A New Improved Model of Marine Predator Algorithm for Optimization Problems

Ramezani, Maryam; Bahmanyar, Danial; Razmjooy, Navid

doi:10.1007/s13369-021-05688-3

Cited by 86 publications

(34 citation statements)

References 28 publications

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“…Chaos theory is widely used in swarm intelligence algorithms due to its randomness and non-repetition. Compared with random search, chaos theory can make full use of the search space, so it is often used to enhance the diversity of the initial population and improve the algorithm to achieve optimized performance [ 17 , 18 , 19 , 20 ]. Circle Map maps the variables to the value interval of the chaotic variable space and finally transforms the solution linearly into the optimization variable space.…”

Section: Chaos Mutation Adaptive Sparrow Search Algorithmmentioning

confidence: 99%

Application of Chaos Mutation Adaptive Sparrow Search Algorithm in Edge Data Compression

Qiu¹,

Li²

2022

Sensors

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In view of the large amount of data collected by an edge server, when compression technology is used for data compression, data classification accuracy is reduced and data loss is large. This paper proposes a data compression algorithm based on the chaotic mutation adaptive sparrow search algorithm (CMASSA). Constructing a new fitness function, CMASSA optimizes the hyperparameters of the Convolutional Auto-Encoder Network (CAEN) on the cloud service center, aiming to obtain the optimal CAEN model. The model is sent to the edge server to compress the data at the lower level of edge computing. The effectiveness of CMASSA performance is tested on ten high-dimensional benchmark functions, and the results show that CMASSA outperforms other comparison algorithms. Subsequently, experiments are compared with other literature on the Multi-class Weather Dataset (MWD). Experiments show that under the premise of ensuring a certain compression ratio, the proposed algorithm not only has better accuracy in classification tasks than other algorithms but also maintains a high degree of data reconstruction.

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Section: Chaos Mutation Adaptive Sparrow Search Algorithmmentioning

confidence: 99%

Application of Chaos Mutation Adaptive Sparrow Search Algorithm in Edge Data Compression

Qiu¹,

Li²

2022

Sensors

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“…Meta-heuristic algorithms, typically inspired by nature and physical manners that are presently utilized as a powerful approach for solving some complicated optimization problems. 20,21 The main purposes of metaheuristics are explained in the following. Metaheuristics are approaches which guide the search process.…”

Section: Fractional Bat Optimization Algorithmmentioning

confidence: 99%

Compressive strength prediction of structural lightweight concrete by applying the recurrent neural network‐fractional order bat optimization algorithm

Khajehzadeh¹

2022

Concurrency and Computation

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Neural networks methods produce an analysis in various fields. The Neural Network is frequently employed for developing statistical methods for nonlinear systems since, the Neural Networks use the simulation benefits of complicated behavior in several problems. In this study, the Recurrent Neural Network and Recurrent Neural Network based on Fractional Order Bat Optimization Algorithm is utilized for predicting the compressive strength of Lightweight Concrete mixes in 3, 7, 14, and 28 days curing. Recurrent Neural Network has been used for having a comparison. The inputs to the network for the compressive strength are made of eight variables: sand, lightweight fine aggregate, water to cement ratio, silica fume utilized in solution, Lightweight course aggregate, silica fume utilized supplementary to cement, curing period, and superplasticizer. According to the results, the Recurrent Neural Network based on Fractional Order Bat Optimization Algorithm has predicted precise outcomes and trained so fast in comparison with RNN method.by considering the outcomes, the Neural Networks methods are satisfactory mechanisms to evaluate the LWC compressive strength. This can decrease the price and reduce the time of processes in these problems. K E Y W O R D S compressive strength of structural lightweight concrete, fractional order bat optimization algorithm, neural networks methods, recurrent neural network, RNN-FOBOA INTRODUCTIONConcrete is the most practical and widely utilized material in the world. The high-strength Lightweight concrete generation began in the 1970s, and in the 1980s, due to the need of some oil companies in the United States and Norway to build offshore structures and reservoirs such as oil rigs, a wide range of research began to build high-strength Lightweight concrete in these two countries. The results were published in the late 1980s and early 1990s. The largest Lightweight concrete building is a 52-story office building in Texas with a height of 215 m. In recent years, the Lightweight concrete use in the roof slabs of high-rise buildings, bridge decks and other similar items, as well as special applications such as decks and bases of oil rigs have been widely used. 1 The use of Lightweight materials has been considered for a long time and in the modern construction industry, due to the need to build structures with high height and large dimensions, the discussion of lightening has been the focus of more attention. Lightweight concrete with its advantages such as availability in most areas and also being economical can be one of the options in the discussion of designing Lightweight structures. The scope of application of this type of material can also be as a member of instruments

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“…The marine predator algorithm (MPA) [ 27 ] has recently been proposed for solving optimization problems of various kinds. The MPA optimization method has been applied to solving some engineering problems such as fault diagnosis of rolling bearing [ 28 ] and the PID tuning problem for DC motor speed control [ 29 ], but it is yet to be adopted in solving antenna synthesis problems. Therefore, this work introduces the MPA to the electromagnetic world both in its standard form and also in its improved form.…”

Section: Introductionmentioning

confidence: 99%

Advanced Marine Predator Algorithm for Circular Antenna Array Pattern Synthesis

Owoola

Xia

Ogunjo

et al. 2022

Sensors

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The pattern synthesis of antenna arrays is a substantial factor that can enhance the effectiveness and validity of a wireless communication system. This work proposes an advanced marine predator algorithm (AMPA) to synthesize the beam patterns of a non-uniform circular antenna array (CAA). The AMPA utilizes an adaptive velocity update mechanism with a chaotic sequence parameter to improve the exploration and exploitation capability of the algorithm. The MPA structure is simplified and upgraded to overcome being stuck in the local optimum. The AMPA is employed for the joint optimization of amplitude current and inter-element spacing to suppress the peak sidelobe level (SLL) of 8-element, 10-element, 12-element, and 18-element CAAs, taking into consideration the mutual coupling effects. The results show that it attains better performances in relation to SLL suppression and convergence rate, in comparison with some other algorithms for the optimization case.

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A New Improved Model of Marine Predator Algorithm for Optimization Problems

Cited by 86 publications

References 28 publications

Application of Chaos Mutation Adaptive Sparrow Search Algorithm in Edge Data Compression

Application of Chaos Mutation Adaptive Sparrow Search Algorithm in Edge Data Compression

Compressive strength prediction of structural lightweight concrete by applying the recurrent neural network‐fractional order bat optimization algorithm

Advanced Marine Predator Algorithm for Circular Antenna Array Pattern Synthesis

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