Extended Prey-Predator Algorithm with a Group Hunting Scenario

Tilahun, Surafel Luleseged; Ong, Hong Choon; Ngnotchouye, Jean Medard T.

doi:10.1155/2016/7325263

Cited by 9 publications

(2 citation statements)

References 22 publications

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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%

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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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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“…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]. Adaptive step length incorporation is another idea to extend the algorithm to obtain a good approximate result.…”

Section: Introductionmentioning

confidence: 99%

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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Swarm hyperheuristic framework

Tilahun

Tawhid

2018

J Heuristics

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Swarm intelligence is one of the central focus areas in the study of metaheuristic algorithms. The effectiveness of these algorithms towards solving difficult problems has attracted researchers and practitioners. As a result, numerous type of this algorithm have been proposed. However, there is a heavy critics that some of these algorithms lack novelty. In fact, some of these algorithms are the same in terms of the updating operators but with different mimicking scenarios and names. The performance of a metaheuristic algorithm depends on how it balance the degree of the two basic search mechanisms, namely intensification and diversification. Hence, introducing novel algorithms which contributes to a new way of search mechanism is welcome but not for a mere repetition of the same algorithm with the same or perturbed operators but different metaphor. With this regard, it is ideal to have a framework where different custom made operators are used along with existing or new operators. Hence, this paper presents a swarm hyperheuristic framework, where updating operators are taken as low level heuristics and guided by a high level hyperheuristic. Different learning approaches are also proposed to guide the intensification and diversification search behaviour of the algorithm. Hence, a swarm hyperheuristic without learning (SHH1), with offline learning (SHH2) and with an online learning (SHH3) is proposed and discussed. A simulation based comparison and discussion is also presented using a set of nine updating operators with selected metaheuristic algorithms based on twenty benchmark problems. The problems are selected from both unconstrained and constrained optimization problems with their dimension ranging from two to fifty. The simulation results show that the proposed approach with learning has a better performance in general.

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Extended Prey-Predator Algorithm with a Group Hunting Scenario

Cited by 9 publications

References 22 publications

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

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

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

Swarm hyperheuristic framework

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