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Design of full band differentiators of integer and non-integer/fractional order using Black Hole Optimization (BHO) algorithm is presented in this paper. The discrete models of the differentiators are obtained without using any s-domain to z-domain generating function. The average performance, pole-zero characteristics, and the error convergence are thoroughly analysed to determine the efficacy of...
In this paper a Genetic Algorithm (GA) is used to partition a distribution network with the aim to minimize the energy exchange among the microgrids (i.e. maximize self-consumption) in presence of distributed generation. The proposed GA is tested on the IEEE prototypical network PG & E 69-bus. The microgrid partitioning is tested over a period of one year with hourly sampled data of real household...
This study presents a new sine and cosine (S&C) optimization algorithm using a novel position update approach. In the proposed algorithm, the position update procedure for each search agent is determined by two coefficients, namely the exploration rate and the exploitation rate. These coefficients are updated in each run of the algorithm and provide an appropriate balance between the exploration...
Wavelet neural network has a slow convergence rate, weak global search capability and easy to search the search results to a minimum, while the genetic algorithm has a high degree of parallelism, randomness, adaptive search and global optimization. The wavelet neural network is transformed and transformed to obtain the discretized wavelet neural network. In this paper, the three-layer wavelet neural...
Based on the existing algorithm of fault-sectin location in distributed network containing distributed generation(DG), the effect of localization is not ideal, especially premature convergence problem in the original genetic algorithm, a new fault location method of chaotic optimization based on multiple-population genetic algorithm is proposed. Firstly, the introduction of a number of population...
In recent years, with the high frequency of the infectious diseases outbreak, the prediction of the infectious diseases has become more and more important, so effective prediction of the infectious diseases can safeguard social stability and promote national economic prosperity. In order to improve the predictive accuracy of infectious diseases, the weight and threshold of BP neural network was optimized...
Evolutionary algorithms are optimization methods inspired by natural evolution. They usually search for the optimal solution in large space areas. In Evolutionary Algorithms it is very important to select an appropriate balance between the ability of the algorithm to explore and exploit the search space. The paper presents a hybrid system consisting of a Genetic Algorithm and an Evolutionary Strategy...
The Evolutionary algorithm (EA) for researching parameters of nonlinear system is a rapidly growing field of identification. This can owe to the importance of EA for both the theoretical field and the engineering community. However, the identification of the nonlinear system is still a knotty problem, especially when heavy-tailed noises exists. Compared to classical identification methods, EA has...
In this paper, an adaptive genetic algorithm based on multi-population elite selection strategy is proposed. The multi-population elite selection strategy is used to preserve the optimal individuals of each group. Finally, these optimal individuals formed a population, and then use the improved adaptive genetic algorithm to finish the solution. By comparing the simulation experiments of TSP problem...
To find the best polarity of large-scale Mixed Polarity Reed-Muller (MPRM) logic circuits, this paper proposes a new Adaptive Simulated Annealing Genetic Algorithm (ASAGA) which can effectively find out the best polarity. Genetic Algorithm (GA) has outstanding global searching ability but easily falls into the local optimum, while the Simulated Annealing Algorithm (SAA) is expert in local searching...
Species distribution modeling (SDM) calculates a species’ probabilistic distribution by combining Environmental raster layers with species datasets. Such models can help to answer complex questions in Ecology/Biology/Health, e.g., by calculating impacts of climate changes in Biodiversity, or the potential for a disease spread (vectors’ modeling). Machine learning is largely applied in SDM, being the...
Genetic algorithm has been successfully applied to complexity problems of real-world. But the genetic algorithm is easy to be premature convergence, and full into local optimum. For the ergodicity of chaotic theory is adopted to genetic algorithm optimization, we put forward the improved chaos genetic algorithm. In order to avoid the slow convergence speed and local optimal problems of BP neural network,...
Based on the biological mechanism of immune algorithm, an improved immune genetic algorithm is proposed, in which particle swarm optimization is taken as global searching strategy to improve the global search ability of the immune genetic algorithm, and progressive optimization algorithm is used for evolving operation of control strategy to improve its local search ability. At the same time, because...
Modern industry has been applying various optimization methods in solving problems and increasing productivity. The bin packing problem is one of the well-known problems in many industries, such as, glass manufacturing industry, paper packaging industry and garment industry that commonly require optimization techniques to improve the productivity. This paper introduces the use of the shuffled frog...
Striking hostile ground targets for air force is a crucial way to achieve air superiority in modern warfare. For the maximum efficiency of limited munitions, the optimal weapon target assignment (WTA) scheme should be developed. Aimed at the fact that lots of approaches have been presented for the WTA problem of stationary targets, while less for the ones of moving targets, this paper proposes an...
To improve the measuring accuracy of planar curve profile error, an improved genetic algorithm is put forward to realize self-adaptive matching of measured curve, eliminating the position deviation during error evaluation of planar curve profile. It not only improves the efficiency and precision of the algorithm but also prevents premature convergence to local optimal solutions by introducing a relative...
There are a lot of typical statistical problems in discrete combination optimization, including integer linear programming, covering problem, knapsack problem, graph theory, network flow and dispatching. As for the NPC (Non-deterministic Polynomial complete) problems, many algorithms have been developed for the discrete optimization where the heuristic algorithm is one kind of the important and effective...
As cloud computing is growing rapidly, efficient task scheduling algorithm plays a vital role to improve the resource utilization and enhance overall performance of the cloud computing environment. However, task scheduling is the severe challenge needed to solve urgently in cloud computing. Therefore, the simulated annealing multi-population genetic algorithm (SAMPGA) is proposed for task scheduling...
This research work investigates various search optimization algorithms for quick estimation of blurring filter for single image blind deblurring. The optimization algorithms include Genetic Algorithm (GA), Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO). Validation has been performed on various image and multiple image quality metrics were utilized for the analysis of convergence...
This paper presents novel approach for optimal distribution network reconfiguration using the combination of cycle-break algorithm and genetic algorithms. Significant improvements are introduced in the phases of initial population generation as well as other general operations inside genetic algorithm. These improvements lead to better convergence rate and computational time reduction. Even though...
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