An improved Shuffled Frog Leaping Algorithm with a fast search strategy for optimization problems

Jaballah, Sami; Rouis, Kais; Abdallah, Faten Ben; Tahar, Jamal Bel Hadj

doi:10.1109/iccp.2014.6936975

Cited by 13 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the sub-populations are merged to form a population consisting of a certain number of solutions. Subsequently, the sub-populations are redistributed using the approach, and the subsequent iteration of local search is executed 27 . Continue iterating the procedure until the desired solution is attained or the termination condition is satisfied.…”

Section: Shuffled Frog Leaping Algorithm (Sfla)mentioning

confidence: 99%

Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm

Dankolo,

Radzi,

Mustaffa

et al. 2024

Baghdad Sci.J

View full text Add to dashboard Cite

The rise of edge-cloud continuum computing is a result of the growing significance of edge computing, which has become a complementary or substitute option for traditional cloud services. The convergence of networking and computers presents a notable challenge due to their distinct historical development. Task scheduling is a major challenge in the context of edge-cloud continuum computing. The selection of the execution location of tasks, is crucial in meeting the quality-of-service (QoS) requirements of applications. An efficient scheduling strategy for distributing workloads among virtual machines in the edge-cloud continuum data center is mandatory to ensure the fulfilment of QoS requirements for both customer and service provider. Existing research used metaheuristic algorithm to solve tak scheduling problem, however, must of the existing metaheuristics used suffers from falling into local mina due to their inefficiency to avoid unfeasible region in the solution search space. Therefore, there is a dire need for an efficient metaheuristic algorithm for task scheduling. This study proposed an FPA-ISFLA task scheduling model using hybrid flower pollination and improved shuffled frog leaping algorithms. The simulation results indicate that the FPA-ISFLA algorithm is superior to the PSO algorithm in terms of makespan time, resource utilization, and execution cost reduction, especially with an increasing number of tasks.

show abstract

Section: Shuffled Frog Leaping Algorithm (Sfla)mentioning

confidence: 99%

Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm

Dankolo,

Radzi,

Mustaffa

et al. 2024

Baghdad Sci.J

View full text Add to dashboard Cite

show abstract

“…Heuristic algorithms try to catch the best solution among all solutions by using existing knowledge for solving the problem defined as an optimization problem [1]. Linear and dynamic programming based algorithms do not guarantee to find optimum solution for complex NP-hard type problems [2]. For solving the complex optimization problems it is needed to a powerful computation at an exponential time [3].…”

Section: Introductionmentioning

confidence: 99%

“…it has been improved a lot of algorithms in literature thanks to adding new operators into existing metaheuristic algorithms such as PSO, ACO and Shuffled Frog Leaping Algorithm (SFLA) to increase the performance of them. Jaballah et al (2014) proposed the Improved Shuffled Frog Leaping Algorithm (ISFLA) by inserting two new operators such as c_1 and c_2 into the original SFLA. According to the experimental results of the study of Jaballah et al, ISFLA has proved to be quite successful results compared to original SFLA [2].…”

Section: Introductionmentioning

confidence: 99%

“…Jaballah et al (2014) proposed the Improved Shuffled Frog Leaping Algorithm (ISFLA) by inserting two new operators such as c_1 and c_2 into the original SFLA. According to the experimental results of the study of Jaballah et al, ISFLA has proved to be quite successful results compared to original SFLA [2]. Lovbjerg (2002) proposed the improved PSO algorithm by using the strategies of Genetic Algorithm and Hill Climbing algorithm in PSO algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Improved Tree Seed Algorithm for Optimization Problems

ASLAN¹,

Beşkirli²,

Kodaz³

et al. 2018

IJMLC

View full text Add to dashboard Cite

Various heuristic algorithms have been proposed in the literature for solving optimization problems. Tree-seed algorithm (TSA) is inspired from relation between trees and seeds that a population-based evolutionary algorithm. when create process of a seed occur in TSA, the position updating of each dimension of the seed is calculated separately. In scope this study, Some changes have been implemented to original TSA. A new operator was added to the position update equation of original TSA when create a seed from tree. This operator is calculated by dynamically according to the dimension of the problem. As the dimension of the problem increases, the value of the this operator decreases. In addition, we determined an upper and a lower bound for the update process of the seed. The Improved Tree Seed Algorithm (ITSA) proposed in this study and the TSA have been tested on some benchmark functions in the literature. As a result, when the experimental results are taken into consideration, it is understood that the proposed algorithm ITSA is obtained more effective results for benchmark functions than TSA. Moreover, it is observed that ITSA found quite successful results compared with TSA for large-scale benchmark problems.

show abstract

Improved shuffled Frog leaping algorithm with unsupervised population partitioning strategies for complex optimization problems

Mehta

2024

J Comb Optim

View full text Add to dashboard Cite

An improved Shuffled Frog Leaping Algorithm with a fast search strategy for optimization problems

Cited by 13 publications

References 10 publications

Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm

Efficient Task Scheduling Approach in Edge-Cloud Continuum based on Flower Pollination and Improved Shuffled Frog Leaping Algorithm

An Improved Tree Seed Algorithm for Optimization Problems

Improved shuffled Frog leaping algorithm with unsupervised population partitioning strategies for complex optimization problems

Contact Info

Product

Resources

About