Adaptive Mutation Probability Research Articles

Parameter adjustment based on improved genetic algorithm for cognitive radio networks

Multi-objective parameter adjustment plays an important role in improving the performance of the cognitive radio (CR) system. Current research focus on the genetic algorithm (GA) to achieve parameter optimization in CR, while general GA always fall into premature convergence. Thereafter, this paper proposed a linear scale transformation to the fitness of individual chromosome, which can reduce the impact of extraordinary individuals exiting in the early evolution iterations, and ensure competition between individuals in the latter evolution iterations. This paper also introduces an adaptive crossover and mutation probability algorithm into parameter adjustment, which can ensure the diversity and convergence of the population. Two applications are applied in the parameter adjustment of CR, one application prefers the bit error rate and another prefers the bandwidth. Simulation results show that the improved parameter adjustment algorithm can converge to the global optimal solution fast without falling into premature convergence.

A modified DNA genetic algorithm for parameter estimation of the 2-Chlorophenol oxidation in supercritical water

Based on the mechanism of biological DNA genetic information and evolution, a modified DNA genetic algorithm (MDNA-GA) is proposed to estimate the kinetic parameters of the 2-Chlorophenol oxidation in supercritical water. In this approach, DNA encoding method, choose crossover operator and frame-shift mutation operator inspired by the biological DNA are developed for improving the global searching ability. Besides, an adaptive mutation probability which can be adjusted automatically according to the diversity of population is also adopted. A local search method is used to explore the search space to accelerate the convergence towards global optimum. The performance of MDNA-GA in typical benchmark functions and kinetic parameter estimation is studied and compared with RNA-GA. The experimental results demonstrate that the proposed algorithm can overcome premature convergence and yield the global optimum with high efficiency.

Research on Nondestructive Image Compression Technology Based on Genetic Algorithm

Based on nondestructive and block iteration function the characteristics of the system, and put forward a kind of improved the global optimal solution from similar partition adaptive genetic algorithm is proposed. In the algorithm for the father the searching space of the individual pieces by gray coding method; Definition of father and son of minimum error for the match fitness function; Genetic algorithm is put forward the improvement of the linear adaptive crossover and mutation probability; Take excellent protection strategy choice. The experimental results show that this method in the similar image guarantee the quality and the compression ratio decompression also can obviously reduce compressed time, effectively improve the searching efficiency.

Optimal node placement of industrial wireless sensor networks based on adaptive mutation probability binary particle swarm optimization algorithm

Industrial Wireless Sensor Networks (IWSNs), a novel technique in industry control, can greatly reduce the cost of measurement and control and improve productive efficiency. Different from Wireless Sensor Networks (WSNs) in non-industrial applications, the communication reliability of IWSNs has to be guaranteed as the real-time field data need to be transmitted to the control system through IWSNs. Obviously, the network architecture has a significant influence on the performance of IWSNs, and therefore this paper investigates the optimal node placement problem of IWSNs to ensure the network reliability and reduce the cost. To solve this problem, a node placement model of IWSNs is developed and formulized in which the reliability, the setup cost, the maintenance cost and the scalability of the system are taken into account. Then an improved adaptive mutation probability binary particle swarm optimization algorithm (AMPBPSO) is proposed for searching out the best placement scheme. After the verification of the model and optimization algorithm on the benchmark problem, the presented AMPBPSO and the optimization model are used to solve various large-scale optimal sensor placement problems. The experimental results show that AMPBPSO is effective to tackle IWSNs node placement problems and outperforms discrete binary Particle Swarm Optimization (DBPSO) and standard Genetic Algorithm (GA) in terms of search accuracy and the convergence speed with the guaranteed network reliability.

Research on University Timetabling Problem Based on the best Decimal Chromosome Replacing GA

Timetabling Problem (TP) was analyzed detailedly, an optimization mathematical model of TP was established, and the framework structure to solving TP was found. According to characteristics of TP, GA was introduced, a variety of improved schemes were designed , includes: decimal code scheme, initial population design scheme ,fitness function design scheme, the best chromosome replacing strategy, adaptive crossover probability and adaptive mutation probability design scheme. Simulation results show that the proposed GA can satisfy multiple constraint conditions and resolve Timetabling Problem more effectively.

An Improved Adaptive Genetic Algorithm Based on Hormone Modulation Mechanism for Job-shop Scheduling Problem

An improved adaptive genetic algorithm (IAGA) for solving the minimum makespan problem of job shop scheduling problem (JSP) is presented. Though the traditional genetic algorithm (GA) exhibits implicit parallelism and can retain useful redundant information about what is learned from previous searches by its representation in individuals in the population, yet GA may lose solutions and substructures due to the disruptive effects of genetic operators and is not easy to regulate GA’s convergence. The proposed IAGA is inspired from hormone modulation mechanism, and then the adaptive crossover probability and adaptive mutation probability are designed. The proposed IAGA is characterized by simplifying operations, high search precision, overcoming premature phenomenon and slow evolution. The proposed method by employing operation-based encoding is effectively applied to solve a dynamic job-shop scheduling problem (DJSP) and a complicated contrastive experiment of JSP in manufacturing system. Meanwhile, in order to ensure to create a feasible solution, a new method for crossover operation is adopted, named, partheno-genetic operation (PGO). The computational results validate the effectiveness of the proposed IAGA, which can not only find optimal or close-to-optimal solutions but can also obtain both better and more robust results than the existing genetic algorithms reported recently in the literature. By employing IAGA, machines can be used more efficiently, which means tasks can be allocated appropriately, production efficiency can be improved, and the production cycle can be shortened efficiently.

SU-E-T-614: An Optimization Algorithm for Beam Angle, Beam Weight and Wedge Angle in Forward Treatment Planning of External-Beam Radiotherapy Based on an Integer-Representation Adaptive Mutation Probability Genetic Algorithm

Purpose: To present the development of an optimization algorithm for beam angle, beam weight and wedge angle in forward treatment planning of external-beam radiotherapy using a genetic algorithm (GA). Methods: An adaptive mutation probability (AMP) integer-representation GA was applied for this optimization process in the MATLAB programming environment. The code allows various user-defined limits and starting points as well as comprehensive searches. We used an integer representation for all variables in the chromosomes pool for encoding the steps, because wedge angles often take discrete values (i.e. 0, 15, 30, 45 and 60 degrees). To improve performance, we designed a dynamic mutation probability assignment code in each generation so that the algorithm automatically adapts the mutation probability using the standard deviation of fitness values of the population at each generation. If the fitness diversity is great enough, a low mutation probability will be applied, and if the fitness values have low diversity, a high mutation probability will be applied. A dose calculation program using correction-based techniques and the CT images of the patient was also written within the same software. The GA code was tested using a standard test function both with AMP and with a constant mutation probability across all GA generations. Convergence of beam angle, beam weight and wedge angle was also investigated. Results: With the AMP technique, the GA maintained the population diversity in the chromosomes pool to avoid premature convergence into a local minimum. Test results showed that the algorithm with AMP (run time of 5 min for a simple standard test function) is more robust compared to the conventional method. Conclusions: This algorithm is a feasible and promising tool for optimization of treatment planning parameters with an acceptable computation time. Testing the algorithm against experienced treatment planners will be performed next.

Determination of the Optimum Capacity and Type of Wind Turbine Generators in a Power System Considering Reliability and Cost

The economic and reliability benefits of adding a wind energy conversion system (WECS) to a power system are highly dependent on the wind turbine generator (WTG) installation design, i.e., the type and number of WTG. This paper presents an approach to determine the optimum installation design at each wind site considering the WTG parameters, the total cost of WECS and the power system reliability performance. The model is formulated as a combinatorial constrained optimization problem with a nonlinear, nondifferentiable objective function, which consists of the capital, maintenance and operating costs, and the costumer interruption costs. A number of constraints, including the total installed capacity at a specific wind site and the capital cost, are considered in the model. The model is solved using a genetic algorithm with adaptive crossover and mutation probabilities based on the standard deviation of chromosome fitness in a generation population. The proposed model and algorithm are tested on two power systems. The results show that the proposed approach can be used to obtain the maximum economic and reliability benefits associated with a WECS installation and is a powerful search technique for determining the optimum number and type of WTG considering reliability performance and costs.

An adaptive annealing genetic algorithm for the job-shop planning and scheduling problem

The genetic algorithm, the simulated annealing algorithm and the optimum individual protecting algorithm are based on the order of nature, and there exist some application limitations on global astringency, population precocity and convergence rapidity. An adaptive annealing genetic algorithm is proposed to deal with the job-shop planning and scheduling problem for the single-piece, small-batch, custom production mode. In the AAGA, the adaptive mutation probability is included to improve upon the convergence rapidity of the genetic algorithm, and to avoid local optimization, the Boltzmann probability selection mechanism from the simulated annealing algorithm, which solves the population precocity and the local convergence problems, is applied to select the crossover parents. Finally, the AAGA-based job-shop planning and scheduling problem is discussed, and the computing results of AAGA and GA are depicted and compared.

Mining Clusters in Data Sets of Data Mining: An Effective Algorithm

This paper propose a new clustering algorithm (GACR) based on genetic algorithm. The searching capability of genetic algorithms is exploited in order to search for appropriate cluster centers in the feature space such that a similarity metric of the resulting clusters is optimized. The chromosomes, which are represented as strings of real numbers, encode the centers of a fixed number of clusters. A chromosome reorganization method is proposed, which may effectively remove the degeneracy for purpose of more efficient search. A new crossover operator that exploits a measure of similarity between chromosomes is also presented. Adaptive probabilities of crossover and mutation are employed to prevent the convergence of the GA to a local optimum. The features of this algorithm are investigated and the performance is evaluated experimentally using real and synthetic datasets with K-means and GCA (10).The experimental result demonstrates that the GACR clustering algorithm has high performance, effectiveness and flexibility.

A protein inspired RNA genetic algorithm for parameter estimation in hydrocracking of heavy oil

Hydrocracking is a crucial process in refineries and suitable model is useful to understand and design hydrocracking processes. Simulating the procedure from RNA to protein, a protein inspired RNA genetic algorithm (PIRGA) is proposed to estimate the parameters of hydrocracking of heavy oil. In the PIRGA, each individual is represented by a RNA strand and a new fitness function combining traditional fitness value and individual ranking is employed to maintain population diversity. Furthermore conventional crossover operators are replaced by RNA-recoding operator and protein-folding operators to improve the searching ability. An adaptive mutation probability in the PIRGA makes the algorithm have more chance to jump out of local optima. Numerical experiments on seven benchmark functions indicate that the PIRGA outperforms other genetic algorithms on both convergence speed and accuracy greatly. 10 parameters are obtained by the PIRGA and the kinetic model for hydrocracking of heavy oil is established. Experimental results reveal that the predictive values are in good agreement with the experimental data with relative error less than 5%. The effectiveness and the robustness of the model are also validated by experiments.

An improved adaptive genetic algorithm based on hormone modulation mechanism for job-shop scheduling problem

An improved adaptive genetic algorithm (IAGA) for solving the minimum makespan problem of job-shop scheduling problem (JSP) is presented. Though the traditional genetic algorithm (GA) exhibits implicit parallelism and can retain useful redundant information about what is learned from previous searches by its representation in individuals in the population, yet GA may lose solutions and substructures due to the disruptive effects of genetic operators and is not easy to regulate GA’s convergence. The proposed IAGA is inspired from hormone modulation mechanism, and then the adaptive crossover probability and adaptive mutation probability are designed. The proposed IAGA is characterized by simplifying operations, high search precision, overcoming premature phenomenon and slow evolution. The proposed method by employing operation-based encoding is effectively applied to solve a dynamic job-shop scheduling problem (DJSP) and a complicated contrastive experiment of JSP in manufacturing system. Meanwhile, in order to ensure to create a feasible solution, a new method for crossover operation is adopted, named, partheno-genetic operation (PGO). The computational results validate the effectiveness of the proposed IAGA, which can not only find optimal or close-to-optimal solutions but can also obtain both better and more robust results than the existing genetic algorithms reported recently in the literature. By employing IAGA, machines can be used more efficiently, which means that tasks can be allocated appropriately, production efficiency can be improved, and the production cycle can be shortened efficiently.

Sequencing the Reconfigurable Assembly Line with a Hybrid Multi-Objective Genetic Algorithm

In order to solve the reconfigurable assembly line sequencing problem, a multi-objective optimization mathematical model is presented, which includes three practically important objectives. Such as minimizing the total utility work cost, minimizing the total production rate variation and minimizing reconfigurable setup cost are considered. A scheduling method for reconfigurable assembly line is proposed based on Pareto multi-objective genetic algorithm, In order to ensure the group’s variety, prevent the premature convergence problem and enhance the globe-optimization capability, some key technologies such as population ranking method, Niche technique are applied. The adaptive crossover and mutation probabilities methods are developed. The computational results show that the proposed hybrid algorithm finds solutions with better quality especially in the case of large-sized problems.

Multi-Objective Evolutionary Algorithm Based on the Fuzzy Similarity Measure

Evolutionary algorithm has gained a worldwide popularity among multi-objective optimization. This paper proposes a novel multi-objective evolutionary algorithm based on the fuzzy similarity measure. First, the best solution of every objective among the multi-objectives is obtained and they are regarded on as the referenced vector. Second, the fuzzy similarity measure between every individual and the referenced vector is solved and the fuzzy similarity measure is acted as fitness of the individual. Moreover, the pareto optimal sets are solved by means of adaptive genetic algorithm. The variety of population is kept by means of adaptive probability of crossover and mutation. At last, the algorithm is used to optimize the design parameters of cylinder helical compression spring. Simulation examples show the effectiveness of the approach proposed.

Optimal design of high pressure hydrogen storage vessel using an adaptive genetic algorithm

The weight minimum optimization of composite hydrogen storage vessel under the burst pressure constraint is considered. An adaptive genetic algorithm is proposed to perform the optimal design of composite vessels. The proposed optimization algorithm considers the adaptive probabilities of crossover and mutation which change with the fitness values of individuals and proposes a penalty function to deal with the burst pressure constraint. The winding thickness and angles of composite layers are chosen as the design variables. Effects of the population size and the number of generations on the optimal results are explored. The results using the adaptive genetic algorithm are also compared with those using the simple genetic algorithm and the Monte Carlo optimization method.

A New Line Symmetry Distance and Its Application to Data Clustering

In this paper, at first a new line-symmetry-based distance is proposed. The properties of the proposed distance are then elaborately described. Kd-tree-based nearest neighbor search is used to reduce the complexity of computing the proposed line-symmetry-based distance. Thereafter an evolutionary clustering technique is developed that uses the new line-symmetry-based distance measure for assigning points to different clusters. Adaptive mutation and crossover probabilities are used to accelerate the proposed clustering technique. The proposed GA with line-symmetry-distance-based (GALSD) clustering technique is able to detect any type of clusters, irrespective of their geometrical shape and overlapping nature, as long as they possess the characteristics of line symmetry. GALSD is compared with the existing well-known K-means clustering algorithm and a newly developed genetic point-symmetry-distance-based clustering technique (GAPS) for three artificial and two real-life data sets. The efficacy of the proposed line-symmetry-based distance is then shown in recognizing human face from a given image.

Application of Genetic Algorithms for a Tyre Production Scheduling Information System

A optimization scheduling problem in a enterprise of manufacturing tyres is discussed in this paper. And this problem is reduced to a single machine scheduling problem to minimize setup times with batchs setup time depending on sequence. A method for solving tyre production scheduling problem using an effective adaptive hybrid genetic algorithm (AHGA) is proposed. We advance a novel operator (looping & cutting operator) to improve the mountain climbing ability of the genetic algorithm, and put forward adaptive probabilities of crossover and mutation based on information entropy. Computational results show that the proposed adaptive hybrid genetic algorithm is effective and robust.

Semi-GAPS: A Semi-supervised Clustering Method Using Point Symmetry

In this paper, an evolutionary technique for the semi-supervised clustering is proposed. The proposed technique uses a point symmetry based distance measure. Semi-supervised classification uses aspects of both unsupervised and supervised learning to improve upon the performance of traditional classification methods. In this paper the existing point symmetry based genetic clustering technique, GAPS-clustering, is extended in two different ways to handle the semi-supervised classi- fication problem. The proposed semi-GAPS clustering algorithmis able to detect any type of clusters irrespective of shape, size and convexity as long as they possess the point symmetry property. Kdtree based nearest neighbor search is used to reduce the complexity of finding the closest symmetric point. Adaptive mutation and crossover probabilities are used. Experimental results demonstrate practical performance benefits of the methodology in detecting classes having symmetrical shapes in case of semi-supervised clustering.

Research on Financial Distress Prediction with Adaptive Genetic Fuzzy Neural Networks on Listed Corporations of China

To design a multi-population adaptive genetic BP algorithm, crossover probability and mutation probability are self-adjusted according to the standard deviation of population fitness in this paper. Then a hybrid model combining Fuzzy Neural Network and multi-population adaptive genetic BP algorithm—Adaptive Genetic Fuzzy Neural Network (AGFNN) is proposed to overcome Neural Network’s drawbacks. Furthermore, the new model has been applied to financial distress prediction and the effectiveness of the proposed model is performed on the data collected from a set of Chinese listed corporations using cross validation approach. A comparative result indicates that the performance of AGFNN model is much better than the ones of other neural network models.

A genetic algorithm with gene rearrangement for K-means clustering

In this paper, a new clustering algorithm based on genetic algorithm (GA) with gene rearrangement (GAGR) is proposed, which in application may effectively remove the degeneracy for the purpose of a more efficient search. A new crossover operator that exploits a measure of similarity between chromosomes in a population is also presented. Adaptive probabilities of crossover and mutation are employed to prevent the convergence of the GAGR to a local optimum. Using the real-world data sets, we compare the performance of our GAGR clustering algorithm with K-means algorithm and other GA methods. An application of the GAGR clustering algorithm in unsupervised classification of multispectral remote sensing images is also provided. Experiment results demonstrate that the GAGR clustering algorithm has high performance, effectiveness and flexibility.