controllable distributed generation and the battery storage system, and the power factor profiles of the controllable and noncontrollable distributed generation. All decision variables are solved simultaneously in a single optimization problem. The variable load shapes of the grid consumers and the profiles of the photovoltaic and wind power systems are considered in the study. All data are observed at the annual level with hourly resolution. The problem solving method uses computational intelligence techniques, namely, metaheuristic optimization methods and artificial neural networks. The study proposes a framework for optimizing the decision variables in the planning phase of distributed generation and battery storage, and for controlling the variable power and power factor profiles based on an artificial neural network in the implementation phase. The optimization problem is solved with a power system simulation program and a metaheuristic optimizer in cosimulation synergy. The three cases of distributed generation and battery storage are considered simultaneously. The proposed method is applied to the test grid operator IEEE with 37 buses, and reductions in annual energy losses and energy exchange are obtained in the ranges 34–86% and 41–99%, respectively.

An analytical study of modified multi-objective Harris Hawk Optimizer towards medical data feature selection

IBJA: An improved binary DJaya algorithm for feature selection

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.

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

Performance analysis of Chaotic Multi-Verse Harris Hawks Optimization: A case study on solving engineering problems

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

This paper introduces a novel meta-heuristic algorithm named Rhinopithecus Swarm Optimization (RSO) to address optimization problems, particularly those involving high dimensions. The proposed algorithm is inspired by the social behaviors of different groups within the rhinopithecus swarm. RSO categorizes the swarm into mature, adolescent, and infancy individuals. Due to this division of labor, each category of individuals employs unique search methods, including vertical migration, concerted search, and mimicry. To evaluate the effectiveness of RSO, we conducted experiments using the CEC2017 test set and three constrained engineering problems. Each function in the test set was independently executed 36 times. Additionally, we used the Wilcoxon signed-rank test and the Friedman test to analyze the performance of RSO compared to eight well-known optimization algorithms: Dung Beetle Optimizer (DBO), Beluga Whale Optimization (BWO), Salp Swarm Algorithm (SSA), African Vultures Optimization Algorithm (AVOA), Whale Optimization Algorithm (WOA), Atomic Retrospective Learning Bare Bone Particle Swarm Optimization (ARBBPSO), Artificial Gorilla Troops Optimizer (GTO), and Harris Hawks Optimization (HHO). The results indicate that RSO exhibited outstanding performance on the CEC2017 test set for both 30 and 100 dimension. Moreover, RSO ranked first in both dimensions, surpassing the mean rank of the second-ranked algorithms by 7.69% and 42.85%, respectively. Across the three classical engineering design problems, RSO consistently achieves the best results. Overall, it can be concluded that RSO is particularly effective for solving high-dimensional optimization problems.

Solving non-convex economic load dispatch problem via artificial cooperative search algorithm

Nowadays, power production, reliability, quality, efficiency and penetration of renewable energy sources are amongst the most important topics in the power systems analysis. The need to obtain optimal power management and economical dispatch are expressed at the same time. The interest in extracting an optimum performance minimizing market clearing price (MCP) for the consumers and provide better utilization of renewable energy sources has been increasing in recent years. Due to necessity of providing energy balance while having the fluctuations in the load demand and non-dispatchable nature of renewable sources, implementing an energy management system (EMS) is of great importance in Microgrids (MG). The appearance of new technologies such as energy storage (ES) has caused increase in the effort to present new and modified optimization methods for power management. Precise prediction of renewable energy sources power generation can only be provided with small anticipation. Hence, for increasing the efficiency of the presented optimization algorithm in large-dimension problems, new methods should be proposed, especially for short-term scheduling. Powerful optimization methods are needed to be applied in such a way to achieve maximum efficiency, enhance the economic dispatch as well as provide the best performance for these systems. Thus, real-time energy management within MG is an important factor for the operators to guarantee optimal and safe operation of the system. The proposed EMS should be able to schedule the MG generation with minimum information shares sent by generation units. To achieve this ability, the present thesis proposes an operational architecture for real time operation (RTO) of a MG operating in both islanding and grid-connected modes. The presented architecture is flexible and could be used for different configurations of MGs in different scenarios. A general formula is also presented to estimate optimum operation strategy, cost optimization plan and the reduction of the consumed electricity combined with applying demand response (DR). The proposed problem is formulated as an optimization problem with nonlinear constraints to minimize the cost related to generation sources and responsive load as well as reducing MCP. Several optimization methods including mixed linear programming, pivot source, imperialist competition, artificial bee colony, particle swarm, ant colony, and gravitational search algorithms are utilized to achieve the specified objectives. The main goal of the thesis is to validate experimentally the design of the real-time energy management system for MGs in both operating modes which is suitable for different size and types of generation resources and storage devices with plug-and-play structure. As a result, this system is capable of adapting itself to changes in the generation and storage assets in real-time, and delivering optimal operation commands to the assets quickly, using a local energy market (LEM) structure based on single side or double side auction. The study is aimed to figure the optimum operation of micro-sources out as well as to decrease the electricity production cost by hourly day-ahead and real time scheduling. Experimental results show the effectiveness of the proposed methods for optimal operation with minimum cost and plug-and-play capability in a MG. Moreover, these algorithms are feasible from computational viewpoints while having many advantages such as reducing the peak consumption, optimal operation and scheduling the generation unit as well as minimizing the electricity generation cost. Furthermore, capabilities such as the system development, reliability and flexibility are also considered in the proposed algorithms. The plug and play capability in real time applications is investigated by using different scenarios.

The key contribution of this paper is to determine the optimal operating parameters of the methane reforming process for hydrogen production. The proposed strategy contained two phases: ANFIS modelling and optimization. Four input controlling parameters were considered to increase the hydrogen: irradiation time (min), metal loading, methane concentration, and steam concentration. In the first phase, an ANFIS model was created with the help of the experimental data samples. The subtractive clustering (SC) technique was used to generate the fuzzy rules. In addition, the Gaussian-type and weighed average were used for the fuzzification and defuzzification methods, respectively. The reliability of the resulting model was assessed statistically by RMSE and the correlation (R2) measures. The small RMSE value and high R2 value of testing samples assured the correctness of the modelling phase, as they reached 0.0668 and 0.981, respectively. Based on the robust model, the optimization phase was applied. The slime mold algorithm (SMA), as a recent as well as simple optimizer, was applied to look for the best set of parameters that maximizes hydrogen production. The resulting values were compared by the findings of three competitive optimizers, namely particle swarm optimization (PSO), Harris hawks optimization (HHO), and evolutionary strategy HHO (EESHHO). By running the optimizers 30 times, the statistical results showed that the SMA obtained the maximum value with high mean, standard deviation, and median. Furthermore, the proposed strategy of combining the ANFIS modelling and the SMA optimizer produced an increase in the hydrogen production by 15.7% in comparison to both the experimental and traditional RSM techniques.