Opposition-based learning in shuffled frog leaping

Two new hybrid algorithms are proposed to improve the performances of the meta-heuristic optimization algorithms, namely the Grey Wolf Optimizer (GWO) and Shuffled Frog Leaping Algorithm (SFLA). Firstly, it advances the hierarchy and position updating of the mathematical model of GWO, and then the SGWO algorithm is proposed based on the advantages of SFLA and GWO. It not only improves the ability of local search, but also speeds up the global convergence. Secondly, the SGWOD algorithm based on SGWO is proposed by using the benefit of differential evolution strategy. Through the experiments of the 29 benchmark functions, which are composed of the functions of unimodal, multimodal, fixed-dimension and composite multimodal, the performances of the new algorithms are better than that of GWO, SFLA and GWO-DE, and they greatly balances the exploration and exploitation. The proposed SGWO and SGWOD algorithms are also applied to the prediction model based on the neural network. Experimental results show the usefulness for forecasting the power daily load.

New Hybrid Algorithms for Prediction of Daily Load of Power Network

Fast three-dimensional Otsu thresholding with shuffled frog-leaping algorithm

https://nottingham-repository.worktribe.com/preview/714779/INS13nsga.pdf

A nondominated sorting genetic algorithm for bi-objective network coding based multicast routing problems

The shuffled frog-leaping algorithm (SFLA) is a relatively new meta-heuristic optimization algorithm that can be applied to a wide range of problems. After analyzing the weakness of traditional SFLA, this paper presents an enhanced shuffled frog-leaping algorithm (MS-SFLA) for solving numerical function optimization problems. As the first extension, a new population initialization scheme based on chaotic opposition-based learning is employed to speed up the global convergence. In addition, to maintain efficiently the balance between exploration and exploitation, an adaptive nonlinear inertia weight is introduced into the SFLA algorithm. Further, a perturbation operator strategy based on Gaussian mutation is designed for local evolutionary, so as to help the best frog to jump out of any possible local optima and/or to refine its accuracy. In order to illustrate the efficiency of the proposed method (MS-SFLA), 23 well-known numerical function optimization problems and 25 benchmark functions of CEC2005 are selected as testing functions. The experimental results show that the enhanced SFLA has a faster convergence speed and better search ability than other relevant methods for almost all functions.

Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems

A new robust watermarking method, named SFLA-QIM, is proposed based on the shuffled frog leaping algorithm (SFLA) and quantization index modulation (QIM). The shuffled frog leaping algorithm is utilized to find out the optimal embedding position and adaptive quantization step for embedding watermark into a carrier image in the framework of QIM. A carefully chosen fitness function is designed in terms of the Peak Signal to Noise Ratio (PSNR) and the Normalized Correlation (NR) value to achieve high transparency and robustness. The proposed scheme is blind. Compared with other quantization index related watermarking methods, SFLA-QIM exhibits satisfactory robustness against a wide variety of attacks such as amplitude scaling, filtering, noise addition, cropping and JPEG compression.

https://www.wacong.org/autosoft/auto/172Abstracts/10_Li-Liu-Wang-Pan.pdf

A New Robust Watermarhing Scheme Based on Shuffled Frog Leaping Algorithm

A new robust watermarking method, named QIM-NSGA-II, is proposed based on the non-dominated sorting genetic algorithm II (NSGA-II) and quantization index modulation (QIM). The NSGA-II algorithm is utilized to find out the optimal embedding position and adaptive quantization step for embedding watermark into a carrier image in the framework of QIM. In the process of searching an optimal solution, the trade-off between robustness and image fidelity of the watermarked image is represented by the Pareto-Front discovered by NSGA-II. Experiment results show that the proposed scheme has a good robustness against common attacks, such as amplitude scaling, noise, filtering, cropping, JPEG compression.

An Adaptive Multi-objective Image Watermarking Scheme for QIM Using NSGA-II

The increasing popularity of electric vehicles (EVs) has posed significant challenges to the existing charging facilities. The existing research works on battery swapping station (BSS) are usually based on the same type of EV battery. This paper introduces a BSS model with multiple battery types for handling the EVs’ requests with diverse battery types. To maximize the battery diversity at BSS, we propose a novel objective function with weighted summarized variables and the average charging time of each battery type. We specially designed a dynamic charging schedule strategy to optimize the output power of the charging units at a BSS. Experimental results have been illustrated to verify the effectiveness of the proposed dynamic charging schedule model with multiple battery types.

A Dynamic Charging Schedule Model for Electric Vehicle Battery Swapping Stations with Multiple Battery Types

Following the intuition that the image variation of faces can be effectively modeled by low dimensional linear spaces, we propose a novel linear subspace learning method for face analysis in the framework of graph embedding model, called semi-supervised graph embedding (SGE). This algorithm builds an adjacency graph which can best respect the geometry structure inferred from the must-link pairwise constraints, which specify a pair of instances belong to the same class. The projections are obtained by preserving such a graph structure. Using the notion of graph Laplacian, SGE has a closed solution of an eigen-problem of some specific Laplacian matrix and therefore it is quite efficient. Experimental results on Yale standard face database demonstrate the effectiveness of our proposed algorithm.

Na Wang

Papers

A New Robust Watermarhing Scheme Based on Shuffled Frog Leaping Algorithm

An Adaptive Multi-objective Image Watermarking Scheme for QIM Using NSGA-II

A Dynamic Charging Schedule Model for Electric Vehicle Battery Swapping Stations with Multiple Battery Types

Semi-Supervised Dimensionality Reduction with Pairwise Constraints Using Graph Embedding for Face Analysis