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With regard to the site selection of construction and demolition of waste recycling plants in China, an optimization model for the site selection of a recycling plant was constructed using a genetic algorithm, and an empirical study was conducted with Panyu and Nansha Districts of Guangzhou City as examples. The study shows that the optimal solution obtained on optimizing the site selection of a construction...
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...
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...
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...
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...
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...
In most global optimization problems, finding a global optimum point in the whole multi-dimensional search space implies a high computational burden. We present a new approach called subdividing labeling genetic algorithm (SLGA) for continuous nonlinear optimization problems. SLGA applies mutation and crossover operators on a subdivided search space where an integer label is defined on a polytope...
Data Clustering in Data Mining is a domain which never gets out of focus. Clustering a data was always an easy task but achieving the required accuracy, precision and performance was never so easy. K means being an archaic clustering algorithm got tested and experimented thousands of times with variety of datasets and other combination of algorithm due to its robustness and simplicity but what this...
In view of the problem of premature convergence of simple genetic algorithm, a multi population genetic algorithm for traffic assignment problems was proposed, multiple populations are introduced and search simultaneous, coevolution through populations are implemented, use immigration operator to exchange information, artificial selection operator to keep best individuals of every generation, and...
For larger system, solution space increases exponentially with the number of time periods and units in the system, therefore the computation time becomes impractical. This paper presents an improved two layer approach for solution for large systems. The first layer generate constraints satisfied high fitness population (HFP) and the second layer is incorporated with a GA algorithm for solving the...
For larger system, solution space increases exponentially with the number of time periods and units in the system, therefore the computation time becomes impractical. This paper presents an improved two layer approach for solution for large systems. The first layer generate constraints satisfied high fitness population (HFP) and the second layer is incorporated with a GA algorithm for solving the...
In this paper, Genetic Algorithm (GA) is used to solve the Unit Commitment (UC) Problem. Unit commitment problem was formulated with consideration of up & down time, startup cost (Hot & Cold start), and production cost. Unit commitment schedule as well as economic dispatch is obtained to obtain total cost of generation. Problem specific operators are used in the algorithm to improve the quality...
Matrix Factorization is one of the popular approaches for learning the latent characteristics from the sparse utility matrix of recommendation systems. In recent times, Coordinate Descent based matrix factorization approach (CCD) have outperformed the other existing approaches such as Alternating Least Squares (ALS) and Stochastic Gradient Descent (SGD). While ALS is not scalable due to its cubic...
Image registration is an important preprocessing step in medical imaging applications. It can be formulated as an optimization problem where the associated energy to be optimized is a non-convex function that often shows local optima. Unlike classical numerical optimization algorithms frequently used in image registration, evolutionary optimizers involve search strategies preventing the algorithm...
Many channel assignment methods are used in cellular networks in allocating the frequencies or channels to the corresponding cells. The assignment strategy of channels should target the maximal efficiency of channels which satisfies the different constraints of the network. The problem of fixing the channels to mobile stations, an NP-hard problem, is solved using the different soft computing strategies...
This paper conducts a comparative study between an improved variants of genetic algorithm (GA) and a swarm intelligence algorithm (SIA), which are the Dual population Genetic Algorithm (DPGA) and Artificial Bee Colony (ABC) Algorithm. DPGA is a multi-population genetic algorithm (MPGA) that implements two population such as the main population and a complementary population. Since the added population...
Based on the characteristic of autonomous underwater vehicle path planning, the method of path planning was analyzed by genetic algorithm. Firstly, by means of grid, plan space was modeled into two markers. Best path was searched by genetic algorithm. Method of giving birth to initial groups was improved. Sufficiency function of path planning was given. Chamfer operator in genetic algorithm was imported...
In this paper, Genetic Algorithm (GA) based on Building Block (BB) identification is applied to optimal design of Interior Permanent Magnet Synchronous Motor (IPMSM). BBs on IPMSM design variables are identified using the proposed algorithm based on nonlinearity check by perturbation. Consequently, BB information regarding the design of IPMSM is proposed and the performance of the proposed algorithm...
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