‎genetic algorithm

Investors need to grasp how liquidity affects both risk and return in order to optimize their portfolio performance. There are three classes of stocks that accommodate those criteria: Liquid, high-yield, and less-risky. Classifying stocks help investors build portfolios that align with their risk profiles and investment goals, in which the model was constructed using the one-versus-one support vector machines method with a radial basis function kernel. This model was trained using a combination of the Kompas100 index and the Indonesian industrial sectors stocks data. Single optimal portfolios were created using the real coded genetic algorithm based on different sets of objectives: Maximizing short-term and long-term returns, maximizing liquidity, and minimizing risk. In conclusion, portfolios with a balance on all these four investment objectives yielded better results compared to those focused on partial objectives. Furthermore, our proposed method for selecting portfolios of top-performing stocks across all criteria outperformed the approach of choosing top stocks based on a single criterion.

‎Consider a graph $G=(V(G),E(G))$‎, ‎where a perfect matching in $G$ is defined as a subset of independent edges with $\frac{|V(G)|}{2}$ elements‎. ‎A global forcing set is a subset $S$ of $E$ such that no two disjoint perfect matchings of $G$ coincide on it‎. ‎The minimum cardinality of global forcing sets of $G$ is called the global forcing number (GFN for short)‎. ‎This paper addresses the NP-hard problem of determining the global forcing number for perfect matchings‎. ‎The focus is on a Genetic Algorithm (GA) that utilizes binary encoding and standard genetic operators to solve this problem‎. ‎The proposed algorithm is implemented on some chemical graphs to illustrate the validity of the algorithm‎. ‎The solutions obtained by the GA are compared with the results from other methods that have been presented in the literature‎. ‎The presented algorithm can be applied to various bipartite graphs‎, ‎particularly hexagonal systems‎. ‎Additionally‎, ‎the results of the GA improve some results that‎ have already been presented for finding GFN‎.

Unmanned aerial vehicle (UAV) safety inspection is a developing technology that offers the benefits of high efficiency, low cost, and freedom from dangerous areas and unique situations. An urgent fundamental issue in the deployment of UAV in factories is how to successfully strike a balance between the effectiveness and cost of UAV safety inspection. In view of this, we build a route planning model for UAV inspection. And then, by using the path planning of UAV safety inspection as the research object, based on the two important evaluation indicators of cost and efficiency, we exploit fuzzy time window and adaptive genetic algorithms to design the solution algorithm. Finally, a case verifies the applicability and logic of the proposed model. The results show that the proposed path optimization model with fuzzy time window can reasonably pass all inspection points under balanced conditions, and the hybrid genetic algorithm has good optimization ability.

In this paper, we present an improved imperialist competitive algorithm for solving an inverse form of the Huxley equation, which is a nonlinear partial differential equation. To show the effectiveness of our proposed algorithm, we conduct a comparative analysis with the original imperialist competitive algorithm and a genetic algorithm. The improvement suggested in this study makes the original imperialist competitive algorithm a more powerful method for function approximation. The numerical results show that the improved imperialist competitive algorithm is an efficient algorithm for determining the unknown boundary conditions of the Huxley equation and solving the inverse form of nonlinear partial differential equations.

This paper is aimed at designing a distribution network of small industries in Arak City. The model presented in this paper will provide optimal rates of order quantity given by a supplier to the producer. NMFC model covers drops in prices and finance costs. Furthermore, the flexible time horizon planning permits the producer to use this model in different time lags like hour, day, and month. The genetic algorithm function has been used in Matlab for achieving the solution space and comparing the output results of the model with two EOQ and JIT models to calculate optimal order quantity. The sensitivity of all parameters has been taken into account to examine its effect on the model which indicates the higher effect of holding and warehousing costs on the total costs.

Chemistry is undoubtedly the most practical science in our life, if we pay attention to the nature around us from the moment we wake up until the day ends, we will find that at the moment of the day and night, we are surrounded by different chemicals. And we have work. In fact, many of our daily activities are related to chemical processes. This has caused the expansion of chemistry and has caused the emergence of various problems. This study aims to analyze a classical chemistry problem known as the chemical reaction equilibrium, which has no uniform solution in different scenarios. In other words, different types of chemical reactions may require diverse methods for establishing equilibrium. This paper proposes a simple system for each chemical reaction through the law of conservation of mass (LCM) and basic mathematical concepts. Optimization algorithms (e.g., the genetic algorithm) are then employed to find a solution to this system.

Bitcoin and digital currencies have emerged as a new market for investment. Therefore, the prediction of their future trend and prices is highly significant. In this research, the factors influencing the price of bitcoin were identified and extracted based on previous researches. The identified factors include the US dollar index, CPI index, S and P 500, Dow Jones, and gold price. Considering the performance of metaheuristic algorithms in predicting bitcoin price, this research utilized genetic algorithm and particle swarm optimization algorithm, and proposed a hybrid algorithm to improve their performance.According to our results, among the investigated factors, the US dollar index has the greatest impact on bitcoin price, followed by inflation rate and the CPI index. Additionally, the proposed hybrid algorithm outperforms the particle swarm optimization and genetic algorithms, with a prediction error of 7.3%. It should be noted that the type and magnitude of the impact of the investigated factors may change over time. For example, a factor that previously had a direct impact may become reversed or neutralized over time.

The current research aims to identify new combined genetic algorithm methods to solve complex problems. The researcher has analyzed the results and findings of the previous researchers using a systematic reviewing approach and has identified the effective factors by implementing the 7 steps of Sandelowski and Barroso’s method. Among 4320 articles, 54 articles were selected based on the CASP method. In this manner, in order to evaluate reliability and quality control, the Kappa index was used, and its value was deemed to be in high compatibility regarding the identified factors. The results of the analysis of the collected data in ATLAS TI software led to the identification of 9 categories and 33 primary codes of new combined genetic algorithm methods to solve complex problems. Based on the coding, 9 categories, and 33 initial codes were identified. The identified categories are layout design, supply network, programming, Anticipation, inventory control, information security, imaging, medical imaging and wireless network.

This paper deals with the single machine scheduling problem with sequence-dependent setup time and learning effect on processing time, where the objective is to minimize total earliness and tardiness of the jobs. A Mixed Integer Linear Programming (MILP) model capable of solving small-sized problems is proposed to formulate this problem. In view of the NP-hard nature of the problem, the Hybrid Particle Swarm Optimization (HPSO) algorithm is proposed to solve the large-sized problems. In order to utilize Particle Swarm Optimization (PSO) to solve the scheduling problems, the proposed HPSO approach uses a random key representation to encode solutions, which can convert the job sequences to continuous position values. Also, the local search procedure is included within the HPSO to enhance the exploitation of the algorithm. The performance of the proposed HPSO is verified for small and medium-sized problems by comparing its results with the best solution obtained by the LINGO. In order to test the applicability of the proposed algorithm to solve large-sized problems, 120 instances are generated, and the results are compared with a Random Key Genetic Algorithm (RKGA). The results show the effectiveness of the proposed model and algorithm.

A new image encryption scheme using the advanced encryption standard (AES), a chaotic map, a genetic operator, and a fuzzy inference system is proposed in this paper. In this work, plain images were used as input, and the required security level was achieved. Security criteria were computed after running a proposed encryption process. Then an adaptive fuzzy system decided whether to repeat the encryption process, terminate it, or run the next stage based on the achieved results and user demand. The SHA-512 hash function was employed to increase key sensitivity. Security analysis was conducted to evaluate the security of the proposed scheme, which showed it had high security and all the criteria necessary for a good and efficient encryption algorithm were met. Simulation results and the comparison of similar works showed the proposed encryptor had a pseudo-noise output and was strongly dependent upon the changing key and plain image.

The optimization of reliability is crucial across various engineering domains. The redundancy allocation problem (RAP) is among the key challenges within reliability. This study introduces an RAP incorporating repairable components and a k-out-of-n sub-systems structure. The objective function aims to maximize system reliability while adhering to cost and weight constraints. The goal is to determine the optimal number of components for each subsystem, including the appropriate allocation of repairmen to each subsystem. Given that this model is classified as an Np-Hard problem, we employed a genetic algorithm (GA) to solve the proposed model. Additionally, response surface methodology (RSM) was utilized to fine-tune the algorithm parameters. To calculate the reliability of each subsystem, as well as the overall system reliability, a Monte Carlo simulation was employed. Lastly, a numerical example was solved to assess the algorithm's performance.

This study proposes an automatic genetic algorithm in fuzzy cluster analysis for numerical data. In this algorithm, a new measure called the FB index is used as the objective function of the genetic algorithm. In addition, the algorithm not only determines the appropriate number of groups but also improves the steps of traditional genetic algorithm as crossover, mutation and selection operators. The proposed algorithm is shown the step by step throughout the numerical example, and can perform fast by the established Matlab procedure. The result from experiments show the superiority of the proposed algorithm when it overcomes the existing algorithms. Moreover, it has been applied in recognizing the image data, and building the fuzzy time series model. These show the potential of this study for many real applications of the different fields.

K-Nearest Neighbors (KNN) is a classification algorithm based on supervised machine learning, which works according to a voting system. The performance of the KNN algorithm depends on different factors, such as unbalanced distribution of classes, the scalability problem, and considering equal values for all training samples. Regarding the importance of the KNN algorithm, different improved versions of this algorithm are introduced, such as fuzzy KNN, weighted KNN, and KNN with variable neighbors. In this paper, a weighted KNN based on Whale Optimization Algorithm is proposed for the objective of increasing the level of detection accuracy. The proposed algorithm devotes a weight to each training sample of every feature by employing the WOA to explore the optimized weight matrix. The algorithm is implemented and experimented on five standard datasets. The evaluation results prove that the proposed algorithm performs better than both weighted KNN based on the Genetic Algorithm (GA) and the classic KNN algorithm.

Sorting agricultural products refers to grading food and other crops based on size, color, appearance, and other factors such as separating impurities, fruits, and damaged and rotten products. Today, sorting technology and related equipment for grading agricultural crops are progressing in developed countries, which can be found in most large agricultural units. Therefore, initial packaging and transportation of the product are facilitated, and more added value can be provided for farmers. This study aimed to optimize the raisin sorting machine based on a genetic algorithm to increase the quality of raisin grading. Therefore, a seedless white variety of grape samples were randomly selected and prepared from an orchard in Makan, East Azerbaijan, Iran. Digital image processing techniques such as the image processing toolbox in MATLAB were used to extract features from an image for sorting. Other meta-heuristic algorithms such as PSO, differential evolution, and artificial bee colony algorithm were used to evaluate the accuracy of the results. According to the results, the artificial bee colony algorithm had better accuracy than other algorithms, but the convergence speed was lower, and the computational volume was higher. However, the genetic and PSO algorithms had an accuracy almost equal to the artificial bee colony algorithm despite having a higher speed of convergence and lower computational operations, which can be used as the best algorithm in this application. Differential evolutionary algorithms and harmony search require processing in many iterations, and the computation time is not economical. Therefore, the clustering of raisins in industrial units requires high clustering speed and minimum error to avoid discarding or outliers, and genetics and PSO algorithms were acceptable.

The present study aims to investigate the optimal fractional order PID controller performance in the chaotic system of HIV disease fractional order using the Particle Swarm optimization and Genetic algorithm method. Differential equations were used to represent the chaotic behavior associated with HIV. The optimal fractional order of the PID controller was constructed, and its performance in the chaotic system with HIV fractional order was tested. Optimization methods were used to get PID control coefficients from particle swarm and genetic algorithms. Findings revealed that the equations for the HIV disease model are such that the system’s behavior is greatly influenced by the number of viruses produced by infected cells, such that if the number of viruses generated by infected cells exceeds 202, the disease’s behavior is such that the virus and disease spread. For varying concentrations of viruses, the controller created for this disease does not transmit the disease.

Due to developments in printing technology, the number of counterfeit banknotes is increasing every year. Finding an effective method to detect counterfeit banknotes is an important task in business. Finding a reliable method to detect counterfeit banknotes is a crucial challenge in the world of economic transactions. Due to technological development, counterfeit banknotes may pass through the counterfeit banknote detection system based on physical and chemical properties undetected. In this study, an intelligent counterfeit banknote detection system based on a Genetic Fuzzy System (GFS) is proposed to detect counterfeit banknotes efficiently. GFS is a hybrid system that uses a network architecture to fine-tune the membership functions of a fuzzy inference system. The learning algorithms Fuzzy Classification, Genetic Fuzzy Classification, ANFIS Classification, and Genetic ANFIS Classification were applied to the dataset in the UCI machine learning repository to detect the authenticity of banknotes. The developed model was evaluated based on Accuracy (ACC), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Error Mean, Error STD, and confusion matrix. The experimental results and statistical analysis showed that the classification performance of the proposed model was evaluated as follows: Fuzzy = 97.64%, GA_Fuzzy = 98.60%, ANFIS = 80.83%, GA_ANFIS = 97.72% accuracy (ACC). This shows the significant potential of the proposed GFS models for fraud detection.

In this paper, we use radial basis function collocation method for solving the system of differential equations in the area of biology. One of the challenges in RBF method is picking out an optimal value for shape parameter in Radial basis function to achieve the best result of the method because there are not any available analytical approaches for obtaining optimal shape parameter. For this reason, we design a genetic algorithm to detect a close optimal shape parameter. The population convergence figures, the residuals of the equations and the examination of the ASN2R and ARE measures all show the accurate selection of the shape parameter by the proposed genetic algorithm. Then, the experimental results show that this strategy is efficient in the systems of differential models in biology such as HIV and Influenza. Furthermore, we show that using our pseudo-combination formula for crossover in genetic strategy leads to convergence in the nearly best selection of shape parameter.

In this paper, we present a redundancy allocation problem (RAP) with series-parallel sub-systems and repairable components. The repairmen will go on multiple vacations. In repairable systems, a fundamental aspect to be considered is to predict the reliability of the systems under study. Set a reliability model for repairable systems, however, is still a challenging problem when considering the dependency This paper aims to evaluate the number of components and repairmen in each sub-system. Because this RAP belongs to Np. Hard problems, also, a Genetic algorithm to solve the presented model.

Resources scarcity, available capabilities and cost-benefit point of view, make it essential to select the best project(s) from available project portfolio. Project selection process has a significant role in the success. Here the main problem is what projects must be selected and how manage simultaneous projects. Used approach to answer these questions must be real, fast, global, flexible, economic and easy to use. It is clear that choosing a good approach for project selection problem with economic and non-economic criteria can be vital for a project manager to success within constraints. The complexity of this problem increases as the number of projects and the number of objectives increase. Therefore, in this research we aim to present a heuristic based on genetic and simulates annealing to select and prioritize available projects based on economic and non-economic criteria. Considered issues are benefit, credit and risk (technical and financial). Presented method starts from multi population of generated solutions and moves toward the final solution. Comparison studies between our method with other recently method in the literature demonstrates the capability of it to find a good basket of projects. Experimental results demonstrate that this method can be used for all kinds of projects basket.