نتایج جستجو برای: bacteria foraging algorithm bfa and binary genetic algorithm bga
تعداد نتایج: 17011352 فیلتر نتایج به سال:
Binary-coded genetic algorithms (BGAs) traditionally use a uniform mapping to decode strings to corresponding real-parameter variable values. In this paper, we suggest a non-uniform mapping scheme for creating solutions towards better regions in the search space, dictated by BGA’s population statistics. Both variable-wise and vector-wise non-uniform mapping schemes are suggested. Results on fiv...
Binary Factor Analysis (BFA) uncovers the independent binary information sources from observations with wide applications. BFA learning hierarchically nests three levels of inverse problems, i.e., inference of binary code for each observation, parameter estimation and model selection. Under Bayesian YingYang (BYY) framework, the first level becomes an intractable Binary Quadratic Programming (B...
a problem of computer vision applications is to detect regions of interest under dif- ferent imaging conditions. the state-of-the-art maximally stable extremal regions (mser) detects affine covariant regions by applying all possible thresholds on the input image, and through three main steps including: 1) making a component tree of extremal regions’ evolution (enumeration), 2) obtaining region ...
This paper deals with a method of the radiation pattern determination of the directional antennas. The method combining both the functional minimization method and the Fourier iterative algorithm is based on the phaseless near-field measurement on two plane surfaces. The method is used for a reconstruction of the phase distribution on the aperture of the measured antenna, and for the determinat...
This paper proposed an Improved Bacterial Foraging Optimization (IBFO) algorithm to enhance the optimization ability of original Bacterial Foraging Optimization. In the new algorithm, Social cooperation is introduced to guide the bacteria tumbling towards better directions. Meanwhile, adaptive step size is employed in chemotaxis process. The new algorithm is tested on a set of benchmark functio...
Feature selection is an important task for data analysis and information retrieval processing, pattern classification systems, and data mining applications. It reduces the number of features by removing noisy, irrelevant and redundant data. In this paper, a novel feature selection algorithm based on Ant Colony Optimization (ACO), called Advanced Binary ACO (ABACO), is presented. Features are tr...
A novel hybrid strategy combining a spiral dynamic algorithm (SDA) and bacterial foraging (BFA) is presented in this article. model incorporated into the chemotaxis of BFA to enhance capability exploration exploitation phases both SDA with aim improve fitness accuracy for convergence speed as well BFA. The proposed tested Congress on Evolutionary Computation 2013 (CEC2013) benchmark functions, ...
To improve the protein folding simulations, polymorphic bacterial chemotaxis optimization (PBCO) was investigated on a 2D lattice model. PBCO is a novel intelligent problem solving technique inspired from the foraging behavior of bacteria. We compared PBCO with standard genetic algorithm (SGA) and Immune Genetic Algorithm (IGA) for various chain lengths. It shows that PBCO has the highest succe...
one of the most important extensions of the capacitated vehicle routing problem (cvrp) is the vehicle routing problem with simultaneous pickup and delivery (vrpspd) where customers require simultaneous delivery and pick-up service. in this paper, we propose an effective ant colony optimization (eaco) which includes insert, swap and 2-opt moves for solving vrpspd that is different with common an...
Flow shop scheduling problem with missing operations is studied in this paper. Missing operations assumption refers to the fact that at least one job does not visit one machine in the production process. A mixed-binary integer programming model has been presented for this problem to minimize the makespan. The genetic algorithm (GA) and tabu search (TS) are used to deal with the optimization...
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