Archimedes optimization algorithm: a new metaheuristic algorithm for solving optimization problems

Honey Badger Algorithm: New metaheuristic algorithm for solving optimization problems

The most crucial function in drilling wells is the rate of penetration, which is modeled by many researchers, and the best one is Young-Bourgyen model, which is used in this study. Eight factors affecting rate of penetration have been studied and approved in developing a mathematical equation that shows the combined effects of these variables on rate of penetration optimization. This paper presents an efficient way to find the optimum values for parameters of the Young-Bourgyen model using metaheuristic algorithms. An actual drilling data was used from Khangiran field to calculate the difference between the actual penetration rate and the predicted one by different optimization algorithms. Particle swarm optimization, dynamic differential annealing optimization, artificial bee colony, gray wolf optimization, Harris hawk's optimization, flower pollination algorithm, firefly algorithm, whale optimization algorithm, and sine cosine algorithm are used to find best possible solution.

This research proposes an improved Puma optimization algorithm (IPuma) as a novel dynamic reconfiguration tool for a photovoltaic (PV) array linked in total-cross-tied (TCT). The proposed algorithm utilizes the Newton-Raphson search rule (NRSR) to boost the exploration process, especially in search spaces with more local regions, and boost the exploitation with adaptive parameters alternating with random parameters in the original Puma. The effectiveness of the introduced IPuma is confirmed through comprehensive evaluations on the CEC’20 benchmark problems. It shows superior performance compared to both established and modern metaheuristic algorithms in terms of effectively navigating the search space and achieving convergence towards near-optimal regions. The findings indicated that the IPuma algorithm demonstrates considerable statistical promise and surpasses the performance of competing algorithms. In addition, the proposed IPuma is utilized to reconfigure a 9 × 9 PV array that operates under different shade patterns, such as lower triangular (LT), long wide (LW), and short wide (SW). In addition to other programmed approaches, such as the Whale optimization algorithm (WOA), grey wolf optimizer (GWO), Harris Hawks optimization (HHO), particle swarm optimization (PSO), gravitational search algorithm (GSA), biogeography-based optimization (BBO), sine cosine algorithm (SCA), equilibrium optimizer (EO), and original Puma, the indicated method is contrasted to the traditional configurations of TCT and Sudoku. In addition, the metrics of mismatch power loss, maximum efficiency improvement, efficiency improvement ratio, and peak-to-mean ratio are calculated to assess the effectiveness of the indicated approach. The proposed IPuma improved the generated power by 36.72%, 28.03%, and 40.97% for SW, LW, and LT, respectively, outperforming the TCT configuration. In addition, it achieved the best maximum efficiency improvement among the algorithms considered, with 26.86%, 21.89%, and 29.07% for the examined patterns. The results highlight the superiority and competence of the proposed approach in both convergence rates and stability, as well as applicability to dynamically reconfigure the PV system and enhance its harvested energy.

zToday, Android malware threats and attacks are rapidly increasing due to their use and popularity.Therefore, the need for systems effectively detecting malware is also increasing day by day.This study proposes the use of various trending metaheuristic algorithms for optimal feature selection (FS) in the detection of Android malware.For this purpose, the ten most prominent recent metaheuristic algorithms (RMAs) for feature selection such as Artificial Bee Colony Algorithm (ABC), Firefly Algorithm (FA), Grey Wolf Optimisation (GWO), Ant Lion Optimisation (ALO), Crow Search Algorithm (CSA), Sine Cosine Algorithm (SCA), Whale Optimisation Algorithm (WOA), Salp Swarm Algorithm (SSA), Harris Hawk Optimization (HHO) and Butterfly Optimization Algorithm (BOA) were used for feature selection in this study.The efficiency of these algorithms is evaluated with five different machine learning (ML) methods on two well-known datasets of Android applications .The results obtained are also compared with five well-known and widely used conventional metaheuristic algorithms (CMAs) for solving this problem.Extensive experimental results show that incorporating RMA into Android malware detection is a valuable approach. Gnmzde Android kt amal yazlm tehdit ve saldrlar, kullanmlar ve poplerlikleri nedeniyle hzla artmaktadr. Bu nedenle, kt amal yazlmlar etkili bir ekilde tespit edebilecek sistemlere olan ihtiya da gn getike artmaktadr. Bu alma, Android kt amal yazlmlarn tespitinde optimum zellik seimi (FS) iin trend olan eitli meta-sezgisel algoritmalarn sarmalama yntemi ile kullanlmasn nermektedir. Bu amala, bu almada Yapay Ar Kolonisi Algoritmas (ABC), Ate Bcei Algoritmas (FA), Gri Kurt Optimizasyonu (GWO), Karnca Aslan Optimizasyonu (ALO), KargaArama Algoritmas (CSA), Sins Kosins Algoritmas (SCA), Balina Optimizasyon Algoritmas (WOA), Salp Sr Algoritmas (SSA), Harris ahin Optimizasyonu (HHO) ve Kelebek Optimizasyonu Algoritmas (BOA) gibi zellik seiminde en ne kan on gncel meta-sezgisel algoritma (RMA) kullanlmtr.Bu algoritmalarn verimlilii, Android uygulamalarnn iyi bilinen iki veri kmesi (Drebin-215 ve Malgenome-215) zerinde be farkl makine renmesi (ML) yntemi ile deerlendirilmitir.Ayrca, elde edilen sonular bu problemin zmnde yaygn olarak kullanlan ve iyi bilinen be geleneksel metasezgisel algoritma (CMAs) ile de karlatrlmtr.Kapsaml deneysel sonular, RMA'nn Android kt amal yazlm tespitine dahil edilmesinin deerli bir yaklam olduunu gstermektedir.

An enhanced Archimedes optimization algorithm based on Local escaping operator and Orthogonal learning for PEM fuel cell parameter identification

In this study, the modification of the Deb feasibility method is considered to solve the constrained optimization problems. In the developed modified Deb feasibility constraint method, the third rule in its procedure was revised in order to increase the performance of the Deb feasibility constraint handling method. The innovation in the method is based on generating a new individual by using both possible solutions that violate the constraints in the method used for solving the problem. In detail, discussions were given about the application and usefulness of six constrained handling techniques. Furthermore, genetic algorithm, particle swarm optimization, Harris hawks optimization, whale optimization algorithm, grey wolf optimization and sine cosine algorithms were applied to both various benchmark functions and also different engineering application problems such as pressure vessel design, welded beam design, speed reducer design and active filter design. Overall the experimental results show that modified Deb feasibility constraint handling technique is more robust and efficient than Deb feasibility technique and most of the other constraint handling techniques.

As research in alternate energy sources is growing, solar radiation is catching the eyes of the research community immensely. Since solar energy generation depends on uncontrollable natural variables, without proper forecasting, this energy source cannot be trusted. For this forecasting, the use of machine learning algorithms is one of the best choices. This paper proposed an optimized solar radiation forecasting ensemble model consisting of pre-processing and training ensemble phases. The training ensemble phase works on an advanced sine cosine algorithm (ASCA) using Newton’s laws of gravity and motion for objects (agents). ASCA uses sine and cosine functions to update the agent’s position/velocity components by considering its mass. The training ensemble model is then developed using the k-nearest neighbors (KNN) regression. The performance of the proposed ensemble model is measured using a dataset from Kaggle (Solar Radiation Prediction, Task from NASA Hackathon). The proposed ASCA algorithm is evaluated in comparison with the Particle Swarm Optimizer (PSO), Whale Optimization Algorithm (WOA), Genetic Algorithm (GA), Grey Wolf Optimizer (GWO), Squirrel Search Algorithm (SSA), Harris Hawks Optimization (HHO), Hybrid Greedy Sine Cosine Algorithm with Differential Evolution (HGSCADE), Hybrid Modified Sine Cosine Algorithm with Cuckoo Search Algorithm (HMSCACSA), Marine Predators Algorithm (MPA), Chimp Optimization Algorithm (ChOA), and Slime Mould Algorithm (SMA). Obtained results of the proposed ensemble model are compared with those of state-of-the-art models, and significant superiority of the proposed ensemble model is confirmed using statistical analysis such as ANOVA and Wilcoxon’s rank-sum tests.

This paper presents the application of an active energy management strategy to a hybrid system consisting of a proton exchange membrane fuel cell (PEMFC), battery, and supercapacitor. The purpose of energy management is to control the battery and supercapacitor states of charge (SOCs) as well as minimizing hydrogen consumption. Energy management should be applied to hybrid systems created in this way to increase efficiency and control working conditions. In this study, optimization of an existing model in the literature with different meta-heuristic methods was further examined and results similar to those in the literature were obtained. Ant lion optimizer (ALO), moth-flame optimization (MFO), dragonfly algorithm (DA), sine cosine algorithm (SCA), multi-verse optimizer (MVO), particle swarm optimization (PSO), and whale optimization algorithm (WOA) meta-heuristic algorithms were applied to control the flow of power between sources. The optimization methods were compared in terms of hydrogen consumption and calculation time. Simulation studies were conducted in Matlab/Simulink R2020b (academic license). The contribution of the study is that the optimization methods of ant lion algorithm, moth-flame algorithm, and sine cosine algorithm were applied to this system for the first time. It was concluded that the most effective method in terms of hydrogen consumption and computational burden was the sine cosine algorithm. In addition, the sine cosine algorithm provided better results than similar meta-heuristic algorithms in the literature in terms of hydrogen consumption. At the same time, meta-heuristic optimization algorithms and equivalent consumption minimization strategy (ECMS) and classical proportional integral (PI) control strategy were compared as a benchmark study as done in the literature, and it was concluded that meta-heuristic algorithms were more effective in terms of hydrogen consumption and computational time.

Lévy flight distribution: A new metaheuristic algorithm for solving engineering optimization problems

A modified weighted mean of vectors optimizer for Chronic Kidney disease classification

Many real-world problems demand optimization, minimization of costs and maximization of profits, and meta-heuristic algorithms have proficiently proved their ability to achieve optimum results. This study proposes an alternative algorithm of Lévy Flight Distribution (LFD) by integrating Opposition-based learning (OBL) operator, termed LFD-OBL, for resolving intrinsic drawbacks of the canonical LFD. The proposed approach adopts OBL operator for catering search stagnancy to ensure faster convergence rate. We validate the usefulness of our approach through IEEE CEC’20 test suite, and compare results with original LFD and several other counterparts such as Moth-flame optimization, whale optimization algorithm, grasshopper optimisation algorithm, thermal exchange optimization, sine-cosine algorithm, artificial ecosystem-based optimization, Henry gas solubility optimization, and Harris’ hawks optimization. To further validate the efficiency of LFD-OBL, we apply it on parameters optimization of Solar Cell based on the Three-Diode Photovoltaic model. The qualitative and quantitative results of all the experiments performed in this study suggest superiority of the proposed method.

A considerable number of intermittent renewable resources like photovoltaic generation and wind energy when integrated into the conventional grid technology causes serious issues in the power systems like frequency instability. So an intelligent controller is desired for steady and reliable operation of the electric grid. This paper deals with the frequency control of hybrid power system (HPS) employing a sine cosine adopted Harris' hawks optimization (SCaHHO) technique. The proposed technique aims at improving the performance of the original Harris' hawks optimization (HHO) algorithm by incorporating a sine and cosine function in the calculation of escaping energy and chances of escaping of prey respectively to emphasize the exploitation process of hawks. The effectiveness of the SCaHHO technique is validated with the novel HHO technique as well as moth flame optimization, sine cosine algorithm, grey wolf optimization, salp swarm algorithm and gravitational search algorithm using 23 standard test functions. A non-parametric statistical investigation is also carried out to verify the effectiveness of the suggested technique. Later the SCaHHO technique is utilized to tune a newly proposed adaptive fuzzy proportional integral derivative controller (AFPID) for frequency control of HPS. The suggested SCaHHO-tuned AFPID controller achieves an improved control action by moderating the frequency fluctuations as compared to the PID controller. Hardware-in-loop validation of the proposed load frequency control scheme is also carried out using OPAL-RT to measure its fidelity with the numerical simulation results.

Optimal reactive power dispatch (ORPD) has a crucial impact to enhance safety, reliability, and economical operation of the electric power system. ORPD is a non-linear, non-convex and mixed variable problem, which has been solved by many researchers via different meta-heuristic algorithms during the last decade. In this work, a novel algorithm named sine-cosine algorithm (SCA) is utilized to solve ORPD problem by considering both dependent and independent control variable constraints. SCA has been tested and validated on standard 14, 30 and 57-bus power systems. To validate the superiority of proposed algorithm, the outcomes obtained through SCA are compared with recent published results attained through particle swarm optimization (PSO), modified Gaussian barebones teaching–learning based optimization (BBTLBO), ant bee colony optimization (ABCO), whale optimization algorithm (WOA) and backtracking search algorithms (BSA). The results attained using SCA show the improvement in the power losses minimization. Thus, with standard 14-bus system, the power losses are minimized from 0.04% to 4.78%. While, using standard 30-bus, the power losses are minimized from 0.4% to 3.4% and with standard 57-bus, power losses are reduced from 0.9% to 1.99%. Furthermore, a comparative analysis with 30 independent runs on the above-mentioned bus systems is performed to examine the functioning of the proposed method in terms of probability density function (PDF) and cumulative density function (CDF). For such analysis, well-known meta-heuristic algorithms such as PSO, WOA, differential evolution (DE) are compared with proposed SCA in solving the ORPD problem. The results of this analysis clearly show that proposed algorithm is robust, effective, and computationally easy in solving the ORPD problem compared to the existing meta-heuristic algorithms.

Task scheduling is one of the most significant challenges in the cloud computing environment and has attracted the attention of various researchers over the last decades, in order to achieve cost-effective execution and improve resource utilization. The challenge of task scheduling is categorized as a nondeterministic polynomial time (NP)-hard problem, which cannot be tackled with the classical methods, due to their inability to find a near-optimal solution within a reasonable time. Therefore, metaheuristic algorithms have recently been employed to overcome this problem, but these algorithms still suffer from falling into a local minima and from a low convergence speed. Therefore, in this study, a new task scheduler, known as hybrid differential evolution (HDE), is presented as a solution to the challenge of task scheduling in the cloud computing environment. This scheduler is based on two proposed enhancements to the traditional differential evolution. The first improvement is based on improving the scaling factor, to include numerical values generated dynamically and based on the current iteration, in order to improve both the exploration and exploitation operators; the second improvement is intended to improve the exploitation operator of the classical DE, in order to achieve better results in fewer iterations. Multiple tests utilizing randomly generated datasets and the CloudSim simulator were conducted, to demonstrate the efficacy of HDE. In addition, HDE was compared to a variety of heuristic and metaheuristic algorithms, including the slime mold algorithm (SMA), equilibrium optimizer (EO), sine cosine algorithm (SCA), whale optimization algorithm (WOA), grey wolf optimizer (GWO), classical DE, first come first served (FCFS), round robin (RR) algorithm, and shortest job first (SJF) scheduler. During trials, makespan and total execution time values were acquired for various task sizes, ranging from 100 to 3000. Compared to the other metaheuristic and heuristic algorithms considered, the results of the studies indicated that HDE generated superior outcomes. Consequently, HDE was found to be the most efficient metaheuristic scheduling algorithm among the numerous methods researched.

Gas lift is one of the most commonly used artificial lift method in oil producing wells. The technique requires constant Optimization of injection gas quantity in the well to maximize oil production. High pressure Lift Gas allocation is an important step in optimization process to reduce the investment on costly and scarce lift gas and maximize oil recovery. In this work, an attempt is made to optimize a group of wells with application of an innovative metaheuristic algorithm. The objective function can be either the generated profit or total produced oil as a function of injected gas. To achieve maximum recovery from gas-lifted wells, it's essential to identify the optimum injection rate for specific facility constraints, including gas availability, maximum injection depth, and compression capabilities. Normally, these parameters are unchangeable because of prior selection and installation, except when optimization techniques are implemented in the design phase. The Gas Lift Performance Curves (GLPC) are the main design element used for optimized gas injection. These curves are generated by modelling wells in a multiphase steady-state simulator. After building model, sensitivity analysis is run, and the curves are generated. In this work, the common workflow to generate GLPC is followed. Then, a new correlation for GLPC is suggested with help of a bio-inspired meta heuristic Whale Optimization Algorithm (WOA). The correlation is then used to formulate a case study for few wells located in Caspian Sea. R-score and Root Mean Square Error (RMSE) values compared. Wells and PVT models are used to create a simulation. The optimization problem is mathematically formulated using stochastic optimization techniques. The new correlation is used to fit the GLPC with WOA. A set of Pareto Optimal solutions are derived. Results of WOA is compared with other two potential algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) to obtain the global optimum of the distribution of a limited gas lift quantity. The advantages of WOA over PSO and GA is discussed, and the optimum gas allocation is obtained. The correlation outperforms all other models and also bring an injection gas saving of 10-15% and improved oil production to the extent of 5-10% with an automated WOA algorithm application. Compared to the traditionally applied numerical methods for gas allocation and optimization, AI meta-heuristic algorithms are considered as revolutionary methods, offering more solutions to gas allocation problems, which can solve non-linear multi-objective optimization problems quickly and more accurately with fast convergence. WOA is simpler, faster and outperforms PSO and GA and yield maximum production at minimum cost.