Comparative study of prey predator algorithm and firefly algorithm

Ong, Hong Choon; Tilahun, Surafel Luleseged; Lee, Wai Soon; Ngnotchouye, Jean Meadard T.

doi:10.1080/10798587.2017.1294811

Cited by 9 publications

(10 citation statements)

References 18 publications

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“…The second issue related to updating the solution is the step length for exploration min and max ( min < max ). The procedure movement of the prey and the Predator can be summarized as follows [24,25,34].…”

Section: Structurementioning

confidence: 99%

Artificial Neural Networks for Prediction of Covid-19 in Saudi Arabia

Hamadneh¹,

Khan²,

Ashraf³

et al. 2021

Computers, Materials &Amp; Continua

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In this study, we have proposed an artificial neural network (ANN) model to estimate and forecast the number of confirmed and recovered cases of COVID-19 in the upcoming days until September 17, 2020. The proposed model is based on the existing data (training data) published in the Saudi Arabia Coronavirus disease (COVID-19) situation-Demographics. The Prey-Predator algorithm is employed for the training. Multilayer perceptron neural network (MLPNN) is used in this study. To improve the performance of MLPNN, we determined the parameters of MLPNN using the prey-predator algorithm (PPA). The proposed model is called the MLPNN-PPA. The performance of the proposed model has been analyzed by the root mean squared error (RMSE) function, and correlation coefficient (R). Furthermore, we tested the proposed model using other existing data recorded in Saudi Arabia (testing data). It is demonstrated that the MLPNN-PPA model has the highest performance in predicting the number of infected and recovering in Saudi Arabia. The results reveal that the number of infected persons will increase in the coming days and become a minimum of 9789. The number of recoveries will be 2000 to 4000 per day.

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Section: Structurementioning

confidence: 99%

Artificial Neural Networks for Prediction of Covid-19 in Saudi Arabia

Hamadneh¹,

Khan²,

Ashraf³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

“…It solves the issues of continuous optimization, combinatorial optimization, and constraint optimization. Moreover, it can deal with highly nonlinear, and multi-modal optimization problems naturally and efficiently [26,32,33]. MLPNN is an optimization problem, where we used PPA to determine the best MLPNN models by determining the optimal values of the models weights.…”

Section: Prey Predator Algorithm (Ppa)mentioning

confidence: 99%

Using Artificial Neural Network with Prey Predator Algorithm for Prediction of the COVID-19: The Case of Brazil and Mexico

2021

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The spread of the COVID-19 epidemic worldwide has led to investigations in various aspects, including the estimation of expected cases. As it helps in identifying the need to deal with cases caused by the pandemic. In this study, we have used artificial neural networks (ANNs) to predict the number of cases of COVID-19 in Brazil and Mexico in the upcoming days. Prey predator algorithm (PPA), as a type of metaheuristic algorithm, is used to train the models. The proposed ANN models’ performance has been analyzed by the root mean squared error (RMSE) function and correlation coefficient (R). It is demonstrated that the ANN models have the highest performance in predicting the number of infections (active cases), recoveries, and deaths in Brazil and Mexico. The simulation results of the ANN models show very well predicted values. Percentages of the ANN’s prediction errors with metaheuristic algorithms are significantly lower than traditional monolithic neural networks. The study shows the expected numbers of infections, recoveries, and deaths that Brazil and Mexico will reach daily at the beginning of 2021.

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“…the process of evolution and the intelligentsia shown by its various species starting from micro-organisms like bacteria to birds, honey bees, flies, fishes, frogs, monkeys, wolfs and many more [81]. The popular and widely applied metaheuristic algorithms include Genetic Algorithms (GAs) [33,73,75], Particle Swarm Optimization algorithm (PSO) [41,42,59,60,65], Artificial Bee Colony algorithm (ABC) [25], Cuckoo Search algorithm (CS) [15,19,26,35], Firefly Algorithm (FA) [11,20,32,37,46], Differential evolution (DE) algorithms [25,29,30,85], Ant colony optimization(ACO) [55] etc. Though more than 300 types of nature inspired metaheuristic algorithms are available in the literature [82], the most widely accepted and popular primary algorithms mainly include the Genetic algorithms(GAs), Differential evolution algorithms(DE), Artificial Bee colony algorithms(ABC), particle swarm optimization algorithms(PSO), fireflies algorithm(FA) and ant colony optimization algorithm(ACO).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In FA, every firefly gets attracted to another brighter firefly and if the firefly is unable to find any neighbourhood brighter one then it tries to find another firefly through a random walk [20][21], [47]. Here the DE algorithm [48] replaces the random walk feature used for exploration of the search space for the desired firefly.…”

Section: Proposed Hybrid Firefly-differential Evolution Algorithmmentioning

confidence: 99%

Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach

et al. 2020

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In model-based testing, the test suites are derived from design models of system specification documents instead of actual program codes to reduce cost and time of testing. In search-based software testing approach, the nature inspired meta-heuristic search algorithms are used for automating and optimizing the test suite generation process of software testing. This paper proposes a concrete model-based testing framework; using UML behavioral state chart model along with the hybrid version of the two most popular nature inspired algorithms, Firefly algorithm (FA) and Differential Algorithm (DE). The hybrid algorithm is adopted to generate optimized test suits for the benchmark triangle classification problem. Experimental results evidently show that the hybrid FADE search algorithm outperforms the individual model-based Firefly and Differential Evolution algorithm's performances in terms of time complexity, better exploration and exploitation as well as variations in test case generation process. The framework generates optimized test data for complete transition path coverage of the available feasible paths of the example problem.

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Comparative study of prey predator algorithm and firefly algorithm

Cited by 9 publications

References 18 publications

Artificial Neural Networks for Prediction of Covid-19 in Saudi Arabia

Artificial Neural Networks for Prediction of Covid-19 in Saudi Arabia

Using Artificial Neural Network with Prey Predator Algorithm for Prediction of the COVID-19: The Case of Brazil and Mexico

Test Suit Generation for Object Oriented Programs: A Hybrid Firefly and Differential Evolution Approach

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