Marine Predators Algorithm: A nature-inspired metaheuristic

Faramarzi, Afshin; Heidarinejad, Mohammad; Mirjalili, Seyedali; Gandomi, Amir H.

doi:10.1016/j.eswa.2020.113377

Cited by 1,637 publications

(951 citation statements)

References 64 publications

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“…The Marine Predators Algorithm (MPA)is a recently developed meta-heuristic algorithm that emulates the relation among the prey and predator in nature 37 . MPA simulates the main aim for most creatures that is searching for their foods, where a predator contiguously searches for food as well as the prey.…”

Section: Methodsmentioning

confidence: 99%

“…MPA simulates the main aim for most creatures that is searching for their foods, where a predator contiguously searches for food as well as the prey. Inspired by this concept, Faramarzi et al 37 developed the MPA algorithm by considering both of a predator a prey as solutions. The MPA starts with the initialization phase and then passing by other three phases with respect to the rational velocity among the prey and the predator.…”

Section: Methodsmentioning

confidence: 99%

“…Stage 2 has been executed in the second third of the total number of iterations when . Faramarzi et al 37 divided the agents for two halves and formulated Eqs. ( 14 )–( 15 ) to emulate the motion of the first half of the population (prey) and Eqs.…”

Section: Methodsmentioning

confidence: 99%

“… where CF is the parameter that controls the step size of movement for the predator. Stage 3: This stage executed on the last third of the iteration numbers ( ) where based on the following formula: Eddy formation and Fish Aggregating Devices’ effect: Faramarzi et al 37 considered the external impacts from the environment, such as the eddy formation or Fish Aggregating Devices (FADs) effects to avoid the local optimum solutions. This stage can be mathematically implemented as below: In Eq.…”

Section: Methodsmentioning

confidence: 99%

“…Then, using an enhanced version of Marine Predators Algorithm to select only relevant features. In general, MPA is a meta-heuristic technique that simulates the behavior of the prey and predator in nature 37 . This algorithm is tested over a global optimization problem.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

COVID-19 image classification using deep features and fractional-order marine predators algorithm

Sahlol

Yousri

Ewees

et al. 2020

Sci Rep

211

141

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Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-19, which causes dangerous symptoms to humans and animals, its complications may lead to death. Although convolutional neural networks (CNNs) is considered the current state-of-the-art image classification technique, it needs massive computational cost for deployment and training. In this paper, we propose an improved hybrid classification approach for COVID-19 images by combining the strengths of CNNs (using a powerful architecture called Inception) to extract features and a swarm-based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. A combination of fractional-order and marine predators algorithm (FO-MPA) is considered an integration among a robust tool in mathematics named fractional-order calculus (FO). The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. By achieving 98.7%, 98.2% and 99.6%, 99% of classification accuracy and F-Score for dataset 1 and dataset 2, respectively, the proposed approach outperforms several CNNs and all recent works on COVID-19 images.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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COVID-19 image classification using deep features and fractional-order marine predators algorithm

Sahlol

Yousri

Ewees

et al. 2020

Sci Rep

211

141

View full text Add to dashboard Cite

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A new configuration of autonomous CHP system based on improved version of marine predators algorithm: A case study

Wang

Wang²,

Zhang³

et al. 2021

Int Trans Electr Energ Syst

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Summary The present paper describes a new optimal design for an autonomous renewable energy‐based CHP system for a remote area in Zhidoi county, China. The configuration contains different parts of the electric heater (EH), photovoltaic‐thermal (PV/T), wind turbines (WTs), thermal energy storage (TES), and electrical energy storage (EES). The total annual cost (TAC) is utilized as a cost function of the system configuration and the idea is to minimize this function to access an optimal configuration. Due to the complicated nonlinear nature of this system, a metaheuristic‐based method, called Improved Marine Predators Algorithm (IMPA) has been introduced and designed. The reason for using this new algorithm is to cover the main drawbacks of most metaheuristics like better accuracy and higher convergence speed. To show the capability of the designed IMPA, it is validated by some different new metaheuristics from the literature. Afterward, the algorithm is used for system configuration optimization. Some sensitivity analysis is also investigated to shoe the method capability and the final results confirm the high ability of the proposed method for providing an optimal renewable energy‐based CHP system. The final results show a $56 307.74 value of TAC. The total efficiency of the system for the winter and the summers are 66% and 61%, respectively and the minimum total annual cost happens at AD = 0.8983. Finally, the minimum value of the total annual cost is achieved by the proposed method with 922.4 kWh.

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S2NA‐GEO method–based charging strategy of electric vehicles to mitigate the volatility of renewable energy sources

Ilango¹,

Rajesh

Shajin

2021

Int Trans Electr Energ Syst

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In this manuscript, an efficient hybrid strategy is proposed to mitigate the negative impact of RES output fluctuations and smart charging method of electric vehicles (EVs). The proposed hybrid system is the joint implementation of Spike Neural Network Learning Algorithm (S2NA) algorithm and Golden Eagle Optimizer (GEO) algorithm; hence, it is known as S2NA-GEO strategy. An innovative uncertainty mode of renewable energy sources (RESs) based upon S2NA-GEO strategy is proposed, which can avert the difficult parameter collection and formula derivation. Price-based mode is accepted as EVs are considered responsive loads, which also take into account the spatial-temporal uncertainty of electric vehicles. Here, two fluctuation indexes are determined as quantitative evaluation of the volatility of RESs, then the charging cost is adopted as a guideline for the satisfaction of electric vehicle charging. The main purpose of the proposed system is diminishing

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Marine Predators Algorithm: A nature-inspired metaheuristic

Cited by 1,637 publications

References 64 publications

COVID-19 image classification using deep features and fractional-order marine predators algorithm

COVID-19 image classification using deep features and fractional-order marine predators algorithm

A new configuration of autonomous CHP system based on improved version of marine predators algorithm: A case study

S2NA‐GEO method–based charging strategy of electric vehicles to mitigate the volatility of renewable energy sources

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