Convergence Rate Of Genetic Algorithm Research Articles

AI-based energy-efficient path planning of multiple logistics UAVs in intelligent transportation systems

In the context of Industry 5.0, artificial intelligence (AI)-based logistics Unmanned Aerial Vehicles (UAVs) have been widely applied in intelligent transportation systems due to their advantages of faster speed, lower cost, more environment-friendly, and less manpower needed. Whereas, most of the existing logistics UAV delivery models have not taken the energy consumption of the logistics UAVs and mixed time windows of the customers, which leads to their models cannot be applied in practical transportation systems. Therefore, we propose to minimize the total energy cost of multiple logistics UAVs during the customized products delivery period for a smart transportation system. Taking the energy consumption variation of the logistics UAVs, mixed time windows of the customers, as well as simultaneous delivery and pick up into consideration, we formulate a cooperative path planning problem via jointly optimizing the route of the logistics UAVs and the service allocation. To solve this large-scale integer programming problem, we employ the Large Neighborhood Search Algorithm (LNS) to accelerate the convergence rate of Genetic Algorithm (GA), and then develop an improved GA based cooperative path planning algorithm (IGCPA). The optimization procedure of the proposed algorithm IGCPA is divided into two phases, using the GA crossover operator and variational operator in the global search phase and LNS operator in the local search phase, and validating the integer programming model and the effectiveness of the solution algorithm based on different scale cases. Finally, abundant simulation results show that the energy cost of IGCPA is reduced by 17.35%, 15.18% and 9.99% compared with GA, LNS and Particle Swarm Optimization (PSO), respectively. Furthermore, the IGCPA is validated using Solomn standard data, which further verifies that the IGCPA can enhance the convergence rate of GA as well as obtain a lower delivery cost. Sensitivity analysis of the maximum UAV load and battery capacity reveals that the distribution cost tends to decrease and then increase as the increase of maximum load and battery capacity.

PWR core loading pattern optimization with adaptive genetic algorithm

In this paper, a new adaptive genetic algorithm (AGA) is proposed for the loading pattern optimization (LPO) of the pressurized water reactor (PWR) core. The tournament selection, the two-point crossover and the mutation based on randomly swapping positions between two fuel assemblies (FAs) are applied. New calculation formulae are introduced to adjust effectively the crossover and mutation probabilities according to the fitness value of the individual. The proposed algorithm is implemented in the LPO for the first core of the 1000 MWe PWR. The objective function is to minimize the maximum radial power peaking factor (RPPF) at the equilibrium of Xe under the constraint condition for the cycle length. The maximum RPPF of the obtained LP is decreased than that of the loading pattern (LP) by the designer. The results show that the proposed AGA is effective to improve the convergence rate of genetic algorithms (GAs) in the LPO.

A discriminant binarization transform using genetic algorithm and error-correcting output code for face template protection

In a biometric cryptosystem, biometric templates are encrypted before being stored in the database. This approach often requires a binarization phase to transform the original real-valued templates into their binary versions. Most of the existing binarization schemes do not take into consideration the need to discriminate binary templates; which in turn degrade the system recognition accuracy. This paper proposes a new binarization transform for face template protection using Error-Correcting Output Code (ECOC) and genetic algorithm (GA) which offsets this drawback. The proposed binarization transform novelty lies in the fact that the specific crossover, mutation and extension operators are defined by considering the theoretical properties of the ECOC framework in order to find an optimized coding matrix. The specification of these operations reduce the search space and speed up the convergence rate of GA. In addition, the proposed method is immune from being trapped in local minima. The quality of our method is evaluated using three well-known face databases: CMU PIE, Extended Yale B and FEI. The results demonstrate that the proposed binarization transform can result in a higher recognition accuracy without sacrificing the security of the protocol compared to alternative methods, such as binary discriminant analysis (BDA), discriminability-preserving (DP), chaos biohashing, and Discriminant-Genetic.

A Parameters Calibration Method in Simulated Complex Traffic Network

Traffic simulation models have been extensively used because of their ability to model the dynamic stochastic nature of transportation systems. Parameter calibration is very complex and does not give optimal results easily. Besides, it is also time-consuming especially for large and complex networks. Initially, the procedure of traffic micro-simulation parameter calibration was put forward. A Vehicle Intelligent Simulation Software Model (VISSIM) models were selected for parameter calibration in complex-network, and, the role of Simultaneous Perturbation Genetic Algorithm (SPGA) was examined in the optimization of component. Moreover an automatic calibration methodology for micro-simulation models was developed in order to select the best parameter set based on the observed Intelligent Transportation Systems (ITS) data which proved effective for different networks. Finally, the methodology was applied to calibrate the Beijing city VISSIM models, followed by the comparison of convergence rate of Genetic Algorithm (GA), Simultaneous Perturbation Stochastic Approximation (SPSA) and SPGA algorithm. The results show that the SPGA was effective and had good performance.

IJARCCE - Computer and Communication Engineering

Cancer any type of malignant growth or tumor, caused by abnormal and uncontrolled cell division: it may spread through the lymphatic system or blood stream to other parts of the body .Cancer classification is known to have the keys for addressing the problems based on cancer diagnosis and drug discovery. The proposed method of DNA microarray technique has made continuous processing thousands of gene expressions possible. Using gene expression data, the researchers have started the performance to explore the possibilities of cancer classification. The various methods have been introduced in recent years with promising results. But there are still a lot of problem which need to be addressed and understood. The performance of combining the genetic algorithm and Hybrid Fast PSO-BPN method is used to solve the optimization problems. Hybrid Fast PSO-BPN method is used to improve the accuracy and better convergence rate of genetic algorithm and this method is better for local search. This proposed method is used to overcome from the problem of computational difficulties occur by ill-condition of the square penalty function. The experimental result shows that this proposed method is better in accurate result with less execution time.

A hybrid method combining genetic algorithm and Hooke-Jeeves method for constrained global optimization

A new global optimization method combining geneticalgorithm and Hooke-Jeeves method to solve a class of constrainedoptimization problems is studied in this paper. We first introducethe quadratic penalty function method and the exact penalty functionmethod to transform the original constrained optimization problemwith general equality and inequality constraints into a sequenceof optimization problems only with box constraints. Then, thecombination of genetic algorithm and Hooke-Jeeves method isapplied to solve the transformed optimization problems. SinceHooke-Jeeves method is good at local search, our proposed methoddramatically improves the accuracy and convergence rate of geneticalgorithm. In view of the derivative-free of Hooke-Jeeves method,our method only requires information of objective function valuewhich not only can overcome the computational difficulties causedby the ill-condition of the square penalty function, but also canhandle the non-differentiability by the exact penalty function.Some well-known test problems are investigated. The numericalresults show that our proposed method is efficient and robust.

The Study on Convergence and Convergence Rate of Genetic Algorithm Based on an Absorbing Markov Chain

The study on convergence of GA is always one of the most important theoretical issues. This paper analyses the sufficient condition which guarantees the convergence of GA. Via analyzing the convergence rate of GA, the average computational complexity can be implied and the optimization efficiency of GA can be judged. This paper proposes the approach to calculating the first expected hitting time and analyzes the bounds of the first hitting time of concrete GA using the proposed approach.

The Applications of Improved Genetic Algorithm on Hypersonic Vehicle Trajectory Optimization

The Optimal trajectory design for hypersonic cruise missile is an optimal control problem with strict terminal constraints and variable final time. The classical algorithms always encounter the problems of high sensitivity to initial guess and local convergence in solving this problem. Aiming at these problems, genetic algorithm (GA) which is of good global convergence is applied to designing the optimal trajectory for hypersonic cruise missile. In order to improve the convergence rate of GA and overcome its premature problems, this text introduces a predatory search (PS) strategy to speed the convergence of genetic algorithms, robust and closer to the optimal solution. This text compares the original genetic algorithm (GA) and improved genetic algorithm by the emulate experiments, and the results show that the PSGA is a more effective method to design the Optimal trajectory for hypersonic cruise missile than the original genetic algorithm.

Based on GA Mixed with Trust Region Method Solving Nonlinear Least Square Problems

Nonlinear least square is one of the unconstrained optimization problems. In order to solve the least square trust region sub-problem, a genetic algorithm (GA) of global convergence was applied, and the premature convergence of genetic algorithms was also overcome through optimizing the search range of GA with trust region method (TRM), and the convergence rate of genetic algorithm was increased by the randomness of the genetic search. Finally, an example of banana function was established to verify the GA, and the results show the practicability and precision of this algorithm.

Aerodynamic shape optimization using efficient evolutionary algorithms and unstructured CFD solver

An efficient evolutionary algorithm is presented for shape optimization of transonic airfoils. Several techniques have been used to improve the efficiency and convergence rate of the optimization Genetic Algorithm (GA). A new airfoil shape parameterization method is used which is capable of producing more efficient shapes at viscous flow conditions. A Real-Coded Population Dispersion (PD) Genetic Algorithm is developed in order to increase the robustness and convergence rate of the Genetic Algorithm. A Multi-Layer Perceptron Neural Network (NN) is utilized to reduce the huge computational cost of the objective function evaluation. Further improvement in the performance of NN is obtained by using dynamic retraining and normal distribution of the training data to determine well trained parts of the design space to NN. Using the above techniques, the total computational time of optimization algorithm is reduced up to 60% compared with the conventional GA.

Techniques for bounding the convergence rate of genetic algorithms

The main purpose of the present paper is the study of computational aspects, and primarily the convergence rate, of genetic algorithms (GAs). Despite the fact that such algorithms are widely used in practice, little is known so far about their theoretical properties, and in particular about their long-term behavior. This situation is perhaps not too surprising, given the inherent hardness of analyzing nonlinear dynamical systems, and the complexity of the problems to which GAs are usually applied. In the present paper we concentrate on a number of very simple and natural systems of this sort, and show that at least for these systems the analysis can be properly carried out. Various properties and tight quantitative bounds on the long-term behavior of such systems are established. It is our hope that the techniques developed for analyzing these simple systems prove to be applicable to a wider range of genetic algorithms, and contribute to the development of the mathematical foundations of this promising optimization method. ©1999 John Wiley & Sons, Inc. Random Struct. Alg., 14, 111–138, 1999