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Modified Shuffled Frog Leaping Algorithm for Solving Economic Load Dispatch Problem  [PDF]
Priyanka Roy, A. Chakrabarti
Energy and Power Engineering (EPE) , 2011, DOI: 10.4236/epe.2011.34068
Abstract: In the recent restructured power system scenario and complex market strategy, operation at absolute minimum cost is no longer the only criterion for dispatching electric power. The economic load dispatch (ELD) problem which accounts for minimization of both generation cost and power loss is itself a multiple conflicting objective function problem. In this paper, a modified shuffled frog-leaping algorithm (MSFLA), which is an improved version of memetic algorithm, is proposed for solving the ELD problem. It is a relatively new evolutionary method where local search is applied during the evolutionary cycle. The idea of memetic algorithm comes from memes, which unlike genes can adapt themselves. The performance of MSFLA has been shown more efficient than traditional evolutionary algorithms for such type of ELD problem. The application and validity of the proposed algorithm are demonstrated for IEEE 30 bus test system as well as a practical power network of 203 bus 264 lines 23 machines system.
Improved Shuffled Frog Leaping Algorithm for Solving CVRP
基于改进混合蛙跳算法的CVRP求解

Luo Jian-ping,Li Xia,Chen Min-rong,
骆剑平
,李霞,陈泯融

电子与信息学报 , 2011,
Abstract: An improved Shuffled Frog Leaping Algorithm (SFLA) is proposed to solve the Capacitated Vehicle Routing Problem(CVRP)based on real-coded patterns. It is then combined with the power-law Extremal Optimization (τ-EO) to further improve the local search ability. The fitness for the components of an individual is carefully designed and the neighborhood for τ-EO mutation is established according to power-law probability distribution. Experimental results show that the proposed algorithm outperforms other heuristic algorithms base on PSO and GA.
Multi-objective dynamic population shuffled frog-leaping biclustering of microarray data  [cached]
Liu Junwan,Li Zhoujun,Hu Xiaohua,Chen Yiming
BMC Genomics , 2012, DOI: 10.1186/1471-2164-13-s3-s6
Abstract: Background Multi-objective optimization (MOO) involves optimization problems with multiple objectives. Generally, theose objectives is used to estimate very different aspects of the solutions, and these aspects are often in conflict with each other. MOO first gets a Pareto set, and then looks for both commonality and systematic variations across the set. For the large-scale data sets, heuristic search algorithms such as EA combined with MOO techniques are ideal. Newly DNA microarray technology may study the transcriptional response of a complete genome to different experimental conditions and yield a lot of large-scale datasets. Biclustering technique can simultaneously cluster rows and columns of a dataset, and hlep to extract more accurate information from those datasets. Biclustering need optimize several conflicting objectives, and can be solved with MOO methods. As a heuristics-based optimization approach, the particle swarm optimization (PSO) simulate the movements of a bird flock finding food. The shuffled frog-leaping algorithm (SFL) is a population-based cooperative search metaphor combining the benefits of the local search of PSO and the global shuffled of information of the complex evolution technique. SFL is used to solve the optimization problems of the large-scale datasets. Results This paper integrates dynamic population strategy and shuffled frog-leaping algorithm into biclustering of microarray data, and proposes a novel multi-objective dynamic population shuffled frog-leaping biclustering (MODPSFLB) algorithm to mine maximum bicluesters from microarray data. Experimental results show that the proposed MODPSFLB algorithm can effectively find significant biological structures in terms of related biological processes, components and molecular functions. Conclusions The proposed MODPSFLB algorithm has good diversity and fast convergence of Pareto solutions and will become a powerful systematic functional analysis in genome research.
Advances in Shuffled Frog Leaping Algorithm
随机蛙跳算法的研究进展

HAN Yi,CAI Jian-hu,ZHOU Gen-gui,LI Yan-lai,LIN Hua-zhen,TANG Jia-fu,
韩毅
,蔡建湖,周根贵,李延来,林华珍,唐加福

计算机科学 , 2010,
Abstract: Shuffled Frog Leaping Algorithm (SFLA) is a population-based novel and effective mcta-heuristics computing method, which received increasing focuses from academic and engineering optimization fields in recent years. Since SFLA is a combination of Mcmctic Algorithm (MA) with strong Local Search (LS) ability and Particle Swarm Optimination (PSO) with good Global Search (GS) capability, it is of strong optimum-searching power and easy to be implemented. In this paper, the fundamental principles and framework of SFLA were described. Then, the related researches of SFLA in the current optimization and engineering fields were summed up. Lastly, the future perspectives of SFLA were presented.
A new niche technique for multimodal function optimization using Shuffled Frog leaping algorithm
Eman Sayedi,Malihe Maghfoori Farsangi,mohammd Barati,Hossen Nezamabadi
Intelligent Systems in Electrical Engineering , 2011,
Abstract: The niche methods for search algorithms are important techniques in optimization. Most niche techniques need some extra tunable parameters to get a better performance. Achieving a good method for multimodal optimization by heuristic algorithms will be possible if and only if the population diversity is preserved. Shuffled Frog leaping (SFL) algorithm is a new heuristic algorithm that its ability is not proved for solving the multimodal problems. This paper proposes a niche method for SFL. Several benchmark problems are considered for testing the robustness and effectiveness of the proposed method over the results available in the literature. The results show that the proposed method performs well.
Application of Improved Shuffled Frog Leaping Algorithm in Optimum of Sensor Location
改进的混合蛙跳算法在传感器配置优化中的应用

LIU Xiao-qin,HUANG Kao-li,AN You-lin,LU Xiao-ming,
刘晓芹
,黄考利,安幼林,吕晓明

计算机科学 , 2011,
Abstract: Optimum of sensor location is an important research field in testability design, and it is a new attempt to use shuffled frog leaping algorithm for optimum of sensor location. Considering the optimal problem of sensor location is set in a space featuring discrete, a discrete shuffled frog leaping algorithm was proposed, and the change in position was redefined discretely. To avoid converging too fast, the algorithm was improved. Chaos optimization algorithm was used to optimize the best solution in the form of probability. An example and simulation results were provided to verify the effcctivcncss and practicability of this approach.
Improved opposition-based shuffled frog leaping algorithm for function optimization problems
改进的反向蛙跳算法求解函数优化问题

LIN Juan,ZHONG Yi-wen,MA Sen-lin,
林 娟
,钟一文,马森林

计算机应用研究 , 2013,
Abstract: Classical shuffled frog leaping algorithm is slow in convergence, and has a low convergent precision to address continuous function optimization problems. To overcome such shortages, this paper presented an improved shuffled frog leaping algorithm which combined the OBL strategy. The proposed approach employed OBL for population initialization and generation jumping to produce populations closer to high-quality solutions. The experiments carried on classic benchmark functions show that it performs significantly better both in terms of convergence speed and solution precision.
Optimal viewpoint selection for volume rendering using shuffled frog leaping algorithm
应用混合蛙跳算法的体绘制最佳视点选择

Zhang Yousai,Wang Bin,
张尤赛
,王彬

中国图象图形学报 , 2011,
Abstract: An optimal viewpoint selection method for volume rendering based on shuffled frog leaping algorithm is presented.Utilizing the opacity,luminance and structure features of the projected views of three-dimensional volume dates,a viewpoint evaluation function is constructed to identify the importance of voxels and the structural information within the volume data.Combined with this function,shuffled frog leaping algorithm is used to optimize the computation process of the optimal viewpoint selection,so as to a...
Study of modified shuffled frog leaping algorithm for solving CVRP
求解CVRP的改进混合蛙跳算法研究

WAN Bo,LU Yu,CHEN Li-yun,HE Rui-bo,
万博
,卢昱,陈立云,何瑞波

计算机应用研究 , 2011,
Abstract: To solve CVRP,this paper proposed a modified SFLA,which based on the mathematical model of CVRP,and designed a new method for constructing initial population.The modified shuffled frog leaping algorithm adopted real-coded patterns.Then it combined with adaptive differential disturbance and chaotic local search strategy in local searching.It enhanced the ability to escape from local optima and sped up the convergence of SFLA,meanwhile,maintained global convergence of SFLA.Experimental results indicate the ef...
Shuffled frog leaping algorithm for solving complex functions
求解复杂函数优化问题的混合蛙跳算法

ZHAO Peng-jun,LIU San-yang,
赵鹏军
,刘三阳

计算机应用研究 , 2009,
Abstract: Basic shuffled frog leaping algorithm(SFLA) easily trapped into local optima and had a slow convergence speed when it was used to address complex functions,in order to overcome the shortcomings,this paper proposed an improved SFLA. The proposed algorithm integrated the attraction-repulsion mechanism in the field of biology into SFLA and modified updating strategy,and thus maintains the subpopulation diversity. Experimental results show that the proposed SFLA enhances convergence velocity and avoids prematur...
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