An Improved Genetic Algorithm and Its Application
نویسندگان
چکیده
Traditional genetic algorithm trapped into the local minimum easily. Therefore, based on a simple genetic algorithm and combine the base ideology of orthogonal design method then applied it to the population initialization, using the intergenerational elite mechanism, as well as the introduction of adaptive local search operator to prevent trapped into the local minimum and improve the convergence speed to form a new genetic algorithm. Through the series of numerical experiments, the new algorithm has been proved to be efficiency.
منابع مشابه
Improved Automatic Clustering Using a Multi-Objective Evolutionary Algorithm With New Validity measure and application to Credit Scoring
In data mining, clustering is one of the important issues for separation and classification with groups like unsupervised data. In this paper, an attempt has been made to improve and optimize the application of clustering heuristic methods such as Genetic, PSO algorithm, Artificial bee colony algorithm, Harmony Search algorithm and Differential Evolution on the unlabeled data of an Iranian bank...
متن کاملAn improved genetic algorithm for multidimensional optimization of precedence-constrained production planning and scheduling
Integration of production planning and scheduling is a class of problems commonly found in manufacturing industry. This class of problems associated with precedence constraint has been previously modeled and optimized by the authors, in which, it requires a multidimensional optimization at the same time: what to make, how many to make, where to make and the order to make. It is a combinatorial,...
متن کاملOptimal Placement and Sizing of Distributed Generation Via an Improved Nondominated Sorting Genetic Algorithm II
The use of distributed generation units in distribution networks has attracted the attention of network managers due to its great benefits. In this research, the location and determination of the capacity of distributed generation (DG) units for different purposes has been studied simultaneously. The multi-objective functions in the optimization model are reducing system line losses; reducing v...
متن کاملApplication of an Improved Neural Network Using Cuckoo Search Algorithm in Short-Term Electricity Price Forecasting under Competitive Power Markets
Accurate and effective electricity price forecasting is critical to market participants in order to make an appropriate risk management in competitive electricity markets. Market participants rely on price forecasts to decide on their bidding strategies, allocate assets and plan facility investments. However, due to its time variant behavior and non-linear and non-stationary nature, electricity...
متن کاملApplication of artificial neural network and genetic algorithm to modelling the groundwater inflow to an advancing open pit mine
In this study, a hybrid intelligent model has been designed to predict groundwater inflow to a mine pit during its advance. Novel hybrid method coupling artificial neural network (ANN) with genetic algorithm (GA) called ANN-GA, was utilised. Ratios of pit depth to aquifer thickness, pit bottom radius to its top radius, inverse of pit advance time and the hydraulic head (HH) in the observation w...
متن کاملA New Multi-Objective Optimization Method Based on Genetic- Fuzzy Algorithm and its Application in Induction Motor Speed Control
In this paper, a new method based on genetic-fuzzy algorithm for multi-objective optimization is proposed. This method is successfully applied to several multi-objective optimization problems. Two examples are presented: the first example is the optimization of two nonlinear mathematical functions and the second one is the design of PI controller for control of an induction motor drive supplie...
متن کامل