The aim of the present study is to predict audit failure using Metaheuristic Algorithms in companies listed on the Tehran Stock Exchange. To achieve this objective, 1, 848 firm-year observations (154 companies over 12 years) were collected from the annual financial reports of companies listed on the Tehran Stock Exchange during the period from 2011 to 2022. In this study, four Metaheuristic Algorithms (including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Bee Colony Optimization (BCO)) were utilized, as well as two methods for selecting the final research variables (the two-sample t-test and the forward stepwise selection method) to create the model. The results from the Metaheuristic Algorithms indicate that the overall accuracy of the GA, PSO, ACO, and BCO Algorithms is 95. 3%, 94. 5%, 90. 6%, and 92. 8%, respectively, demonstrating the superiority of the Genetic Algorithm (GA) compared to other Metaheuristic Algorithms. Furthermore, the overall results from the variable selection methods indicate the efficiency of the stepwise method. Therefore, in companies listed on the Tehran Stock Exchange, the stepwise method and the Genetic Algorithm (GA) provide the most efficient model for predicting audit failure.

This paper presents the application of Metaheuristic methods to the minimum crossing number problem for the first time. These Algorithms including particle swarm optimization, improved ray optimization, colliding bodies optimization and enhanced colliding bodies optimization. For each method, a pseudo code is provided. The crossing number problem is NP-hard and has important applications in engineering. The proposed Algorithms are tested on six complete graphs and eight complete bipartite graphs and their results are compared with some existing methods.

This research article describes the frequency regulation of an interconnected power system that includes wind energy systems and thermal non-reheat systems, with a proportional integral derivative (PID) controller optimized using Metaheuristic Algorithms such as Genetic-Algorithm (GA), Harmony-Search-Algorithm (HSA), Bat-Algorithm (BA), and Flower-Pollination-Algorithm (FPA). With the demand for precisely efficient energy systems growing, system engineers are increasingly looking for the finely optimized control solution that also has the benefit of faster convergence and avoids entrapment in local minimal. To minimize the fitness function which is based on ITAE (Integral of Time multiplied Absolute Error) criteria composed of frequency and tie-line power changes, we have obtained an optimum solution in terms of PID controller gain values using the Metaheuristics optimizing techniques. Change in frequency in area 1, deviation in tie-line power, and change in frequency in area 2 obtained from different techniques are compared. The results obtained by simulating MATLAB/Simulink convey that PID controller gain values optimized using the HSA technique provide better dynamic performance compared to BA, FPA and GA techniques. The simulation results have been experimentally validated using hardware-in-loop (HIL) on a real-time simulator based on field-programmable gate arrays (FPGA). The HSA optimized PID controller is used to investigate the robustness of the system by Step-Load-Perturbation (SLP) and Random Step Load Pattern (RSLP). Results obtained by running simulation also show that the HSA optimized PID controller for the same optimized gain value can withstand the SLP and RSLP variation made in the system.

Choosing a stock portfolio is always one of the most important issues for investors. Theoretically, selecting a stock portfolio can be solved by minimizing risk assumptions with the help of mathematical relationships, but with the variety of choices in the capital market, mathematical relationships alone are not an effective solution. The variety of investment tools and the differences in the functionality of investors’ complexity have complicated the selection process. Now the expansion of financial and capital markets, the use of rule-based systems for quick decisions, with minimal risk and away from human error, design, development, or improvement of these systems can be a competitive advantage. In the present study, neural network Algorithms and genetic programming Algorithms have been used to identify effective features and the decision tree to improve id3 has been proposed as a method for predicting price and trend of stock price change to select the optimal basket. The research results show that in addition to reducing computational and memory overhead, the proposed method is able to accurately predict severe fluctuations with nonlinear patterns and compared to modern methods such as nearest neighbor search, linear regression, autoregressive integrated moving average, and time series prophet algorithm will do better.

Supplier selection, order allocation and production planning are important and challenging decisions in supply chain management. There are many studies on mentioned topics separately. In this paper, a multi-objective mathematical model is proposed to optimize a sustainable supplier selection problem with order allocation and production planning simultaneously. This study considers a multi-supplier, multi-product, multi-item and multi-period supply chain. The designed mathematical model seeks to maximize total profit and minimize unsatisfied demand and total risk along with enforcing sustainability criteria in selecting suppliers. Supplier selection is a virtual process in every manufacturing company. On the other hand, this research considers all the important aspects of this problem. Therefore, the proposed framework can be implemented in many different companies like electronic, food, chemical industry. The proposed model is solved utilizing two Metaheuristic Algorithms including NSGA II and MOPSO. Moreover, Algorithms are tuned utilizing Taguchi analysis. Furthermore, ten sample problems are generated and results are compared to identify the best algorithm for the proposed model.

Concrete gravity dams secure their stability by the weight of the concrete used in their structure. Therefore, minimizing their weight (the volume of concrete consumed in their body) can reduce the costs significantly. This study aims to evaluate the performance of three Metaheuristic optimization Algorithms: harmony search, particle swarm optimization, and artificial bee colony, to find the optimal cross-section size of the gravity dam. In this way, the Koyna dam located in India is considered a case study. The programming is applied in Matlab software. Each algorithm under the constraints of this problem (the sliding, overturning, and vertical tension on the body of the dam) is run 6 times. Finally, the lowest value was chosen as the optimal result. The results revealed that however all the Algorithms have the optimal outputs than their real one but the optimum one is for the harmony search algorithm. To investigate the role of available uncertainties of dam cross-section, Monte Carlo simulation is engaged. The achieved results based on reliability show more safety of dam design.

Field experience shows that the cutting transportation and hole-cleaning phenomena are essential during the drilling phase. Particularly in directional drilling, when the accumulation of cutting has caused some drilling problems such as drill string sticking, formation failure, slow rate of penetration, drill bit abrasion, and the like. Through the study, a novel method for efficient hole cleaning, considering different parameters such as flow rate, the drill bit nozzles’ flow area, the consistency and flow behavior indices in the same time using PSO and ACO Algorithms were implemented. Moreover, Power Law has been considered for the fluid rheology model. Based on this, the research parameter shows that the PSO algorithm is much more accurate than the ACO algorithm, improving objective function by 50% and 4%, respectively. The performance of each algorithm was evaluated, and the results show that hole cleaning has been significantly improved. The flow rate and the bit nozzle size, which play key roles, were selected as optimization variables. Effective parameters on hole cleaning were evaluated, and the results before and after optimization showed a significant improvement in the model. The PSO and ACO Algorithms have been coded in MATLAB software, and the results are compared to the results of the ant colony. The amount of PV and YP has an inverse effect on the increment of minimum velocity required for cutting transport. Various model analyses reveal that the PSO algorithm is more accurate and robust than the Ant colony algorithm.

Metaheuristic Algorithms mostly consist of some parameters influencing their performance when faced with various optimization problems. Therefore, this paper applies Multi-Stage Parameter Adjustment (MSPA), which employs Extreme Latin Hypercube Sampling (XLHS), Primary Optimizer, and Artificial Neural Networks (ANNs) to a recently developed algorithm called the African Vulture Optimization Algorithm (AVOA) and a well-known one named Particle Swarm Optimization (PSO) for tuning their parameters. The performance of PSO is tested against two engineering and AVOA for two structural optimization problems, and their corresponding results are compared to those of their default versions. The results showed that the employment of MSPA improved the performance of both Metaheuristic Algorithms in all the considered optimization problems.

The main objective of this study is to optimize reinforced concrete (RC) frames in the framework of performance-based design using Metaheuristics. Three improved and efficient Metaheuristics are employed in this work, namely, improved multi-verse (IMV), improved black hole (IBH) and modified newton Metaheuristic algorithm (MNMA). These Metaheuristic Algorithms are applied for performance-based design optimization of 6- and 12-story planar RC frames. The seismic response of the structures is evaluated using pushover analysis during the optimization process. The obtained results show that the IBH outperforms the other Algorithms.