genetic algorithm (ga)

Vehicular ad-hoc networks (VANET) represent an improved iteration of Wireless Sensor Networks (WSN) with mobile sensory nodes situated within vehicles. The Vehicular Adhoc Networks hold a crucial position within smart city applications as inter-vehicle communication is deemed indispensable for maintaining the technological efficiency of the city. Despite the benefits provided by VANET, it encounters numerous challenges and drawbacks within the context of smart city applications. One such challenge pertains to the security and privacy principles of VANET. Privacy and security emerge as principal concerns associated with VANET, prompting multiple researchers to propose security solutions over the past decade. The present research endeavor focuses on elevating the quality of service (QoS) by offering an enhanced level of security for data communication. This security enhancement is achieved through the use of blockchain technology and the integration of Elliptical Curve Cryptography with secure hash functions to safeguard data communication from node to Mobile Control Unit (MCU). Furthermore, the proposed research work presents an efficient routing mechanism for data between mobile nodes and the Control Unit by employing neuro fuzzy logic to identify the optimal path from the source node to the Mobile Control Unit (MCU). The proposed work is compared with existing cryptographic methodologies as well as state-of-the-art routing path optimization algorithms, namely Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Memetic Algorithm (MA), and Honey Bee Optimization (HBO), in order to establish its superiority in terms of computational time, throughput, packet delivery ratio, and accuracy.

The adaptive fuzzy neural inference system (ANFIS) is an efficient estimation model not only among fuzzy neural systems but also among other types of machine learning techniques. Despite its acceptance among researchers, ANFIS cited limitations such as inefficiencies in large data and data problems, cost of computation, processing time and optimization, and error training. The ANFIS structural design is a complex optimization problem that can be improved using meta-heuristic algorithms. In this study, to optimize and reduce errors, a new meta-heuristic algorithm inspired by nomadic migration was designed and used to design an adaptive fuzzy neural system called the Qashqai nomadic meta-heuristic algorithm. The results of the hypothesis test showed that the Qashqai optimization algorithm is not defeated by the genetic algorithm and particle swarm and works well in terms of convergence to the optimal answer. In this hybrid algorithm, random data set are first generated and then trained by designing a basic fuzzy neural system. Subsequently, the parameters of the basic fuzzy system were adjusted according to the modeling error using the meta-heuristic optimization algorithm of Qashqai nomads. The fuzzy nervous system with the best values was obtained as the final result.The main achievements of the study are:• Improving ANFIS accuracy using a novel meta-heuristic algorithm.• Fix and remove some problems and Limitations in the Anfis model, such as inefficiencies in large data, cost of computation, Answer accuracy, and reduce errors.• Comparing the proposed ANFIS+QA with some recent related work such as ANFIS+QA and ANFIS+Pso.

The estimation of statistical parameters for multivariate data can lead to wasted information if the missing values are neglected, which in return will lead to inaccurate estimates, therefore the incomplete data must be estimated using one of the statistical estimation methods to obtain accurate results and thus obtaining good estimates for the parameters.
Missing values is considered one of the most important problems that researchers encounter and the most common, and in the case of the multivariate skew normal distribution (MSN) the presence of this problem will lead to weak and misleading conclusions for the research, which calls for treating this problem and in return obtaining efficient and convincing results. The aim of this paper is to estimate the missing values for the multivariate skew normal distribution function using the K-nearest neighbors Imputation (KNN). After estimating the missing values, the parameters are estimated using Genetic Algorithm (GA), and the Bayesian Approach was also used to estimate the missing values and find the estimates for the parameters. Using simulation, the Mean Squared Error (MSE) was calculated to find out which method is the best for estimation by comparing the two methods using different sample sizes (400, 600, and 800). The (GA) that is based on the (KNN) algorithm to estimate the missing values proved to be better and more efficient than the Bayesian Approach in terms of the results.

Our research includes studying the case 1//F(∑Ui,∑Ti,Tmax) minimized the cost of a three-criteria objective function on a single machine for scheduling n jobs. and divided this into several partial problems and found simple algorithms to find the solutions to these partial problems and compare them with the optimal solutions. This research focused on one of these partial problems to find minimize a function of sum cost of (∑Ui) sum number of late job and (∑Ti) sum Tardiness and (Tmax) the Maximum Tardiness for n job on the single machine, which is NP-hard problem, first found optimal solutions for it by two methods of Complete Enumeration technique(CEM) and Branch and Bounded ((BAB)). Then use some Local search methods(Descent technique(DM), Simulated Annealing (SA) and Genetic Algorithm (GA)), Develop algorithm called ((A)) to find a solution close to the optimal solution. Finally, compare these methods with each other.

Nowadays, effort estimation in software development is of great value and significance in project management. Accurate and appropriate cost estimation not only helps customers trust to invest but also has a significant role in logical decision making during project management. Different models of cost estimation are presented and employed to the date, but the models are application specific. In this paper, a three-phase hybrid approach is proposed to overcome the problem. In the first phase, features are selected using a combination of genetic algorithm and the perceptron neural network. In the second phase, impact factors are associated to each selected feature using multiple linear regression methods which act as coefficients of influence for each feature. In the last and the third phase, the feature weights are optimized by Imperialist Competitive Algorithm. To compare the proposed model for effort estimation with state-of-the-art models, three datasets are chosen as benchmark, namely COCOMO, Maxwell and Albrecht. The datasets are standard and publicly available for assessment. The experiments show promising results and average performance is improved by the proposed model for MMRE performance criterion on the datasets by 23%, 38% and 35%, respectively.