Prey predator hyperheuristic

Tilahun, Surafel Luleseged

doi:10.1016/j.asoc.2017.04.044

Cited by 17 publications

(10 citation statements)

References 35 publications

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“…Note that the F-test formula (Equation (24)) is used to determine the p-values [55]. In Equation (24), the mean square (MS) represents the variation of variances between and within samples. For the single factor presented in Table 4, the p-value (0.0013) is greater than the critical value (0.001), and this indicates that the model adequately predicts the experimental results.…”

Section: Resultsmentioning

confidence: 99%

“…It has also been modified to suit combinatorial optimization problems, like the travel salesman problem, both the standard and clustered traveling salesman problems, and exam timetabling problem [23]. Furthermore, to boost its performance it has been extended to different versions [24]. In order to increase controlling the degree of exploration and exploitation a new version is introduced [25].…”

Section: Introductionmentioning

confidence: 99%

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

2018

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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".

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2018

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“…Adjusting the degree of exploration and exploitation is one of the challenging issues and has been in the forefront of research issues in the field [5]. The prey-predator algorithm gives a clear way of controlling exploration and exploitation [15,17]. It has also been studied and tested in different problems and is found to be effective [6-10, 14, 15, 18, 20, 21].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, it is a generalized swarm-based algorithm where some well-known algorithms occur as a special case of this algorithm, including the particle swarm algorithm and modified firefly algorithm [11]. Furthermore, the success of the algorithm motivates researchers to propose a generalized version of the algorithm by incorporating a hyperheuristic approach [17], which in turn leads researchers towards proposing a generalized swarm framework [19].…”

Section: Introductionmentioning

confidence: 99%

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

Tilahun¹

2019

Turk J Elec Eng & Comp Sci

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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.

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“…After the contrivance of theoretical ecology by Lotka [10] and Volterra [11], the models, named as Lotka-Volterra equations, played an important role in different fields of applied sciences [12][13][14][15]. The novelty in this attempt is the inclusion of Holling-type II function [16][17][18][19][20] in the system, which can effectively measure the functional response between the capital and the labour force. The existence of competitive interaction between these two factors produces a major change in the production process that yields a salient change in economic growth.…”

Section: Introductionmentioning

confidence: 99%

Economic Growth by Means of Interspecific Functional Response of Capital-Labour in Dynamical System

Razzaq

Khan

Ain

et al. 2019

ijaa

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Physical capital and labour force are two major factors of any economy, which play a key role in its growth. The association of these two components with each other is also a matter of study, which is carried out in this endeavour by means of an ecological system with Holling-type II function. The governing model is an avantgrade approach for economic theory as its equilibrium states and the stability analysis so obtained, referrer to different economic states with detail information about the capital-labour interaction. This novel assessment also contributes a significant way to scrutinize the capability of labours on consuming time on the capital and the efficiency of capital on processing output. Moreover, different patterns and cyclic behaviour of the Cobb-Douglas and constant-elasticity production functions, for different steady and oscillating states of the system are also provided comparatively. In addition, a numerical example is also discussed graphically with economic significance. These measurements will consequently keep the production cycle moving and so sustain the economic growth.

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Prey predator hyperheuristic

Cited by 17 publications

References 35 publications

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

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

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

Economic Growth by Means of Interspecific Functional Response of Capital-Labour in Dynamical System

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