Improvement Of Genetic Algorithm Research Articles

A genetic algorithm improvement by tour constraint violation penalty discount for maritime cargo delivery

The problem of minimizing the cost of maritime cargo delivery is considered. The cost is equivalent to the sum of the tour lengths of feeders used for the delivery. The problem is formulated as a multiple traveling salesman problem. In order to find its solution as the shortest route of the tours of feeders, a genetic algorithm is used where we present two inequalities constraining the tour length of every feeder to lie between the shortest and longest lengths. Apart from the constant tour constraint violation penalty in the genetic algorithm, we suggest a changeable penalty as an exponential function of the algorithm iteration, where we maintain the possibility of the penalty rate to be either increasing or decreasing, whose steepness is controlled by a positive parameter. Our tests show that the changeable penalty algorithm may return shorter routes, although the constant penalty algorithms cannot be neglected. As the longest possible tour of the feeder is shortened, the changeable penalty becomes more useful owing to a penalty discount required either at the beginning or at the end of the algorithm run to improve the selectivity of the best feeder tours. In optimizing maritime cargo delivery, we propose to run the genetic algorithm by the low and constant penalties along with the increasing and decreasing penalties. The solution is the minimal value of the four route lengths. In addition, we recommend that four algorithm versions be initialized by four different pseudorandom number generator states. The expected gain is a few percent, by which the route length is shortened, but it substantially reduces expenses for maritime cargo delivery.

Intelligent and Improved Self-Adaptive Anomaly based Intrusion Detection System for Networks

With the advent of digital technology, computer networks have developed rapidly at an unprecedented pace contributing tremendously to social and economic development. They have become the backbone for all critical sectors and all the top Multi-National companies. Unfortunately, security threats for computer networks have increased dramatically over the last decade being much brazen and bolder. Intrusions or attacks on computers and networks are activities or attempts to jeopardize main system security objectives, which called as confidentiality, integrity and availability. They lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an organization. There is a great need for an effective Network Intrusion Detection System (NIDS), which are security tools designed to interpret the intrusion attempts in incoming network traffic, thereby achieving a solid line of protection against inside and outside intruders. In this work, we propose to optimize a very popular soft computing tool prevalently used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel machine learning framework called “ISAGASAA”, based on Improved Self-Adaptive Genetic Algorithm (ISAGA) and Simulated Annealing Algorithm (SAA). ISAGA is our variant of standard Genetic Algorithm (GA), which is developed based on GA improved through an Adaptive Mutation Algorithm (AMA) and optimization strategies. The optimization strategies carried out are Parallel Processing (PP) and Fitness Value Hashing (FVH) that reduce execution time, convergence time and save processing power. While, SAA was incorporated to ISAGA in order to optimize its heuristic search. Experimental results based on Kyoto University benchmark dataset version 2015 demonstrate that our optimized NIDS based BPNN called “ANID BPNN-ISAGASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. Moreover, improvement of GA through FVH and PP saves processing power and execution time. Thus, our model is very much convenient for network anomaly detection.

Improvement of genetic algorithm using artificial bee colony

Genetic algorithm (GA) is a part of evolutionary computing that simulates the theory of evolution and natural selection, where this technique depends on a heuristic random search. This algorithm reflects the operation of natural selection, where the fittest individuals are chosen for reproduction so that they produce offspring of the next generation. This paper proposes a method to improve GA using artificial bee colony (GABC). This proposed algorithm was applied to random number generation (RNG), and travelling salesman problem (TSP). The proposed method used to generate initial populations for GA rather than the random generation that used in traditional GA. The results of testing on RNG show that the proposed GABC was better than traditional GA in the mean iteration and the execution time. The results of testing TSP show the superiority of GABC on the traditional GA. The superiority of the GABC is clear in terms of the percentage of error rate, the average length route, and obtaining the shortest route. The programming language Python3 was used in programming the proposed methods.

RESEARCH ON HIGH RESOLUTION REMOTE SENSING IMAGE CLASSIFICATION BASED ON SEGNET SEMANTIC MODEL IMPROVED BY GENETIC ALGORITHM

Abstract. SegNet model is an improved model of Full Convolutional Networks (FCN). Its encoder, i.e. image feature extraction, is still a convolutional neural network (CNN). Aiming at the problem that most traditional CNN training uses error back propagation algorithm (BP algorithm), which has slow convergence speed and is easy to fall into local optimum solution, this paper takes SegNet as the research object, and proposes a method of extracting partial weights by using genetic algorithm (GA) to select features of SegNet model, and to alleviate the problem that SegNet is easy to fall into local optimal solution. In the training process of SegNet model, the weight of convolution layer of SegNet model used to extract features is optimized through selection, crossover and mutation of genetic algorithm, and then the improved SegNet semantic model (GA-SegNet model) is obtained by GA. In order to verify the image classification effect of the proposed GA-SegNet model, the same high-resolution remote sensing image data are used for experiments, and the model is compared with maximum likelihood (ML), support vector machine (SVM), traditional CNN and SegNet semantic model without GA improvement. The experimental results show that the proposed GA-SegNet model has the best classification accuracy and effect, which GA overcomes the problem of premature convergence of BP random gradient descent to a certain extent, and improves the classification performance of SegNet semantic model.

Improved Genetic Algorithm Tuning Controller Design for Autonomous Hovercraft

By mimicking the biological evolution process, genetic algorithm (GA) methodology has the advantages of creating and updating new elite parameters for optimization processes, especially in controller design technique. In this paper, a GA improvement that can speed up convergence and save operation time by neglecting chromosome decoding step is proposed to find the optimized fuzzy-proportional-integral-derivative (fuzzy-PID) control parameters. Due to minimizing tracking error of the controller design criterion, the fitness function integral of square error (ISE) was employed to utilize the advantages of the modified GA. The proposed method was then applied to a novel autonomous hovercraft motion model to display the superiority to the standard GA.

Energy-saving algorithm for data centre network based on genetic algorithm

This thesis focuses on network equipments in the data centre which has caused rapidly growth of energy consumption recent years. The switches account for the largest proportion of energy consumption of network equipments, so turning off unneeded switches can reduce energy consumption effectively. Based on this point, we develop a high-efficient routing algorithm based on genetic algorithm (GA) in order to improve energy consumption of network equipments. Genetic algorithm is a kind of a heuristic algorithm which solves the optimisation problem rapidly by imitating the way of the natural selection, but to a certain degree, it reduces accuracy. It is a complicated problem to decide routing path in a short period, so we choose genetic algorithm to achieve our goals. In our simulation, we make some improvements of GA in order to fit our problem and raise the accuracy of its solution.

A new non-geometric transmission parameter optimization design method for HMCVT based on improved GA and maximum transmission efficiency

The HMCVT (Hydro-Mechanical Continuous Variable Transmission) can realize continuously variable speed in a large range and transmit a large load torque, and thus it is very applicable to engineering vehicles, such as the tractor. This paper proposes a new transmission parameter optimization design method for HMCVT in order to enhance the practicability of the HMCVT, improve its transmission efficiency, realize the fast determination of HMCVT transmission parameters, and allow HMCVT to meet the work requirements of engineering vehicles like the tractor. Specifically, this paper first conducts a characteristic analysis of a certain model of HMCVT. Then, according to the operation requirements of the tractor and the driving characteristic continuity of the HMCVT, the improved design of the transmission scheme, the maximization of working efficiency, and the most proper positions of junction points of variable speed stages, this paper puts forward an HMCVT transmission parameter optimization design model. This paper uses the GA (genetic algorithm) to implement the transmission parameter optimization, and improves the GA to obtain a higher algorithm running speed and accuracy. The improvement in GA primarily includes avoiding the super individual in the initial population, the adaptive changes of the population size in the iteration process, and the adaptive changes of crossover probability and mutation probability. The research results demonstrate that the improved GA has a faster rate of convergence and better optimization accuracy. The new proposed optimization design method can design HMCVT transmission parameters flexibly, efficiently, and quickly, as expected. The HMCVT with optimized transmission parameters exhibits driving characteristics that meet the working requirements of the tractor, significantly improves working efficiency, and offers theoretical guidance and a basis of design for the determination of the proper positions of junction points of variable speed stages.

Intelligent and Improved Self-Adaptive Anomaly based Intrusion Detection System for Networks

With the advent of digital technology, computer networks have developed rapidly at an unprecedented pace contributing tremendously to social and economic development. They have become the backbone for all critical sectors and all the top Multi-National companies. Unfortunately, security threats for computer networks have increased dramatically over the last decade being much brazen and bolder. Intrusions or attacks on computers and networks are activities or attempts to jeopardize main system security objectives, which called as confidentiality, integrity and availability. They lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an organization. There is a great need for an effective Network Intrusion Detection System (NIDS), which are security tools designed to interpret the intrusion attempts in incoming network traffic, thereby achieving a solid line of protection against inside and outside intruders. In this work, we propose to optimize a very popular soft computing tool prevalently used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel machine learning framework called “ISAGASAA”, based on Improved Self-Adaptive Genetic Algorithm (ISAGA) and Simulated Annealing Algorithm (SAA). ISAGA is our variant of standard Genetic Algorithm (GA), which is developed based on GA improved through an Adaptive Mutation Algorithm (AMA) and optimization strategies. The optimization strategies carried out are Parallel Processing (PP) and Fitness Value Hashing (FVH) that reduce execution time, convergence time and save processing power. While, SAA was incorporated to ISAGA in order to optimize its heuristic search. Experimental results based on Kyoto University benchmark dataset version 2015 demonstrate that our optimized NIDS based BPNN called “ANID BPNN-ISAGASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. Moreover, improvement of GA through FVH and PP saves processing power and execution time. Thus, our model is very much convenient for network anomaly detection.

Application of Distributed Power in Reactive Power Optimization of Distribution Network

With the rapid development of new energy technologies, distributed power generation technology has attracted widely attention. The advantages of them are small investment, clean and environmental protection, reliable and flexible power supply. They can output (or absorb) continuously adjustable reactive power and participate in the reactive power optimization, which can keep the balance of reactive power and optimize the distribution of reactive power flow. In this paper, firstly, the power flow model of typical distributed power supplies: wind power, photovoltaic power and gas turbine power generation are researched. Then, the distributed power supply as a continuously adjustable reactive power device, combined with the traditional equipment, participate in reactive power optimization. An objective function considering system loss is proposed to find optimal solution. Finally, the IEEE33 node system is used to verification. Results show that the distributed power supply can effectively reduce the system loss and improve the voltage stability. Meanwhile, the effectiveness and feasibility of the improved algorithm are verified. The optimization of this paper is the improvement of genetic algorithm. It can make up for the lack of local optimization ability of genetic algorithm. Compared with the traditional genetic algorithm, the global optimization ability is greatly enhanced.

A hierarchy in mutation of genetic algorithm and its application to multi-objective analog/RF circuit optimization

This paper presents a multi-objective analog circuit design optimization tool using genetic algorithm based on hierarchical mutation scheme. The idea is to improve the convergence and diversity of genetic algorithm by incorporating hierarchy during polynomial mutation operation. In this regard, a theoretical framework of proposed genetic algorithm is presented using Markov chain principle. To investigate the effectiveness of hierarchy in polynomial mutation operator, the scheme is compared with six different mutation strategies. Experiments are performed for different function evaluations to evaluate the performance of hierarchical polynomial mutation operator. Further, to showcase the improvement in genetic algorithm, numerous experiments are performed on twelve different test functions and two design examples. The proposed genetic algorithm shows competitive performance over other standard optimization techniques in terms of both convergence and diversity of solutions.

The application research on improvement of genetic algorithm in linear CCD detection

When the linear array image sensor (CCD) is used for spot detection, the optimization of the detection signal is usually one of the problems that plague the user. In linear array imaging sensor (CCD) detection applications, optimization of the detection signal is usually one of the problems with the user. Based on the characteristics of linear array CCD detection signal, a genetic algorithm (GA) is established to solve the problem of mathematical model. In this paper, a new adaptive genetic algorithm (IGA) with directional adaptive guidance and adaptive control technology and threshold constraint technology are proposed for the lack of local optimization ability of the standard genetic algorithm (SGA), premature convergence and low accuracy. By applying IGA to the actual detection data, it is proved that IGA has certain advantages in solving the problem of linear CCD detection signal optimization. In the end of this paper, the performance of IGA, SGA and peak finding algorithm (SP) are analyzed and compared, which fully demonstrates the advantages of IGA in solving such problems.

Inverse Kinematic Solutions of Dual Redundant Camera Robot Based on Genetic Algorithm

Inverse kinematic solutions for a dual redundant camera robot in position are examined in order to alleviate operation difficulty and reduce time. The inverse kinematic algorithm is based on a basic genetic algorithm, and the genetic algorithm which is used to solve the problem of a redundant robot is mainly optimized in the joint space. On this basis, the genetic algorithm improvement strategies are studied. In this paper, a genetic algorithm with constrained 2 redundant degrees of freedom (DOF) is proposed through setting 2 parameter variables, with more flexible structure of optimization objective function and more efficient algorithm than basic genetic algorithm. Finally, the result of inverse kinematic algorithm is achieved in terms of the physical prototype.

Accuracy improvement of genetic algorithm for obtaining floating-point solution

The aim of our study is implementation of genetic algorithm (GA) in FPGA hardware. We use GA for obtaining floating-point solutions accurately. For this purpose, we propose applying a gray-coded floating-point format to GA to improve accuracy of the solutions. In this paper, we show the result of simulations using a gray-coded floating-point format. We evaluate performance of the proposed GA by obtaining solutions of five-dimensional Sphere function and two-dimensional Rosenbrock function. In these experimentations, we focused on mutation probability which is one of the parameters of GA for improving its accuracy. In the results, there was a trade-off between convergence speed and speed of finding the optimal solution depending on the mutation probability. However, we showed that our theory can obtain the optimal solutions effectively compared with the normal floating-point format.

Study on the Improvement of Genetic Algorithm by Using Vehicle Routing Problem

mprove the existing genetic algorithm, make the vehicle path planning problem solving can be higher quality and faster solution. The mathematic model for study of VRP with genetic algorithms was established. An improved genetic algorithm was proposed, which consist of a new method of initial population and partheno genetic algorithm revolution operation.Exploited Computer Aided Platform and Validated VRP by simulation software. Compared this improved genetic algorithm with the existing genetic algorithm and approximation algorithms through an example, convergence rate Much faster and the Optimal results from 117.0km Reduced to 107.8km,proved that this article improved genetic algorithm can be faster to reach an optimal solution. The results showed that the improved GA can keep the variety of cross and accelerate the search speed.

A genetic algorithm approach to the integrated inventory-distribution problem

We introduce a new genetic algorithm (GA) approach for the integrated inventory distribution problem (IIDP). We present the developed genetic representation and use a randomized version of a previously developed construction heuristic to generate the initial random population. We design suitable crossover and mutation operators for the GA improvement phase. The comparison of results shows the significance of the designed GA over the construction heuristic and demonstrates the capability of reaching solutions within 20% of the optimum on sets of randomly generated test problems.

An Improvement of Genetic Algorithms by Search Space Reductions in Solving Large-scale Flowshop Problems

While searching for suboptimal solutions for large-scale problems, it is critical to force search algorithms on promising regions. This paper presents genetic algorithms with search space reductions (RGAs) and their application to solving large-scale permutation flowshop problems. The reduced search spaces are defined by adding precedence constraints generated by heuristic rules. To balance between the size of reduced spaces and the risk of missing good solutions, a set of consecutively included search spaces is proposed. RGAs are implemented and their performance is tested on a large-scale flowshop problem. Primary experiments show that the RGAs outperform the standard genetic algorithms greatly. Moreover, we propose an improved uniform crossover operator which preserves the precedence constraints to focus genetic search on the specified search spaces. It is shown from computational experiments that the mechanism of search space reductions works well with GAs and RGAs outperform standard genetic algorithms significantly.

Improvement of genetic algorithms with decomposition procedures for large-scale multiobjective multidimensional 0-1 knapsack problems incorporating fuzzy goals

Improvement of genetic algorithms with decomposition procedures for large-scale multiobjective multidimensional 0-1 knapsack problems incorporating fuzzy goals