scholarly journals Enhanced shuffled frog leaping algorithm with improved local exploration and energy-biased load reduction phase for load balancing of gateways in WSNs

2021 ◽  
Vol 11 (1) ◽  
pp. 437-460
Author(s):  
Amol Adamuthe ◽  
Abdulhameed Pathan
Keyword(s):  
Load Balancing ◽  
Convergence Rate ◽  
Local Search ◽  
Heuristic Algorithm ◽  
Information Exchange ◽  
Load Reduction ◽  
Deterministic Approach ◽  
Shuffled Frog Leaping Algorithm ◽  
Shuffled Frog Leaping ◽  
Search Capability

Abstract Wireless sensor networks (WSNs) have grown widely due to their application in various domains, such as surveillance, healthcare, telecommunication, etc. In WSNs, there is a necessity to design energy-efficient algorithms for different purposes. Load balancing of gateways in cluster-based WSNs is necessary to maximize the lifetime of a network. Shuffled frog leaping algorithm (SFLA) is a popular heuristic algorithm that incorporates a deterministic approach. Performance of any heuristic algorithm depends on its exploration and exploitation capability. The main contribution of this article is an enhanced SFLA with improved local search capability. Three strategies are tested to enhance the local search capability of SFLA to improve the load balancing of gateways in WSNs. The first proposed approach is deterministic in which the participation of the global best solution in information exchange is increased. The next two variations reduces the deterministic approach in the local search component of SFLA by introducing probability-based selection of frogs for information exchange. All three strategies improved the success of local search. Second contribution of article is increased lifetime of gateways in WSNs with a novel energy-biased load reduction phase introduced after the information exchange step. The proposed algorithm is tested with 15 datasets of varying areas of deployment, number of sensors and number of gateways. Proposed ESFLA-RW variation shows significant improvement over other variations in terms of successful local explorations, best fitness values, average fitness values and convergence rate for all datasets. Obtained results of proposed ESFLA-RW are significantly better in terms of network energy consumption, load balancing, first gateway die and network life. The proposed variations are tested to check the effect of various algorithm-specific parameters namely frog population size, probability of information exchange and probability of energy-biased load reduction phase. Higher population size and probabilities give better solutions and convergence rate.

scholarly journals A Modified Shuffled Frog Leaping Algorithm for the Topology Optimization of Electromagnet Devices

2020 ◽  
Vol 10 (18) ◽  
pp. 6186
Author(s):  
Wenjia Yang ◽  
Siu Lau Ho ◽  
Weinong Fu
Keyword(s):  
Topology Optimization ◽  
Local Search ◽  
Three Dimensional ◽  
Optimization Method ◽  
Memetic Algorithms ◽  
Local Optimum ◽  
Shuffled Frog Leaping Algorithm ◽  
Electromagnetic Problems ◽  
Electromagnetic Devices ◽  
Shuffled Frog Leaping

The memetic algorithms which employ population information spreading mechanism have shown great potentials in solving complex three-dimensional black-box problems. In this paper, a newly developed memetic meta-heuristic optimization method, known as shuffled frog leaping algorithm (SFLA), is modified and applied to topology optimization of electromagnetic problems. Compared to the conventional SFLA, the proposed algorithm has an extra local search step, which allows it to escape from the local optimum, and hence avoid the problem of premature convergence to continue its search for more accurate results. To validate the performance of the proposed method, it was applied to solving the topology optimization of an interior permanent magnet motor. Two other EAs, namely the conventional SFLA and local-search genetic algorithm, were applied to study the same problem and their performances were compared with that of the proposed algorithm. The results indicate that the proposed algorithm has the best trade-off between the results of objective values and optimization time, and hence is more efficient in topology optimization of electromagnetic devices.

Weighted Subspace Fitting DOA Estimation Based on Shuffled Frog Leaping Algorithm

2013 ◽  
Vol 411-414 ◽  
pp. 1049-1052
Author(s):  
Zhi Cheng Zhang ◽  
Xiao Hui Yu ◽  
Hai Xin Sun
Keyword(s):  
Heuristic Algorithm ◽  
Optimization Problem ◽  
Doa Estimation ◽  
Heuristic Methods ◽  
Multimodal Optimization ◽  
Shuffled Frog Leaping Algorithm ◽  
Subspace Fitting ◽  
Shuffled Frog Leaping ◽  
Simulation Results ◽  
Computation Load

In this paper, shuffled frog leaping algorithm (SFLA) is used to reduce the computation load of weighted subspace fitting (WSF) method for DOA estimation. As a recently proposed meta-heuristic algorithm, SFLA is suitable to solve the nonlinear multimodal optimization problem which WSF method encounters, so it offers an excellent alternative to the conventional methods in WSF-DOA estimation. Thus, the feasibility of SFLA applying to WSF-DOA estimation is analyzed, and its performance is compared with other popular meta-heuristic methods. Simulation results demonstrate that the proposed method is more efficient in computation and statistical performance.

An Efficient Load Balancing of Gateways Using Improved Shuffled Frog Leaping Algorithm and Novel Fitness Function for WSNs

2017 ◽  
Vol 17 (20) ◽  
pp. 6724-6733 ◽  
Author(s):  
Damodar Reddy Edla ◽  
Amruta Lipare ◽  
Ramalingaswamy Cheruku ◽  
Venkatanareshbabu Kuppili
Keyword(s):  
Load Balancing ◽  
Fitness Function ◽  
Shuffled Frog Leaping Algorithm ◽  
Shuffled Frog Leaping

A Novel Hybrid Optimization Method of Shuffled Frog Leaping Algorithm and Particle Swarm Optimization

2013 ◽  
Vol 717 ◽  
pp. 433-438 ◽  
Author(s):  
Mei Jin Lin ◽  
Fei Luo ◽  
Yu Ge Xu ◽  
Long Luo
Keyword(s):  
Particle Swarm Optimization ◽  
Information Exchange ◽  
Search Strategy ◽  
Particle Swarm ◽  
Convergence Speed ◽  
Hybrid Optimization ◽  
Local Optimum ◽  
Shuffled Frog Leaping Algorithm ◽  
Swarm Optimization ◽  
Shuffled Frog Leaping

Shuffled frog leaping algorithm (SFLA) is a meta-heuristic algorithm, which combines the social behavior technique and the global information exchange of memetic algorithms. But the SFLA has the shortcoming of low convergence speed while solving complex optimization problems. Particle swarm optimization (PSO) is a fast searching algorithms, but easily falls into the local optimum for the diversity scarcity of particles. In the paper, a new hybrid optimization called SFLA-PSO is proposed, which introduced PSO to SFLA by combining the fast search strategy of PSO and global search strategy of SFLA. Six benchmark functions are selected to compare the performance of SFLA-PSO, basic PSO, wPSO and SFLA. The simulation results show that the proposed algorithm SFLA-PSO possesses outstanding performance in the convergence speed and the precision of the global optimum solution.

scholarly journals An Effective Hybrid Cuckoo Search Algorithm with Improved Shuffled Frog Leaping Algorithm for 0-1 Knapsack Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Yanhong Feng ◽  
Gai-Ge Wang ◽  
Qingjiang Feng ◽  
Xiang-Jun Zhao
Keyword(s):  
Information Exchange ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Genetic Mutation ◽  
Cuckoo Search Algorithm ◽  
Shuffled Frog Leaping Algorithm ◽  
Transform Method ◽  
Shuffled Frog Leaping ◽  
Binary Differential Evolution ◽  
Comparative Results

An effective hybrid cuckoo search algorithm (CS) with improved shuffled frog-leaping algorithm (ISFLA) is put forward for solving 0-1 knapsack problem. 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 small probability. Subsequently, in order to improve the convergence speed and enhance the exploitation ability, a novel CS model is proposed with considering the specific advantages of Lévy flights and frog-leap operator. Furthermore, the greedy transform method is used to repair the infeasible solution and optimize the feasible solution. Finally, numerical simulations are carried out on six different types of 0-1 knapsack instances, and the comparative results have shown the effectiveness of the proposed algorithm and its ability to achieve good quality solutions, which outperforms the binary cuckoo search, the binary differential evolution, and the genetic algorithm.

K-Means Clustering Algorithm Method Based on Shuffled Frog Leaping Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 2245-2249
Author(s):  
Zhe Tang ◽  
Ke Luo
Keyword(s):  
Local Search ◽  
Clustering Algorithm ◽  
Global Search ◽  
Experimental Results ◽  
Shuffled Frog Leaping Algorithm ◽  
The Real ◽  
Searching Strategy ◽  
Shuffled Frog Leaping ◽  
Low Efficiency

Aiming to resolve the problems of the traditional k-means clustering algorithm such as random selecting of initial clustering centers,the low efficiency of clustering,low in the real,this paper proposed a novel k-means clustering algorithm method based on shuffled frog leaping algorithm.This algorithm combined the advantages of k-means algorithm and shunffled forg leaping algorithm.A chaotic local search was introduced to improve the quality of the initial individual,a new searching strategy was presented to update frog position,that increased the optimization ability of algorithm.According to the variation of the frog’s finess variance used k-means algorithm,it has the advantages in the global search ability and convergence speed.The experimental results show that this algorithm has higher accuracy..

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