Genetic and improved shuffled frog leaping algorithms for. Shuffled frog leaping algorithm is one of the popular used optimization algorithms. Hybridizing shuffled frog leaping and shuffled complex. 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. Improved local search in shuffled frog leaping algorithm. The shuffled frog leaping algorithm which combines deterministic and random search strategies shows excellent performance on various complex optimization problems. Novel hybrid shuffled frog leaping algorithm for vrptw 4. Developing shuffled frogleaping algorithm sfla method. Application of shuffled frog leaping algorithm in software project scheduling. An improved shuffled frog leaping algorithm with single step. In this paper, we propose a hybrid shuffled frog leaping algorithm hsfla for solving the multiobjective flexible job shop scheduling problem. The algorithm is inspired by the behavior of frogs when seeking for the food. Preeti gupta, tarun kumar sharma, deepti mehrotra and ajith abraham, knowledge building through optimized classification rule set generation using.
In this paper, we present a novel multiobjective shuffled frog leaping algorithm mosfla, which incorporates an archiving strategy based on selfadaptive niche method to maintain the nondominated solutions, and improves the memetic evolution process of sfla to adapt to the multiobjective optimization problem. Shuffled frog leaping algorithm sfla has long been considered as new evolutionary algorithm of group evolution, and has a high computing performance and excellent ability for global search. Shuffled frog leaping algorithm for preemptive project scheduling problems with resource vacations based on patterson set yihan, 1,2,3 ikoukaku, 4 jianhucai, 2 yanlaili, 5 chaoyang, 1 andlilideng 2 school of management, huazhong university of. Shuffled frogleaping algorithm for control of selective and. Optimization of water distribution network design using the. Application of shuffled frogleaping algorithm on clustering liacs. 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. In figure 3b, the circled frog in the memeplex indicated by full squares is an example.
A modified shuffled frogleaping optimization algorithm. Shuffled frog leaping algorithm optimization for acdc optimal power flow dispatch. Cultural shuffled frog leaping algorithm and its applications. Sfla is a novel metaheuristic algorithm for combinatorial optimization problems. Isfla is an approach to solve the multimode resource constrained project scheduling problem. Multi objective combined emission constrained unit. Three minimization objectives the maximum completion time makespan, the total workload of all machines, and the workload of the critical machine are considered simultaneously. Pdf application of shuffled frogleaping algorithm on clustering. Multilevel image threshold selection based on the shuffled. The new metaheuristic is called the shuffled frogleaping algorithm sfla. Shuffled frog leaping algorithm the shuffled frog leaping algorithm sfl is a memetic metaheuristic that is designed to seek a global optimal solution by performing a heuristic search. 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. The purpose of the frogs is to find the maksimum food with minimum step.
We propose a novel quantuminspired shuffled frog leaping algorithm nqsfla to obtain more accurate detection results in this paper. Two main steps are involved in sfla, namely, local search and global search. Realtime rate of penetration optimization using the shuffled. By using local and global searches simultaneously, sfla is effective in solving various optimization problems.
To improve the power quality, several methods such. An improved shuffled frogleaping algorithm for flexible. The sfla is a populationbased cooperative search metaphor inspired by natural memetics. At first, each individual of subgroups learns from the group extremum and the subgroup.
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. A discrete shuffled frogleaping algorithm to identify. 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. This paper proposes an improved shuffled frog leaping algorithm to solve the flexible job shop scheduling problem. A hybrid shuffled frog leaping algorithm and intelligent. The algorithm uses memetic evolution in the form of influencing of ideas from one individual to another in a local search by simulating the food searching of frogs. T1 optimization of water distribution network design using the shuffled frog leaping algorithm. Shu ed frog leaping algorithm sfla is a swarm evolution algorithm which imitates frogs exchanging information as the divided memeplexes when they are searching for food. Dg reduces line losses and improves system voltage profile. Example of the flexible jobshop scheduling problem. 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.
The idea of memetic algorithm comes from memes, which unlike genes can adapt. This hybrid algorithm performs the parallel computation of. Pdf shuffled frog leaping algorithm optimization for ac. The frog algorithm is a combination of ga based on. 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 of water distribution network design using. Research article adaptive grouping cloud model shuffled frog. The sflo had been applied to solve the optimization problem such as image thresholding. The traditional sfla is easy to be premature convergence.
If you would like to participate, please visit the project page, where you can join the discussion and see a list of open tasks. As a new bionic intelligent optimization algorithm, sfla combines the advantages of memetic evolutionary algorithm and particle swarm optimization algorithm, with the concept. In this paper, we proposed a new solution for control of selective and total harmonic distortion problem known as shuffled frog leaping algorithm sfla. It is a relatively new evolutionary method where local search is applied during the evolutionary cycle. Application of shuffled frogleaping algorithm on clustering.
A fuzzyrough based binary shuffled frog leaping algorithm. Abstractthe shuffled frog leaping algorithm sfla, which is a memetic metaheuristic algorithm, is modeled based on the behaviors of the social frogs. The shuffled frogleaping algorithm draws its formulation from two other search techniques. It paves a new way to solve the energyaware realtime task scheduling problems. Directional shuffled frog leaping algorithm springerlink.
This method converges to the optimal solution by evolution of memes which are carried by the particles called frogs in this regime, which exchange information with each other. Simulation results show that the improved algorithm in the optimization accuracy, convergence. An improved fireworks algorithm based on grouping strategy. The algorithm possesses an adjustment sequence to design the strategy of local searching and an extremal optimization in information exchange. The shuffled frog leaping algorithm is a populationbased heuristic optimization algorithm with cooperative search metaphor inspired by natural memetics. The clustering is an important technique for data mining and data analysis. Its a procedure to make a system or design more effective, especially involving the mathematical techniques. Economic emission load dispatch by modified shuffled frog. Leaping of the frog is improved by the introduction of cognitive component.
Dawkins 1976, who coined the word in his book the selfish gene, defines. The algorithm contains elements of local search and global information exchange. Request pdf locally informed shuffled frog leaping algorithm shuffled frogleaping algorithm sfla is a memetic metaheuristic approach for solving complex optimization problems. Specific optimization process of sfla is illustrated in section 4. Using the modified shuffled frog leaping algorithm for. Shuffled frog leaping algorithm sfla was originally developed in 2003 by eusuff and lansey.
The new feature selection method is obtained by hybridizing the bsfla with the frdd. 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. Msfla starts with an initial population of x frogs created randomly like other evolutionary algorithms. Shuffled frog leaping algorithm sfla is proposed by eusuff and lansey 9, 10. The flexible job shop scheduling problem is a wellknown combinatorial optimization problem. Adaptive grouping quantum inspired shuffled frog leaping. Solving energyaware realtime tasks scheduling problem. This paper presents an objective function to deploy the radar network and a shuffled frog leaping algorithm sfla is proposed to implement the radar network deployment. In the searching population, the optimal solution is saved and the second best solution is updated. Research article shuffled frog leaping algorithm for. Optimization shuffled frog leaping algorithm free download as powerpoint presentation.
Modified shuffled frog leaping algorithm for solving. A memtic meta heuristic for discrete optimization a memetic metaheuristic called the shuffled frog leaping algorithm. Application of shuffled frog leaping algorithm to an uncapacitated. Distributed generation dg will have a growing role in the future of the power systems. Sfla is a memetic metaheuristic to find the global optimal solution. In this paper, a discrete shuffled frog leaping algorithm is proposed specially. Next, the sfla anns are used for channel equalization. The novel fuzzy roulette wheel selection is proposed and used in selecting frogs to form a. Application of shuffled frog leaping algorithm for. Natureinspired metaheuristic algorithms guide books. A fast shuffled frog leaping algorithm ieee conference. This redirect is within the scope of wikiproject computer science, a collaborative effort to improve the coverage of computer science related articles on wikipedia.
In this research, a study was carried out to exploit the hybrid schemes combining two classical local search techniques i. Discrete shuffled frog leaping algorithm and its application to 01 knapsack problem. A memetic metaheuristic called the shuffled frog leaping algorithm sfla has been developed for solving combinatorial optimization problems. The basic idea of sfla is inspired by the frog foraging behavior. The msfla is a new mimetic metaheuristic algorithm with efficient mathematical function and global search capability. An improved fireworks algorithm based on grouping strategy of the shuffled frog leaping algorithm to solve function optimization problems. It is a type of rising swarmintelligence optimizer that can optimize additional objectives, such as minimizing hydromechanical specific energy. Krishna and murty 5 proposed a novel approach called. 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. A modified shuffled frogleaping optimization algorithm for. Nature inspired intelligent algorithms is moderately a new research paradigm that offers novel stochastic search techniques for solving many complex. An improved shuffled frogleaping algorithm for knapsack. 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. Shuffled frog leaping algorithm sfla, a memetic algorithm modeled on the foraging behavior of natural species called frogs.
This paper makes use of shuffled frog leaping algorithm sfla as a training algorithm to train multilayer artificial neural network ann. Shuffled frog rleaping algorithm for control of selective and total harmonic distortion, a. Sfla embeds the features of both particle swarm optimization pso and shuffled complex evolution sce algorithm. An improved shuffled frogleaping algorithm for flexible job. The shuffled frog leaping algorithm sfla was proposed by eusuff et al.
A novel hybrid shuffled frog leaping algorithm for vehicle. Consists of an extended shuffled frog leaping algorithm sfla for highdimensional biomedical data feature selection. Shuffled frog leaping algorithm and winddriven optimization technique modified with multilayer perceptron article pdf available in applied sciences 102. 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. The substance of the deployment of radar network is a multiparameter optimization problem.
A novel immune optimization with shuffled frog leaping. A new frog leaping rule is proposed to improve the local exploration of the sfla, which in turn improves the overall performance of. Novel multiobjective shuffled frog leaping algorithm with. Shuffled frogleaping algorithm sfla is a new addition to the range of intelligent. Improved shuffled frog leaping algorithm on system. As for the search strategy, a new version of a binary shuffled frog leaping algorithm is proposed bsfla. Modified shuffled frog leaping algorithm in this section, a modified shuffled frog leaping algorithm msfla is designed based on the same frame work of shuffled frog leaping algorithm sfla. Pdf shuffled frog leaping algorithm and winddriven.
N2 shuffled frog leaping algorithm sfla is a metaheuristic for solving discrete optimization problems. The primary concern of designing a channel is to determine optimum dimensions while minimizing construction costs. The difficulties associated with using mathematical optimization on largescale engineering problems have contributed to the development of alternative solutions. Shuffled frog leaping algorithm sfla is a populationbased novel and effective. 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. Application of shuffled frog leaping algorithm in software. Pdf a novel method based on modified shuffled frog. Oppositionbased learning embedded shuffled frogleaping. The evolutionary algorithm was developed by mimicking or simulating processes found in nature and mainly includes genetic algorithms, memetic algorithms, particle swarm optimization, ant colony optimization, and shuffled frog leaping algorithm sfla.
Shuffled frog leaping algorithm sfla is a new memetic, population based. Tarun kumar sharma and millie pant, opposition based learning ingrained shuffled frog leaping algorithm, journal of computational science, elsevier doi. Performance studies on differential evolution algorithm and. Part of the advances in intelligent and soft computing book series ainsc, volume. This algorithm can serve as a preprocessing tool to help optimize the feature selection. The shuffled frog leaping algorithm sfla is a metaheuristic inspired by the natural evolution of frogs in searching for the largest source of food. A combination of shuffled frog leaping and fuzzy logic for. First time, shuffled frog leaping algorithm sfla was expressed by eusuff and lansey in 2006eusuff et al, 2006. Optimization of water distribution network design using the shuffled frog leaping algorithm journal of water resources planning and management april 2003 coupled binary linear programmingdifferential evolution algorithm approach for water distribution system optimization. A multi gpu based approach to the 01 knapsack problem using. Interactive fuzzy binary shuffled frog leaping algorithm for. Shuffled frog leap algorithm sfla is another population based metaheuristic which works in a way which closely resembles the pso.
Selection from metaheuristic and evolutionary algorithms for engineering optimization book. Pdf enhanced shuffled frogleaping algorithm for solving. Oct 21, 2014 to arrive at the optimum drilling parameters efficiently and quickly, the metaheuristic evolutionary algorithm, called the shuffled frog leaping algorithm, sfla is used in this paper. Sfla is a metaheuristic optimization method based on observing and modeling the behavior of frogs. Data clustering with shuffled leaping frog algorithm sfla. Part of the communications in computer and information science book series. Object detection plays an important role in the underwater object recognition technology of sonar equipment. Solving the graph coloring problem by modified shuffled frog. The population is separated into two parts which are searching population and competing population. This ensures the faster convergence and global optimal solution. Multi objective combined emission constrained unit commitment.
The algorithm begins by randomly selecting f frogs and sorting them according to their fitness value order. Leaping algorithm summary this chapter describes the shuffled frog. An effective hybrid cuckoo search algorithm cs with improved shuffled frog leaping algorithm isfla is put forward for solving 01 knapsack problem. A modified and efficient shuffled frog leaping algorithm msfla. The sfla is a swarm intelligence algorithm based on the memetic evolution of the social behavior of frogs. An effective hybrid cuckoo search algorithm with improved. Evolutionary algorithm, first introduced by holland 4. So, we present an improved shuffled frogleaping algorithm with single step search.
Feng y, wang g, feng q and zhao x 2014 an effective hybrid cuckoo search algorithm with improved shuffled frog leaping algorithm for 01 knapsack problems, computational intelligence and neuroscience, 2014, 3636, online publication date. Shuffled frogleaping algorithm for optimal design of open. Application of modified shuffled frog leaping algorithm. In equation 1, rand determines the movement step sizes of. N2 shuffled frog leaping algorithm sfla is a metaheuristic for.
Abstractshuffled frogleaping algorithm sfla is a heuristic optimization technique based on swarm intelligence that is inspired by foraging behavior of the swarm of frogs. The main idea of the sfla is participating of each member in previous experience of all other members. Statcom estimation using backpropagation, pso, shuffled. Genetic and improved shuffled frog leaping algorithms for a 2stage model of a hub covering location network m. To minimize the cost of production or to maximize the efficiency of production. Hybridizing shuffled frog leaping and shuffled complex evolution algorithms using local search methods. Four different methods are compared to this proposed method. Application of the shuffled frog leaping algorithm sfla in. The emergence of shuffled frog leaping algorithm sfla offers a promising and effective solution for multiobjective and combinatorial optimization problems. It was used to solve feature selection in highdimensional biomedical data.
Nov 04, 2015 discrete shuffled frog leaping algorithm and its application to 01 knapsack problem. Here, we tested sfla with a singlelevel example with 1 item and 12 periods as. The sfla is a metaheuristic optimization method inspired by the memetic evolution of a group of frogs when searching for food. In this paper, a memetic metaheuristic algorithms called shuffled frogleaping algorithm sfla is used to optimize sampling design variables of composite open channels. In equation 1, rand determines the movement step sizes of frogs through the x b and x w positions. Shuffled frog leaping algorithm by parallel implementations in fpgas, 2010 initialization step generate a random population of frogs, p and arrange them in decreasing order of fitness each frog is an n bit binary string where each bit represents an item of the knapsack.
It is based on the evolution of memes carried by individuals and a global exchange of information among the population. Oct 17, 2017 optimizationshuffled frog leaping algorithm 2. In the competing population, the mutation operator is. By introducing the grouping strategy of the shuffled frog leaping algorithm sfla, an improved fasfla hybrid algorithm is put forward. An improved shuffled frog leaping algorithm and its. Pdf application of shuffled frogleaping algorithm on. This book constitutes the refereed proceedings of the th international conference on systems simulation, asia simulation 20, held in singapore, in november 20. Genetic and improved shuffled frog leaping algorithms for a 2. In this paper, a recent evolutionary algorithm called the shuffled frog leaping algorithm sfla is applied for the solution of economic dispatch problem with multiple fuel options. The proposed nqsfla adopts a fitness function combining intraclass difference with interclass difference to evaluate the frog. Nonparametric statistical tests are conducted to compare the proposed approach with several existing methods over twenty two datasets, including nine.
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