Shuffled frog leaping algorithm sfla is a new heuristic cooperative search algorithm, which simulates the foraging behavior of a group of frogs jumping in wetlands 1. The emergence of shuffled frog leaping algorithm sfla offers a promising and effective solution for multiobjective and combinatorial optimization problems. This algorithm has been developed by eusuff and lansey 14. Shuffled frog leaping algorithm and its application to 01.
This ensures the faster convergence and global optimal solution. This paper makes use of shuffled frog leaping algorithm sfla as a training algorithm to train multilayer artificial neural network ann. Shuffled frog leaping algorithm based clustering algorithm. The performance of the solutions found by the foregoing proposed algorithms is compared with exact solutions of the mathematical programming model. Leaping of the frog is improved by the introduction of cognitive component. The msfl approach is based on two major modifications on the conventional sfl method. Shuffled frog leaping algorithm sfla file exchange. Count the minimal number of jumps that the small frog must perform to reach its target. Solving energyaware realtime tasks scheduling problem.
A discrete shuffled frogleaping algorithm to identify. The research of data mining based on improved shuffled frog. In this paper, a modified shuffled frog leaping msfl algorithm is proposed to overcome drawbacks of standard shuffled frog leaping sfl method. Shuffled frog leaping algorithm for photovoltaic model. To apply linear programming, the input output function is to be expressed as a set of linear functions which may lead to loss of accuracy. Optimization shuffled frog leaping algorithm free download as powerpoint presentation. Dawkins 1976, who coined the word in his book the selfish gene, defines. The proposed approach is based on two metaheuristic algorithms. This paper presents a novel approach using the shuffled frog leaping algorithm sfla to determine the unknown parameters of the single diode pv model. The proposed algorithms include eight transfer function and five discretization methods in order to solve the binary representation of scp. The shuffled frog leaping algorithm which combines deterministic and random search strategies shows excellent performance on various complex optimization problems. Applications to project management article in structure and infrastructure engineering 31. Here it is applied to determine optimal discrete pipe sizes.
Application of shuffled frog leaping algorithm in software. An effective hybrid cuckoo search algorithm cs with improved shuffled frog leaping algorithm isfla is put forward for solving 01 knapsack problem. Multilevel image threshold selection based on the shuffled. The social algorithm shuffled frog leaping algorithm is a new parameter free population based algorithm combined with clonal selection algorithm csa. Shuffled frog leaping algorithm optimization for acdc optimal power flow dispatch. The optimization is based on the modified shuffled frog leaping algorithm msfla aiming at determining the optimal dg allocation and sizing in the distribution network. In the building sector, for example, having a reliable approximation of the shear strength helps foundation engineers to choose the type of foundation, as well as. Shuffled frog leaping differential evolution and its application on cognitive radio throughput. The algorithm begins by randomly selecting f frogs and sorting them according to their fitness value order.
Pdf a new pulse coupled neural network pcnn for brain. This algorithm can serve as a preprocessing tool to help optimize the feature selection process of high. Nature inspired intelligent algorithms is moderately a new research paradigm that offers novel stochastic search techniques for solving many complex. Sfla is a metaheuristic optimization method based on observing and modeling the behavior of frogs. Redirect this redirect does not require a rating on the projects quality scale. Optimization of water distribution network design using the. A modified shuffled frogleaping optimization algorithm for. Data clustering with shuffled leaping frog algorithm sfla. The msfla is a new mimetic metaheuristic algorithm with efficient mathematical function and.
This paper proposes an improved shuffled frog leaping algorithm to solve the flexible job shop scheduling problem. Shuffled frog leaping algorithm sfla firstly used by eusuff and lansey in 2003 to determine. In this paper we present a discrete version of this algorithm and compare its performance with a sfl algorithm, a binary genetic algorithm bga, and a discrete particle swarm optimization dpso algorithm on seven low dimensional and five high. Adaptive grouping quantum inspired shuffled frog leaping. The shuffled frog leaping algorithm is a populationbased heuristic optimization algorithm with cooperative search metaphor inspired by natural memetics. An effective hybrid cuckoo search algorithm with improved. Sfla algorithm is a new metaheuristic algorithm with efficient mathematical function and global search functionality. A memetic metaheuristic called the shuffled frogleaping algorithm sfla has been developed for solving combinatorial optimization problems. A modified shuffled frogleaping optimization algorithm for solving optimal reactive power dispatch problem. Improved shuffled frog leaping algorithm on system reliability analysis. Pdf application of shuffled frogleaping algorithm on. Mass communications algorithms workplace diversity workplace multiculturalism. An improved shuffled frog leaping algorithm and its.
A combination of shuffled frog leaping and fuzzy logic for. Issuu is a digital publishing platform that makes it simple to publish magazines, catalogs, newspapers, books, and more online. It was used to solve feature selection in highdimensional biomedical data. Proposed technique in the proposed approach, shuffled frog leaping algorithm based clustering algorithm sbca is used to find optimal clusterheads in mobile ad hoc networks. Binarization methods for shuffled frog leaping algorithms. The sfla is a populationbased cooperative search metaphor inspired by natural memetics. Shuffled frogleaping model for solving timecostresource. Cf recommendation method based on dqsfl improves accuracy of rating score prediction. If you would like to participate, please visit the project page, where you can join the discussion and see a list of open tasks. First proposed by eusuff and lansey, the hybrid shuffled frog leaping algorithm is an artificial algorithm based on the shuffled complex evolution and pso methods.
Discrete shuffled frog leaping algorithm and its application to 01 knapsack problem. In this paper, a discrete shuffled frog leaping algorithm is proposed specially to identify influential nodes for influence maximization. In some cases, greedy algorithms construct the globally best object by repeatedly choosing the locally best option. An improved shuffled frogleaping algorithm for flexible.
Application of shuffled frogleaping algorithm on clustering. Shuffled frog leaping algorithm sfla is a metaheuristic for solving discrete optimization problems. The sfla consists of a set of interacting virtual population of frogs partitioned. In this research, a study was carried out to exploit the hybrid schemes combining two classical local search techniques i. Evolutionary algorithms, such as shuffled frog leaping, are stochastic search methods that mimic natural biological evolution andor the social behavior of species. Abstractthe shuffled frog leaping algorithm sfla, which is a memetic metaheuristic algorithm, is modeled based on the behaviors of the social frogs. The algorithm uses shuffled frog leaping algorithm for its fast global searching ability to generate initial clustering centers of fcm, and then use fcm to optimize. This paper applies the sflo algorithm to search for the multilevel thresholds using the maximum entropy met criterion.
Improved shuffled frog leaping algorithm on system. In this study, an improved algorithm is used to perform clustering. Use of the improved frogleaping algorithm in data clustering. In order to enhance the performance of shuffled frog leaping algorithm in solving optimization problem, this paper added the mutation operator to original shuffled frog leaping algorithm, an improved shuffled frog leaping algorithm of solving tsp was proposed. In the experiments presented in this paper, the exhaustive. An objective function is then calculated for each frog and is used as. In figure 3b, the circled frog in the memeplex indicated by full squares is an example.
First of all, with the framework of sfla, an improved frog leap operator is designed with the effect of the global optimal information on the frog leaping and information exchange between frog individuals combined with genetic mutation with a. Using the modified shuffled frog leaping algorithm for. Linear programming and dynamic programming techniques, for. This redirect is within the scope of wikiproject computer science, a collaborative effort to improve the coverage of computer science related articles on wikipedia.
A novel immune optimization with shuffled frog leaping. A fast shuffled frog leaping algorithm ieee conference. Developing shuffled frogleaping algorithm sfla method. However, the approximation algorithms or heuristics in 6,7,8,9,10,11 are timeconsuming and difficult to work in practice. Shuffled frog rleaping algorithm for control of selective and total harmonic distortion, a. At first, each individual of subgroups learns from the group extremum and the subgroup extremum when it is updated by the. Application of shuffled frog leaping algorithm in software project scheduling. T1 optimization of water distribution network design using the shuffled frog leaping algorithm. The new metaheuristic is called the shuffled frogleaping algorithm sfla. Different instances of the set covering problem are solved to test our algorithm showing very promising. The book provides an extensive theoretical account of the.
The idea of memetic algorithm comes from memes, which unlike genes can adapt. The algorithm contains elements of local search and global information exchange. In this study, we developed a text document clustering optimization model using a novel genetic frog leaping algorithm that efficiently clusters text documents based on selected features. While much qualitative information about a pulse can be gleaned from its frog trace, wed usually like to obtain more quantitative information, specifically, the intensity and phase vs. Simulation results show that the improved algorithm in the optimization accuracy, convergence. Hybridizing shuffled frog leaping and shuffled complex. To minimize the cost of production or to maximize the efficiency of production. Because of the weaknesses of the shuffled frog leaping algorithm sfla for optimizing some functions such as a low optimization precision, a slow speed, and trapping into the local optimum easily, etc. In this paper, we propose a directional shuffled frog leaping algorithm dsfla by introducing the directional updating and realtime interacting concepts. Multi objective combined emission constrained unit commitment.
Shuffled frog leaping algorithm sfla is proposed by eusuff and lansey 9, 10. Improved shuffled frog leaping algorithm sfla is a memetic algorithm which deals with the behaviour of group of frogs searching for the location that has the maximum amount of available food. An improved shuffled frogleaping algorithm for solving the. Application of modified shuffled frog leaping algorithm for. Shuffled frog leaping algorithm and winddriven optimization. Greedy algorithms a greedy algorithm is an algorithm that constructs an object x one step at a time, at each step choosing the locally best option. For example, in a problem with decision variables, the frogs are vectors.
This work proposes shuffled frog leaping algorithms sflas to solve set covering problems scps. Dg reduces line losses and improves system voltage profile. Then, a new searchacceleration parameter is added to the original formulation to create a modi. Directional shuffled frog leaping algorithm springerlink. The frog algorithm is a combination of ga based on. A modified shuffled frogleaping optimization algorithm. The mathematical model of the pv module is a nonlinear iv characteristic that includes several unknown parameters because of the limited information provided by the pv manufacturers. Two main steps are involved in sfla, namely, local search and global search.
By introducing the grouping strategy of the shuffled frog leaping algorithm sfla, an improved fasfla hybrid algorithm is put forward. The improved algorithm is related to research conducted at the. Pdf shuffled frog leaping algorithm optimization for ac. Genetic and improved shuffled frog leaping algorithms for. In binary problems we found approach in magnetic optimization algorithm 2, gravitational search algorithm 15, firefly algorithm 9, shuffled frog leaping algorithms 10.
This hybrid algorithm performs the parallel computation of. Frog s hungry a reading az level d leveled book word count. Distributed generation dg will have a growing role in the future of the power systems. Our shuffled frog leaping algorithm not only can obtain many feasible solutions in a shorter time but also save more energy than the related works in 6,7,8,9,10,11. The flexible job shop scheduling problem is a wellknown combinatorial optimization problem. Isfla is an approach to solve the multimode resource constrained project scheduling problem. Hybridizing shuffled frog leaping and shuffled complex evolution algorithms using local search methods. Its a procedure to make a system or design more effective, especially involving the mathematical techniques. Physics 115242 the leapfrog methodand other symplectic.
Shuffled frogleaping differential evolution and its. Geneticfrogleaping algorithm for text document clustering. An example of these rules can be seen in the reference. The leapfrog methodand other symplectic algorithms for integrating newtons laws of motion peter young dated. A discrete quantuminspired shuffled frog leaping algorithm dqsfl is proposed. Introduction one frequently obtains detailed dynamical information about interacting classical systems from molecular dynamics md simulations, which require integrating newtons equations of motion. As a new bionic intelligent optimization algorithm, sfla combines the advantages of memetic evolutionary algorithm and particle swarm optimization algorithm, with the concept. Shuffled frog leaping algorithm sfla is a memetic metaheuristic developed for combinatorial optimization by eusuff and lansey in 2003. Solving the graph coloring problem by modified shuffled frog. The purpose of the frogs is to find the maksimum food with minimum step. A new pulse coupled neural network pcnn for brain medical image fusion empowered by shuffled frog leaping algorithm article pdf available in frontiers in neuroscience march 2019 with 169. A discrete shuffled frog optimization algorithm springerlink. Specific optimization process of sfla is illustrated in section 4.
The quay in the container terminal can be configured as either a discrete or continuous quay. Next, the sfla anns are used for channel equalization. In this paper, we proposed a new solution for control of selective and total harmonic distortion problem known as shuffled frog leaping algorithm sfla. Buy products such as leapfrog, learning friends 100 words book, bilingual book for toddlers at walmart and save. Shuffled frogleaping algorithm for control of selective and. Selection from metaheuristic and evolutionary algorithms for engineering optimization book. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. N2 shuffled frog leaping algorithm sfla is a metaheuristic for solving discrete optimization problems. Shuffled frogleaping algorithm for optimal design of open. Applied mechanics and materials advances in science and technology international journal of engineering research in africa advanced engineering forum journal of biomimetics, biomaterials and biomedical engineering materials science. Application of modified shuffled frog leaping algorithm.
It is a random search algorithm inspired by natural memetics which provides high performance by integrating the potential advantages of both the memetic algorithm ma and the particle swarm optimization pso. A memetic metaheuristic called the shuffled frog leaping algorithm sfla has been developed for solving combinatorial optimization problems. Shuffled frogleaping algorithm for control of selective. This algorithm includes the local search and global search two solving modes, but in this method only the worst frog from divided group is considered for improving location. Modified shuffled frog leaping algorithm for solving. Nov 04, 2015 discrete shuffled frog leaping algorithm and its application to 01 knapsack problem. The algorithm possesses an adjustment sequence to design the strategy of local searching and an extremal optimization in information exchange. Optimizationshuffled frog leaping algorithm mathematical. The primary concern of designing a channel is to determine optimum dimensions while minimizing construction costs. An efficient modified shuffled frog leaping optimization.
A simple structured matlab implementatio of sfla for global optimization. The difficulties associated with using mathematical optimization on largescale engineering problems have contributed to the development of alternative solutions. Shuffled frogleaping algorithm and its applications. Original sfla the sfla is a memetic metaheuristic algorithm which is. First time, shuffled frog leaping algorithm sfla was expressed by eusuff and lansey in 2006eusuff et al, 2006.
A collaborative filtering recommendation method based on. The shuffled frog leaping algorithm sfla is a metaheuristic optimization method which is based on observing, imitating, and modeling the behavior of a group of frogs when searching for the location that has the maximum amount of available food. A new method based on modified shuffled frog leaping. The shuffled frog leaping sfl optimization algorithm has been successful in solving a wide range of realvalued optimization problems. Shuffled frogleaping algorithm and its applications xia li. Optimization of water distribution network design using. It is a relatively new evolutionary method where local search is applied during the evolutionary cycle. Consists of an extended shuffled frog leaping algorithm sfla for highdimensional biomedical data feature selection. Understanding machine learning machine learning is one of the fastest growing areas of computer science, with farreaching applications.
The shuffled frog leaping algorithm and quantum information theory are combined. In this paper, a memetic metaheuristic algorithms called shuffled frog leaping algorithm sfla is used to optimize sampling design variables of composite open channels. Leaping algorithm summary this chapter describes the shuffled frog. 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. The main objective of expression the sfla algorithm is to obtain an algorithm that can be solving the npcomplete optimization problems, without using of mathematical equations. Pdf a novel method based on modified shuffled frog.
In equation 1, rand determines the movement step sizes of frogs through the x b and x w positions. Learn more about leap frog, doit4me, no attempt, sfla. The algorithm is inspired by the behavior of frogs when seeking for the food. Research article by wireless communications and mobile computing. The clustering is an important technique for data mining and data analysis.
Oct 17, 2017 optimizationshuffled frog leaping algorithm 2. The sfla comprises an arrangement of communicating virtual population of frogs divided into various memeplexes. Such algorithms have been developed to arrive at nearoptimum solutions to complex and largescale optimization problems which cannot be solved by gradientbased mathematical programming techniques. The fireworks algorithm fa is a new parallel diffuse optimization algorithm to simulate the fireworks explosion phenomenon, which realizes the balance between global exploration and local searching by means of adjusting the explosion mode of fireworks bombs. This proposed method is called the maximum entropy based shuffled frog leaping algorithm thresholding mesflot algorithm. The proposed shuffled frog leaping model considers the simultaneous optimization of three important objective functions in project planning. In this paper, a memetic metaheuristic algorithms called shuffled frogleaping algorithm sfla is used to optimize sampling design variables of composite open channels.
1148 1205 296 1109 540 966 344 1486 159 133 677 842 972 964 841 676 550 1121 504 562 784 1152 1217 538 218 367 1290 1077 995 808 1175 1261 576 932 546 396 1244 179 1179 1285