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On the Convergence of prey predator algorithm

International Journal of Applied and Computational Mathematics 2024年第2期10卷 1-23页

作者： Tilahun, Surafel Luleseged Department of Mathematics HPC and Big Data Analytics Center of Excellence Addis Ababa Science and Technology University Addis Ababa 16417 Ethiopia

Unlike their success in solving complex and difficult problems, there is a lack of proper mathematical analysis and discussion of metaheuristic algorithms. Even though some researches are done on these aspects, the gap is still wide and different researchers outlined that more needs to be done to understand and analyse the search behaviour mathematically in order to move towards explainable AI. Convergence analysis is one of the issues researchers are trying to address. In this paper, a discussion on the convergence of prey predator algorithm will be presented. It is a swarm based algorithm which shows promising results. Furthermore, some initial researches show that it is a generalized algorithm where some well known algorithms fall as its special case. Hence, its analysis can be customized and used for other algorithms as well. Based on mathematical arguments, this paper discusses the convergence analysis of the algorithm to a near optimal solution with appropriate implementation, i.e. appropriate parameter setting. prey predator algorithm is shown to be an ϵ-convergent algorithm that means there is a non infinity iteration number to get at least epsilon less quality from an optimal solution. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2024.

关键词： Convergence Metaheuristic prey predator algorithm Swarm intelligence

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

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JOURNAL OF KING SAUD UNIVERSITY SCIENCE 2019年第4期31卷 618-627页

作者： Hamadneh, Nawaf N. Khan, Waseem S. Khan, Waqar A. Saudi Elect Univ Coll Sci & Theoret Studies Dept Basic Sci Riyadh 11673 Saudi Arabia Majmaah Univ Coll Engn Dept Mech & Ind Engn POB 66 Majmaah 11952 Saudi Arabia

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. (C) 2018 Production and hosting by Elsevier B.V. on behalf of King Saud University.

关键词： Thermal conductivity PAN Electrospun fibers Artificial neural network prey predator algorithm Carbon nanotubes

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prey predator algorithm with adaptive step length

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2016年第4期8卷 195-204页

作者： Tilahun, Surafel Luleseged Ngnotchouye, Jean Medard T. Univ KwaZulu Natal Sch Math Stat & Comp Sci Private Bag X01 ZA-3209 Pietermaritzburg South Africa

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.

关键词： prey predator algorithm PPA metaheuristic bio-inspired computing adaptive step length convergence group hunting optimisation multimodal problem exploration versus exploitation

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

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COMPUTERS & STRUCTURES 2020年 236卷 106271-106271页

作者： Grillanda, Nicola Chiozzi, Andrea Milani, Gabriele Tralli, Antonio Tech Univ Milan Dept Architecture Built Environm & Construct Engn Piazza Leonardo da Vinci 32 I-20133 Milan Italy Univ Ferrara Dept Engn Via Saragat 1 I-44122 Ferrara Italy

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. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Masonry Masonry vaults Limit analysis Meta-heuristic algorithms Genetic algorithm prey predator algorithm NURBS

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prey predator hyperheuristic

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APPLIED SOFT COMPUTING 2017年 59卷 104-114页

作者： Tilahun, Surafel Luleseged Univ Zululand Dept Math Sci Private Bag X1001 ZA-3886 Kwa Dlangezwa South Africa

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. (C) 2017 Elsevier B.V. All rights reserved.

关键词： prey predator algorithm prey predator hyperheuristic Hyperheuristic Exploration versus exploitation Swarm intelligence

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