Speed Of Genetic Algorithm Research Articles

An improved genetic algorithm in shared bicycle parking point allocation

Aiming to solve the problem of inadequate parking places for shared bicycles especially during peak hours, an improved genetic algorithm for parking point allocation is proposed in this paper. We integrate linear regression algorithm with the genetic algorithm to increase the direct of individual mutation, which leads to avoiding falling into local optimum. Meanwhile, we use linear regression to haste the convergence speed of genetic algorithm which ensures the new method can improve efficiency while allocating parking point. For the sake of carrying out the experiment accurately and conveniently, we use geohash to encode the locations of parking points and bicycles into short letters and numbers. According to the analysis of experimental results, it proves the improved algorithm is superior to the conventional method for parking point allocation.

Choice of fitness functions and parameter settings in Genetic Algorithms for analysis of induction motors

The paper proposes methods for enhancing the accuracy and speed of Genetic Algorithms when determining the parameters of induction motors. A systematic study of the impact of genetic operators and algorithm parameters on optimization process performance is done. New fitness functions are developed containing additional quantities such as the input power of motors. Various genetic operator types and constraints are analysed providing an insight into a specific settings choice. Special attention is given to the factors generating stochastic noise and the ways to eliminate it. To evaluate the effectiveness of the proposed fitness functions and parameter settings, ten combinatorial optimization experiments are conducted on two types of induction motors. The results show that adequate fitness functions, combined with proper genetic operators setups, can significantly enhance the accuracy and speed of Genetic Algorithms while reducing the required input data.

Study on Selecting the Optimal Algorithm and the Effective Methodology to ANN-Based Short-Term Load Forecasting Model for the Southern Power Company in Vietnam

Recently, power companies apply optimal algorithms for short-term load forecasting, especially the daily load. However, in Vietnam, the load forecasting of the power system has not focused on this solution. Optimal algorithms and can help experts improve forecasting results including accuracy and the time required for forecasting. To achieve both goals, the combinations of different algorithms are still being studied. This article describes research using a new combination of two optimal algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). This combination limits the weakness of the convergence speed of GA as well as the weakness of PSO that it easily falls into local optima (thereby reducing accuracy). This new hybrid algorithm was applied to the Southern Power Corporation’s (SPC—a large Power company in Vietnam) daily load forecasting. The results show the algorithm’s potential to provide a solution. The most accurate result was for the forecasting of a normal working day with an average error of 1.15% while the largest error was 3.74% and the smallest was 0.02%. For holidays and weekends, the average error always approximated the allowable limit of 3%. On the other hand, some poor results also provide an opportunity to re-check the real data provided by SPC.

A Survey on Crossover Operators

Crossover is an important operation in the Genetic Algorithms (GA). Crossover operation is responsible for producing offspring for the next generation so as to explore a much wider area of the solution space. There are many crossover operators designed to cater to different needs of different optimization problems. Despite the many analyses, it is still difficult to decide which crossover to use when. In this article, we have considered the various existing crossover operators based on the application for which they were designed for and the purpose that they were designed for. We have classified the existing crossover operators into two broad categories, namely (1) Crossover operators for representation of applications -- where the crossover operators designed to suit the representation aspect of applications are discussed along with how the crossover operators work and (2) Crossover operators for improving GA performance of applications -- where crossover operators designed to influence the quality of the solution and speed of GA are discussed. We have also come up with some interesting future directions in the area of designing new crossover operators as a result of our survey.

A novel switching group-size GA based on HRL

A novel switching group-size genetic algorithm (GA) based on Hierarchical Reinforcement Learning (HRL) is proposed to improve the global optimisation performance by accelerating the convergence speed of genetic algorithm and improving the computational efficiency. In the early stage of the evolution, the group can be expanded to increase diversity, while the group should be downsized to protect the more adaptive individuals in the latter stage. Chromosome crossover operation in the HRL algorithm is regarded as behaviour. Choosing the optimal method according to the specific evolution of the chromosome reflects the optimisation selection. At the same time, abstract and hierarchical system is used to decompose the problem to multilevel sub-task space. The convergence speed is improved by learning strategically in each sub-task space and multiplexing the sub-strategy between layers. The experiment has proved the validity of the algorithm.

Key Problem of Component-Based Software Development

In order to improve the component dynamic test efficiency, this paper proposes a keating component built-in test case generation method of genetic algorithm and designs the chromosome coding method. The test point and keating component facet description of dynamic test data generation method. Mass in order to improve the generation of test cases and add Yang the convergence speed of genetic algorithm. We improve the algorithm of the method for calculating the fitness function and fitness function not only consider the case of path coverage, but also considers the path coverage rate of increase, thus effectively improved the path coverage and reduce the Yang cases produce cost.

Research on SVM Line Loss Rate Prediction Based on Heuristic Algorithm

With the characteristic of line loss volatility, a research of line loss rate prediction was imperatively carried out. Considering the optimization ability of heuristic algorithm and the regression ability of support vector machine, a heuristic algorithm-support vector machine model is constructed. Case study shows that, compared with other heuristic algorithms’, the search efficiency and speed of genetic algorithm are good, and the prediction model is with high accuracy.

Structural Physical Parameter Identification Based on Empirical Genetic-Simplex Algorithm and Structural Dynamic Response

In the complex conversion analysis of multi-degrees of freedom，large calculation count needed in each calculation step of Genetic Algorithm limits the running speed of genetic algorithm. So the positive calculation count to be reduced is an effective method to enlarge the range of GA’s application. Empirical Genetic-Simplex Algorithm (EGSA) proposed in this paper is one of the effective methods to solve the problem. This method is applied to structural physical parameters identification based on the structural dynamic response. The result shows that EGSA has many advantages on precision, efficiency in searching, strong to resist the noise, and good adaptation to the incomplete information.