SFDE: Shuffled Frog-Leaping Differential Evolution and Its Application on Cognitive Radio Throughput

Wang, Hongbo; Zhen, Xiaoxiao; Tu, Xuyan

doi:10.1155/2019/2965061

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…In each local iterative calculation of the s th subpopulation, Fw s t is updated according to Eq. ( 14) (Wang et al 2019)…”

Section: Local Update Operationmentioning

confidence: 99%

“…Compared with the basic genetic algorithm, the DE algorithm and the genetic algorithm with an improved crossover operation can obtain a better global optimal solution. In SFDE, (Wang et al 2019) combined DE with the most basic shuffled frog-leaping algorithm, which improved the diversity of the population and the speed of global search during the entire dynamic evolution process. (Parouha and Verma 2021) designed an advanced hybrid algorithm (haDEPSO), which integrated with the advanced DE and PSO.…”

Section: Hybrid Strategymentioning

confidence: 99%

“…combines the enhanced DE based on the self-adaptive control parameter adjustment mechanism and classification mutation strategy with the meme group idea in shuffled frog leaping and achieves better results. Compared with the latest advanced DE variants, such as SAMDE(Zhu et al 2020) and SFDE(Wang et al 2019), SFSADE has great differences and performance improvements.…”

mentioning

confidence: 99%

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SFSADE: an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy

et al. 2021

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The differential evolution (DE) algorithm is an efficient random search algorithm based on swarm intelligence for solving optimization problems. It has the advantages of easy implementation, fast convergence, strong optimization ability and good robustness. However, the performance of DE is very sensitive to the design of different operators and the setting of control parameters. To solve these key problems, this paper proposes an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy (SFSADE). It innovatively incorporates the idea of the shuffled frog-leaping algorithm into DE, and at the same time, it cleverly introduces a new strategy of classification mutation, and also designs a new adaptive adjustment mechanism for control parameters. In addition, we have carried out a large number of simulation experiments on the 25 benchmark functions of CEC 2005 and two nonparametric statistical tests to comprehensively evaluate the performance of SFSADE. Finally, the results of simulation experiments and nonparametric statistical tests show that SFSADE is very effective in improving DE, and significantly improves the overall diversity of the population in the process of dynamic evolution. Compared with other advanced DE variants, its global search speed and optimization performance also has strong competitiveness.

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“…In each local iterative calculation of the s th subpopulation, Fw s t is updated according to Eq. ( 14) (Wang et al 2019)…”

Section: Local Update Operationmentioning

confidence: 99%

Section: Hybrid Strategymentioning

confidence: 99%

mentioning

confidence: 99%

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SFSADE: an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy

et al. 2021

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Quality of service optimization in orthogonal frequency division multiplexing‐based cognitive radio systems based on shuffled frog leaping algorithm

Benmammar

2021

Concurrency and Computation

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In this article, we propose to use shuffled frog leaping algorithm (SFLA) in order to provide the quality of service (QoS) requested by the secondary user (SU) in orthogonal frequency division multiplexing-based cognitive radio (CR) systems. The performance of the SFLA was evaluated through three transmission modes (emergency, multimedia, and low battery). SFLA provided the best possible configuration in a multicarrier context and thus proved its effectiveness to satisfy the needed QoS for the SU in CR systems. The obtained simulation results showed that SFLA offers better performance compared with genetic algorithms.

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

Adamuthe¹,

Pathan²

2021

Open Computer Science

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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.

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SFDE: Shuffled Frog-Leaping Differential Evolution and Its Application on Cognitive Radio Throughput

Cited by 3 publications

References 15 publications

SFSADE: an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy

SFSADE: an improved self-adaptive differential evolution algorithm with a shuffled frog-leaping strategy

Quality of service optimization in orthogonal frequency division multiplexing‐based cognitive radio systems based on shuffled frog leaping algorithm

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

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