Hybridizing particle swarm optimization with simulated annealing and differential evolution

Mirsadeghi, Emad; Khodayifar, Salman

doi:10.1007/s10586-020-03179-y

Cited by 12 publications

(7 citation statements)

References 55 publications

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“…Mirsadeghi et al [24] utilized the SOM to develop and control the decision system for township humanoids. The technology enables devices to obtain dynamic the SOM algorithm [24]. This procedure combines the speed combination and works assignment approach for the neighboring current's status of the sea.…”

Section: Underwater Networkmentioning

confidence: 99%

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Privacy Prevention and Nodes Optimization, Detection of IoUT based on Artificial Intelligence

Gaur

Prakash

2023

Preprint

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Transportation, temperature control, and the production of pharmaceuticals all rely on 71 percent of the Earth's surface, which is covered by water. Valuable things, including minerals, metals, corals, and coral reefs, can be identified with the help of the IoUT, among other applications. One of the crucial uses is in preventing damage caused by natural disasters. The IoT boosted concepts for a new undersea network (IoUT). Underwater networks have many drawbacks, including a lack of dependability, limited bandwidth, long propagation delays, high processing demands, high energy costs, and node detection with secure communication. Real-time, secure data communication is a significant area of research for us, along with node identification and dynamic network configuration. IoUT faces severe obstacles in the form of these issues. Our strategy involves a dynamic graph for network design, an AFA algorithm based on AI for node recognition, and ECC for a secure communication mechanism. In order to increase discretion and provide the undersea network with adequate robust security.

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Section: Underwater Networkmentioning

confidence: 99%

“…This technique signifies a system dynamically managing the group-based humanoids to numerous locations' movement. Mirsadeghi et al [24] utilized the SOM to develop and control the decision system for township humanoids. The technology enables devices to obtain dynamic the SOM algorithm [24].…”

Section: Underwater Networkmentioning

confidence: 99%

Privacy Prevention and Nodes Optimization, Detection of IoUT based on Artificial Intelligence

Gaur

Prakash

2023

Preprint

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“…It can be estimated by calculating the number of basic operations that require constant time. Its usual function form is Time n = f (n) , where n is the dimension of the problem, Time n is the execution time of the n-dimensional algorithm, f ( * ) is a function that returns the execution time (Mirsadeghi and Khodayifar 2021).…”

Section: The Basic Process Of Sfsadementioning

confidence: 99%

“…and so on. Most optimization algorithms, such as differential evolution, particle swarm optimization, simulated annealing, etc., have a time complexity ofo(p(n + CF)) , where p is the size of the population, n is the dimension of the problem, and CF is the Cost Function time complexity (Mirsadeghi and Khodayifar 2021). Therefore, as shown in the code in Algorithm 1, due to the additional nesting of loops outside, the time complexity of SFSADE iso(p(n 2 + CF)).…”

Section: The Basic Process Of Sfsadementioning

confidence: 99%

“…To further analyze the experimental results, we use the Wilcoxon signed ranks test and Friedman test, two nonparametric statistical tests, to evaluate the performance of the above algorithms (Mirsadeghi and Khodayifar 2021). Therefore, we consider to propose two different hypotheses and compare the proposed algorithm SFSADE with the other DE variants, namely JADE, SaDE, SOUPDE, EPSDE, ESADE, MPEDE, and SAMDE algorithms:…”

Section: Statistical Testmentioning

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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Path test data generation using adaptive simulated annealing particle swarm optimization

Jiao,

Zhou

2024

Soft Comput

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Hybridizing particle swarm optimization with simulated annealing and differential evolution

Cited by 12 publications

References 55 publications

Privacy Prevention and Nodes Optimization, Detection of IoUT based on Artificial Intelligence

Privacy Prevention and Nodes Optimization, Detection of IoUT based on Artificial Intelligence

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

Path test data generation using adaptive simulated annealing particle swarm optimization

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