An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications

Tang, Deyu; Zhao, Jie; Jian-hui, Yang; Liu, Zhen; Cai, Yongming

doi:10.1155/2021/8928182

Cited by 6 publications

(7 citation statements)

References 71 publications

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“…The shuffled frog leaping algorithm (SFLA) is a biological evolutionary algorithm that was introduced by Eusuff and Lansey in 2003 24 . It is founded on the concept of collective intelligence.…”

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

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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.

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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

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“…A novel scheme based on quantum evolution strategy and eigenvector evolution strategy was introduced. In this scheme, the frog leaping rule based on quantum evolution is achieved by two potential wells with the historical information for the local search, and eigenvector evolution is achieved by the eigenvector evolutionary operator for the global search [3]. By introducing acceleration factors c1 and c2 into the basic SFLA [13], the ability of the worst individual to learn from best individual within the sub memeplexes or global best individual of the entire population was improved and the convergence rate of algorithm was accelerated.…”

Section: Introductionmentioning

confidence: 99%

A modified shuffled frog leaping algorithm with inertia weight

Zhao

Wang

Liu

et al. 2023

Preprint

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The shuffled frog leaping algorithm (SFLA) is a promising metaheuristic bionics algorithm, which has been designed by the shuffled complex evolution (SCE) and the particle swarm optimization (PSO) framework. But it is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize the complex engineering problems. To overcome the short-comings, a novel modified shuffled frog leaping algorithm (MSFLA) with inertia weight is proposed in this paper. To extend the scope of the direction and length of the updated worst frog (vector) of the original SFLA, the inertia weight α was introduced and its meaning and range of the new parameters are fully explained. Then the convergence of the MSFLA is deeply analyzed and proved theoretically by a new dynamic equation formed by Z-transform. Finally, we have compared the solution of 7 benchmark function with the original SFLA, other improved SFLAs, genetic algorithm (GA), PSO, artificial bee colony (ABC) algorithm, and the grasshopper optimization algorithm with invasive weed optimization (IWGOA). The testing results showed that the modified algorithms can effectively improve the solution accuracies and convergence properties, exhibited an excellent ability of global optimization in high-dimensional space and complex function problems.

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“…Refs. [30,31] examined power system expansion and total investment and expansion costs, which were handled using a novel heuristic, namely, the shuffled frog-leaping algorithm (SFLA). Authors of [32] introduced the MO oppositionbased chaotic differential evolution (MOCDE) method for tackling the MO issue through power loss reduction, annual energy loss, and voltage drift as multi-objectives.…”

Section: Introductionmentioning

confidence: 99%

Single- and Multi-Objective Modified Aquila Optimizer for Optimal Multiple Renewable Energy Resources in Distribution Network

et al. 2022

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Nowadays, the electrical power system has become a more complex, interconnected network that is expanding every day. Hence, the power system faces many problems such as increasing power losses, voltage deviation, line overloads, etc. The optimization of real and reactive power due to the installation of energy resources at appropriate buses can minimize the losses and improve the voltage profile, especially for congested networks. As a result, the optimal distributed generation allocation (ODGA) problem is considered a more proper tool for the processes of planning and operation of power systems due to the power grid changes expeditiously based on the type and penetration level of renewable energy sources (RESs). This paper modifies the AO using a quasi-oppositional-based learning operator to address this problem and reduce the burden on the primary grid, making the grid more resilient. To demonstrate the effectiveness of the MAO, the authors first test the algorithm performance on twenty-three competitions on evolutionary computation benchmark functions, considering different dimensions. In addition, the modified Aquila optimizer (MAO) is applied to tackle the optimal distributed generation allocation (ODGA) problem. The proposed ODGA methodology presented in this paper has a multi-objective function that comprises decreasing power loss and total voltage deviation in a distribution system while keeping the system operating and security restrictions in mind. Many publications investigated the effect of expanding the number of DGs, whereas others found out the influence of DG types. Here, this paper examines the effects of different types and capacities of DG units at the same time. The proposed approach is tested on the IEEE 33-bus in different cases with several multiple DG types, including multi-objectives. The obtained simulation results are compared to the Aquila optimizer, particle swarm optimization algorithm, and trader-inspired algorithm. According to the comparison, the suggested approach provides a superior solution for the ODGA problem with faster convergence in the DNs.

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An Evolutionary Frog Leaping Algorithm for Global Optimization Problems and Applications

Cited by 6 publications

References 71 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

A modified shuffled frog leaping algorithm with inertia weight

Single- and Multi-Objective Modified Aquila Optimizer for Optimal Multiple Renewable Energy Resources in Distribution Network

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