Prey Predator Algorithm Research Articles

Nature-inspired optimization prey–predator algorithm for soil slope stability analysis with physically informed initial population generation

In soil slope stability, locating the critical slip surface with the minimum safety factor is a difficult and complicated optimization problem. Most methods fail when proper bounds are not applied to the decision variable. For the first time, the paper uses the prey–predator algorithm to analyze slopes stability with circular slip surfaces making use of meaningful rules, from an engineering point of view, to define these bounds. The Fellenius method based on limit equilibrium technique also serves as the constraint of the mathematical problem adopted to solve the task. A pseudo-analytical study is also carried out on four benchmark literature problems to test the performance and to certify the accuracy of the results obtained with the new application of the prey–predator algorithm. We show that the solutions obtained are accurate and should be assumed as a reference comparing to other methods.

A Discrete Prey–Predator Algorithm for Cloud Task Scheduling

Cloud computing is considered a key Internet technology. Cloud providers offer services through the Internet, such as infrastructure, platforms, and software. The scheduling process of cloud providers’ tasks concerns allocating clients’ tasks to providers’ resources. Several mechanisms have been developed for task scheduling in cloud computing. Still, these mechanisms need to be optimized for execution time and makespan. This paper presents a new task-scheduling mechanism based on Discrete Prey–Predator to optimize the task-scheduling process in the cloud environment. The proposed Discrete Prey–Predator mechanism assigns each scheduling solution survival values. The proposed mechanism denotes the prey’s maximum surviving value and the predator’s minimum surviving value. The proposed Discrete Prey–Predator mechanism aims to minimize the execution time of tasks in cloud computing. This paper makes a significant contribution to the field of cloud task scheduling by introducing a new mechanism based on the Discrete Prey–Predator algorithm. The Discrete Prey–Predator mechanism presents distinct advantages, including optimized task execution, as the mechanism is purpose-built to optimize task execution times in cloud computing, improving overall system efficiency and resource utilization. Moreover, the proposed mechanism introduces a survival-value-based approach, as the mechanism introduces a unique approach for assigning survival values to scheduling solutions, differentiating between the prey’s maximum surviving value and the predator’s minimum surviving value. This improvement enhances decision-making precision in task allocation. To evaluate the proposed mechanism, simulations using the CloudSim simulator were conducted. The experiment phase considered different scenarios for testing the proposed mechanism in different states. The simulation results revealed that the proposed Discrete Prey–Predator mechanism has shorter execution times than the firefly algorithm. The average of the five execution times of the Discrete Prey–Predator mechanism was 270.97 s, while the average of the five execution times of the firefly algorithm was 315.10 s.

A comparative analysis of metaheuristics applied to adaptive curriculum sequencing.

The effective adoption of online learning depends on user satisfaction as distance education approaches suffer from a lack of commitment that may lead to failures and dropouts. The adaptive learning literature argues that an alternative to achieve student satisfaction is to treat them individually, delivering the educational content in a personalized manner. In addition, the sequencing of this content—called Adaptive Curriculum Sequencing (ACS)—is important to avoid cognitive overload and disorientation. The search for an optimal sequence from ever-growing databases is an NP-Hard combinatorial optimization problem. Although some approaches have been proposed, it is challenging to assess their contributions due to the lack of benchmark data available. This paper presents a procedure to create synthetic dataset to evaluate ACS approaches and, as a concept proof, analyzes metaheuristics usually used in ACS approaches: Genetic Algorithm, Particle Swarm Optimization (PSO) and Prey–Predator Algorithm using student’s learning goals and their extrinsic and intrinsic information. We also propose an approach based on Differential Evolution (DE). The computational experiments include synthetic datasets with a varied amount of learning materials and real-world datasets for comparison. The results show that DE performed better than the other methods when less than 500 learning materials are used while PSO performed better for larger problems.

NURBS upper bound prey-predator scheme for collapse analysis of masonry vaults

We present here an improvement in the non-uniform rational Bezier spline (NURBS)-based kinematic limit analysis approach, which has proven to be particularly effective for masonry vaults, by adding an innovative mesh adaptation scheme. The procedure is based on the application of the kinematic theorem of limit analysis on a 3D model composed of NURBS rigid blocks. The definition of curved geometries through NURBS surfaces allows using mesh of few elements without modifications of the real geometry. An adjustment of the initial mesh is needed to minimize the kinematic load multiplier. Therefore, a Prey-Predator Algorithm (PPA), an innovative meta-heuristic algorithm based on the natural interaction between a predator and preys, is implemented as core of the mesh adaptation, allowing an efficient evaluation of load-bearing capacity and collapse behavior of masonry vaults. In this work, the standard PPA is further improved by introducing a variable population size, the so-called saw-tooth model, which allows reducing the computational effort without penalizing the evaluation of the objective function. Some numerical examples, which involve a masonry arch, a skew arch, and a horizontally loaded dome, are finally analyzed. For all the cases, a comparison between the proposed PPA and a traditional Genetic Algorithm (GA) is presented.

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

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.

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

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.

Efficient meta-heuristic mesh adaptation strategies for NURBS upper–bound limit analysis of curved three-dimensional masonry structures

We propose a new adaptive NURBS upper-bound limit analysis approach for the assessment of general three-dimensional curved masonry structures, based on different meta-heuristic mesh adaptation schemes. The method, which can easily be integrated within CAD modeling environments, allows to establish the actual failure mechanism and load bearing capacity of a masonry structure by iteratively adjusting a tentative pattern of yield lines, defined upon a suitable initial mesh of NURBS rigid elements, by means of a suitable meta-heuristic algorithm which searches for the minimum collapse load multiplier, thus enforcing the upper-bound theorem of limit analysis. In particular, we investigate and discuss the efficiency of several meta-heuristic algorithms in delivering the optimal solution: a specifically devised Prey Predator Algorithm (PPA) is compared with the Particle Swarm Optimization (PSO) Algorithm, the Firefly Algorithm (FA) and a suitable Genetic Algorithm (GA). Four masonry vaults have been chosen as case studies. In particular, the modified PPA proves to be the most efficient mesh-adjustment scheme for the proposed adaptive NURBS-based limit analysis procedure.

Solving the Adaptive Curriculum Sequencing Problem with Prey-Predator Algorithm

Adaptive curriculum sequencing (ACS) is still a challenge in the adaptive learning field. ACS is a NP-hard problem especially considering the several constraints of the student and the learning material when selecting a sequence from repositories where several sequences could be chosen. Therefore, this has stimulated several researchers to use evolutionary approaches in the search for satisfactory solutions. This work explores the use of an adaptation of the prey-predator algorithm for the ACS problem. Pedagogical experiments with a real student dataset and convergence experiments with a synthetic dataset have shown that the proposed solution is suitable for the problem, although it is a solution not yet explored in the adaptive learning literature.

NURBS-Based Upper Bound Limit Analysis of FRP Reinforced Masonry Vaults through an Efficient Mesh Adaptation Scheme

Masonry vaults represent one of the typical structural typologies in historical masonry buildings. The study of the ultimate behavior of masonry vaults, together with the need to design adequate retrofitting techniques, is of high relevance in the optics of the preservation of the cultural heritage. In this paper, a new approach for the limit analysis of masonry construction is applied to FRP reinforced masonry vaults. This approach relies on the representation of geometry through NURBS surfaces, upper bound formulation of limit analysis, idealization of the structure as an assembly of rigid bodies with dissipation allowed only along interfaces, and optimization by means of a mesh adaptation scheme. The presence of FRP strips can be taken into account in easy way, because they can be included simply by adding NURBS surfaces and assigning them an adequate delamination stress value. The efficient mesh adaptation is performed by means of a Prey Predator Algorithm, which has been proven to be very suited for these problems. The strength of the proposed method lies in an accurate estimation of load-bearing capacity and collapse mechanism obtained with a model which requires a very low computational effort.

Balancing the Degree of Exploration and Exploitation of Swarm Intelligence Using Parallel Computing

The two basic search behaviors used in metaheuristic algorithms in general and swarm intelligence in particular are exploration and exploitation. Exploration refers to searching the unexplored area of the feasible region while exploitation refers to the search of the neighborhood of a promising region. The success of these algorithms highly depends on how these two search behaviors are balanced. Increasing the number of initial solutions indeed increase the performance of the algorithm over the expense of the runtime. Different exploration and exploitation adjustments are used in different metaheuristic algorithms. To well balance the degree of exploration and exploitation, which ultimately leads to finding good solutions, needs a careful implementation and sufficient runtime. However, the runtime is another issue which has been a bottleneck for different application, especially for high dimensional, highly constrained and expensive objective function cases. Using parallel computing to overcome this limitation has been a central research issue in the study of metaheuristic algorithms. The three basic parallelization approaches used are the parallel move model which is a master slave model where solutions will be distributed to different processors for updating, parallel multi start model which uses several updating methods for the solutions in parallel and move acceleration method, which refers to a parallel implementation with parallelizing the objective function. There is no specific parallelization used towards balancing of exploration and exploitation is mentioned. Hence, in this paper a parallel implementation of swarm intelligence in general and two algorithms, namely particle swarm optimization and prey predator algorithm, in particular will be discussed. The parallelization proposed is aimed to merge the parallel move model and the parallel multi start model with the aim of adjusting the degree of exploration and exploitation. Experimental study based on ten benchmark problems from constrained as well as unconstrained problems will be used for simulation. It will be shown that the parallelization with specific distribution of exploration and exploitation runs better and efficiently compared to the serial and also the parallel version without exporation/exploitation adjustment.

Prey-predator algorithm for discrete problems: a case for examinationtimetabling problem

The prey-predator algorithm is a metaheuristic algorithm inspired by the interaction between a predator and its prey. Initial solutions are put into three categories: the better performing solution as the best prey, the worst performing solution as a predator, and the rest as ordinary prey. The best prey totally focuses on exploiting its neighborhood while the predator explores the search space searching for a promising region in the search space. The ordinary prey will be affected by these two extreme search behaviors of exploration and exploitation. The algorithm has been tested and found to be effective in solving different problems arising from different disciplines including engineering, tourism, and management. Originally, the algorithm was designed to deal with continuous problems. However, many problems arising from real aspects are not continuous. Hence, in this paper the prey-predator algorithm will be extended to suit discrete problems. Examination timetabling is used to test the approach. The simulation results with appropriate statistical analysis show that the approach is as good as the cumulative best performance of results recorded in the literature for the selected benchmark problems.

Optimization of Microchannel Heat Sinks Using Prey-Predator Algorithm and Artificial Neural Networks

A rectangular microchannel heat sink is modeled by employing thermal resistance and pressure drop networks. The available correlations for both thermal resistance and pressure drop are utilized in optimization. A multi-objective optimization technique, the prey–predator algorithm, is employed with the objective to find the optimal values for the heat sink performance parameters, i.e., thermal resistance and the pumping power of the heat sink. Additionally, a radial basis function neural network is used to investigate a relationship between these parameters. Full training based on the prey–predator algorithm with the sum of the squared error function is used to achieve the best performance of the model. The analysis of variance method is also employed to test the performance of this model. This study shows that the multi-objective function based on the prey–predator algorithm and the neural networks is suitable for finding the optimal values for the microchannel heat sink parameters. The minimum values of the multi-objective function are found to be “pumping power = 2.79344” and “total thermal resistance = 0.134133”.

Weight Minimization of a Speed Reducer Using Prey Predator Algorithm

This article describes how a speed reducer is a mechanical device which is very useful to reduce speed in different machineries. The use of gears to adjust a speed is an old practice, however the formulation of the problem as an optimization problem and solving it using different approaches begun in early 1970's. The problem is a constrained, nonlinear and nonconvex problem. That makes it challenging to handle it by deterministic approaches. Hence, different metaheuristic and hybrid methods have been proposed and used. Furthermore, due to the challenging behaviour of the problem, it has been used as a benchmark problem to test and compare new algorithms. In this article, a brief review of the problem, its application and the advances in solving the problem will be presented. A metaheuristic algorithm called prey predator algorithm will then be used to solve the problem. Prey-predator algorithm has been found to be effective with an easy way to control the exploration and exploitation search behaviour. Simulation-based comparison with previous results shows that indeed the algorithm produces a promising result. Hence, this study showed that applying prey predator algorithm for speed reducer production is a reasonable idea.

Prediction of thermal conductivities of polyacrylonitrile electrospun nanocomposite fibers using artificial neural network and prey predator algorithm

In this study, artificial neural networks were employed to predict thermal conductivity of polyacrylonitrile (PAN) electrospun nanocomposite fibers embedded with multiwalled carbon nanotubes (MWCNTs) and Nickel Zinc ferrites [(Ni0.6Zn0.4) Fe2O4]. The prey predator algorithm was used to train the neural networks to find the best models. This is the first paper on the application of multilayer perception neural network (MLPNN) with the prey predator algorithm for the prediction of thermal conductivity of PAN Electrospun nanocomposites. The method of nonlinear regression was used to minimize the error distribution between the experimental data and the predicted results. Both MWCNTs and Nickel Zinc ferrites were used in different weight proportions. The predicted ANN responses were analyzed statistically using z-test and error functions for both nanoinclusions. The predicted ANN responses for PAN Electrospun nanocomposite fibers were compared with the experimental data and were found in good agreement.

Comparative study of prey predator algorithm and firefly algorithm

AbstractMetaheuristic algorithms are found to be promising for difficult and high dimensional problems. Most of these algorithms are inspired by different natural phenomena. Currently, there are hundreds of these metaheuristic algorithms introduced and used. The introduction of new algorithm has been one of the issues researchers focused in the past fifteen years. However, there is a critic that some of the new algorithms are not in fact new in terms of their search behavior. Hence, a comparative study in between existing algorithms to highlight their differences and similarity needs to be studied. Apart from knowing the similarity and difference in search mechanisms of these algorithms it will also help to set criteria on when to use these algorithms. In this paper a comparative study of prey predator algorithm and firefly algorithm will be discussed. The discussion will also be supported by simulation results on selected twenty benchmark problems with different properties. A statistical analysis called ...

Prediction of thermal conductivity of polyvinylpyrrolidone (PVP) electrospun nanocomposite fibers using artificial neural network and prey-predator algorithm.

In this study, multilayer perception neural network (MLPNN) was employed to predict thermal conductivity of PVP electrospun nanocomposite fibers with multiwalled carbon nanotubes (MWCNTs) and Nickel Zinc ferrites [(Ni0.6Zn0.4) Fe2O4]. This is the second attempt on the application of MLPNN with prey predator algorithm for the prediction of thermal conductivity of PVP electrospun nanocomposite fibers. The prey predator algorithm was used to train the neural networks to find the best models. The best models have the minimal of sum squared error between the experimental testing data and the corresponding models results. The minimal error was found to be 0.0028 for MWCNTs model and 0.00199 for Ni-Zn ferrites model. The predicted artificial neural networks (ANNs) responses were analyzed statistically using z-test, correlation coefficient, and the error functions for both inclusions. The predicted ANN responses for PVP electrospun nanocomposite fibers were compared with the experimental data and were found in good agreement.

Prey predator hyperheuristic

Prey predator algorithm is a population based metaheuristic algorithm inspired by the interaction between a predator and its prey. In the algorithm, a solution with a better performance is called best prey and focuses totally on exploitation whereas the solution with least performance is called predator and focuses totally on exploration. The remaining solutions are called ordinary prey and either exploit promising regions by following better performing solutions or explore the solution space by randomly running away from the predator. Recently, it has been shown that by increasing the number of best prey or predator, it is possible to adjust the degree of exploitation and exploration. Even though, this tuning has the advantage of easily controlling these search behaviors, it is not an easy task. As any other metaheuristic algorithm, the performance of prey predator algorithm depends on the proper degree of exploration and exploitation of the decision space. In this paper, the concept of hyperheuristic is employed to balance the degree of exploration and exploitation of the algorithm. So that it learns and decides the best search behavior for the problem at hand in iterations. The ratio of the number of the best prey and the predators are used as low level heuristics. From the simulation results the balancing of the degree of exploration and exploitation by using hyperheuristic mechanism indeed improves the performance of the algorithm. Comparison with other algorithms shows the effectiveness of the proposed approach.

Solving Satisfiability Logic Programming Using Radial Basis Function Neural Networks

We proposed a new technique to solve QBF based on Radial basis function neural networks (RBFNNs) and Prey-Predator algorithm (PPA). Prey-Predator algorithm (PPA) is a neural learning algorithm used to determine the parameters of the networks. We built the neural networks to represent the logic programming in Conjunctive Normal Form (CNF), which has at most three variables in each clause (3-CNF). Then, these neural networks are developed to be recurrent neural networks to deal with universal variables in QBF problems. The neural networks models can be applied to solve a wide range of practical applications of Satisfiability logic programming, such as NP-complete decision problem, and computer network design.

Prey predator algorithm with adaptive step length

Prey predator algorithm is a swarm-based metaheuristic algorithm inspired by the interaction between a predator and its prey. The worst performing solution from the solution set is called a predator, the best preforming solution is called best prey and the rest are called ordinary prey. The predator focuses on exploration while the best prey totally focuses on exploitation. Parameter assignments, especially step length, plays an important role in rapid convergence of the solution to the optimal solution. If the step length is too short, the algorithm will take more time to converge whereas if it is too big, then the algorithm will oscillates by jumping over the solution, making it hard to obtain the desired quality of solution. In this paper, adaptive step length for prey predator algorithm will be used to produce a rapid convergence. The study is also supported by simulation results with appropriate statistical analysis.

Extended Prey-Predator Algorithm with a Group Hunting Scenario

Prey-predator algorithm (PPA) is a metaheuristic algorithm inspired by the interaction between a predator and its prey. In the algorithm, the worst performing solution, called the predator, works as an agent for exploration whereas the better performing solution, called the best prey, works as an agent for exploitation. In this paper, PPA is extended to a new version callednm-PPA by modifying the number of predators and also best preys. Innm-PPA, there will benbest preys andmpredators. Increasing the value ofnincreases the exploitation and increasing the value ofmincreases the exploration property of the algorithm. Hence, it is possible to adjust the degree of exploration and exploitation as needed by adjusting the values ofnandm. A guideline on setting parameter values will also be discussed along with a new way of measuring performance of an algorithm for multimodal problems. A simulation is also done to test the algorithm using well known eight benchmark problems of different properties and different dimensions ranging from two to twelve showing thatnm-PPA is found to be effective in achieving multiple solutions in multimodal problems and also has better ability to overcome being trapped in local optimal solutions.