Relevant bibliographies by topics / Nature inspired algorithms metaheuristics

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Nature inspired algorithms metaheuristics'

Author: Grafiati
Published: 5 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Nature inspired algorithms metaheuristics"

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Rahman, Md Ashikur, Rajalingam Sokkalingam, Mahmod Othman, Kallol Biswas, Lazim Abdullah, and Evizal Abdul Kadir. "Nature-Inspired Metaheuristic Techniques for Combinatorial Optimization Problems: Overview and Recent Advances." Mathematics 9, no. 20 (2021): 2633. http://dx.doi.org/10.3390/math9202633.

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Combinatorial optimization problems are often considered NP-hard problems in the field of decision science and the industrial revolution. As a successful transformation to tackle complex dimensional problems, metaheuristic algorithms have been implemented in a wide area of combinatorial optimization problems. Metaheuristic algorithms have been evolved and modified with respect to the problem nature since it was recommended for the first time. As there is a growing interest in incorporating necessary methods to develop metaheuristics, there is a need to rediscover the recent advancement of meta
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Rajendran, Shankar, Ganesh N., Robert Čep, Narayanan R. C., Subham Pal, and Kanak Kalita. "A Conceptual Comparison of Six Nature-Inspired Metaheuristic Algorithms in Process Optimization." Processes 10, no. 2 (2022): 197. http://dx.doi.org/10.3390/pr10020197.

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In recent years, several high-performance nature-inspired metaheuristic algorithms have been proposed. It is important to study and compare the convergence, computational burden and statistical significance of these metaheuristics to aid future developments. This study focuses on six recent metaheuristics, namely, ant lion optimization (ALO), arithmetic optimization algorithm (AOA), dragonfly algorithm (DA), grey wolf optimizer (GWO), salp swarm algorithm (SSA) and whale optimization algorithm (WOA). Optimization of an industrial machining application is tackled in this paper. The optimal mach
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Ouadfel, Salima, and Souham Meshoul. "Nature-Inspired Metaheuristics for Automatic Multilevel Image Thresholding." International Journal of Applied Metaheuristic Computing 5, no. 4 (2014): 47–69. http://dx.doi.org/10.4018/ijamc.2014100103.

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Thresholding is one of the most used methods of image segmentation. It aims to identify the different regions in an image according to a number of thresholds in order to discriminate objects in a scene from background as well to distinguish objects from each other. A great number of thresholding methods have been proposed in the literature; however, most of them require the number of thresholds to be specified in advance. In this paper, three nature-inspired metaheuristics namely Artificial Bee Colony, Cuckoo Search and Bat algorithms have been adapted for the automatic multilevel thresholding
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Almufti, Saman. "Vibrating Particles System Algorithm: Overview, Modifications and Applications." ICONTECH INTERNATIONAL JOURNAL 6, no. 3 (2022): 1–11. http://dx.doi.org/10.46291/icontechvol6iss3pp1-11.

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Real-world problems are difficult enough to encourages academics to develop innovative, effective problem-solving methods. Generally, metaheuristics algorithms that are inspired by nature, biology, and physics have a flexibility and capacity to adapt to different situations, metaheuristics based on evolutionary computation algorithms have been widely employed to solve complicated, constrained/unconstrained, single/multiple objective, and Non-deterministic polynomial hard (NP-Hard) optimization problems.
 This paper describes Vibrating Particles System (VPS) as a Physics Based metaheuristi
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Bacanin, Nebojsa, and Milan Tuba. "Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint." Scientific World Journal 2014 (2014): 1–16. http://dx.doi.org/10.1155/2014/721521.

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Portfolio optimization (selection) problem is an important and hard optimization problem that, with the addition of necessary realistic constraints, becomes computationally intractable. Nature-inspired metaheuristics are appropriate for solving such problems; however, literature review shows that there are very few applications of nature-inspired metaheuristics to portfolio optimization problem. This is especially true for swarm intelligence algorithms which represent the newer branch of nature-inspired algorithms. No application of any swarm intelligence metaheuristics to cardinality constrai
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Li, Hongwei, Yuvraj Gajpal, Chirag Surti, Dongliang Cai, and Amit Kumar Bhardwaj. "Nature-Inspired Metaheuristics for Two-Agent Scheduling with Due Date and Release Time." Complexity 2020 (December 29, 2020): 1–13. http://dx.doi.org/10.1155/2020/1385049.

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This paper delves into a two-agent scheduling problem in which two agents are competing for a single resource. Each agent has a set of jobs to be processed by a single machine. The processing time, release time, weight, and the due dates of each job are known in advance. Both agents have their objectives, which are conflicting in nature. The first agent tries to minimize the total completion time, while the second agent tries to minimize the number of tardy jobs. The two agents’ scheduling problem, an NP-hard problem, has a wide variety of applications ranging from the manufacturing industry t
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Kaleche, Rachid, Zakaria Bendaoud, and Karim Bouamrane. "Bio-Inspired Metaheuristics." International Journal of Organizational and Collective Intelligence 10, no. 4 (2020): 1–18. http://dx.doi.org/10.4018/ijoci.2020100101.

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In real life, problems becoming more complicated, among them NP-Hard problems. To resolve them, two families of methods exist, exact and approximate methods. When exact methods provide the optimal solution in an unacceptable amount of time, the approximate ones provide good solutions in a reasonable amount of time. Approximate methods are two kinds, heuristics and metaheuristics. The first ones are problem specific, while metaheuristics are independent from problems. A broad number of metaheuristics are inspired from nature, specially from biology. These bio-inspired metaheuristics are easy to
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Cirello, Antonino, Tommaso Ingrassia, Antonio Mancuso, Giuseppe Marannano, Agostino Igor Mirulla, and Vito Ricotta. "Evaluating the Efficiency of Nature-Inspired Algorithms for Finite Element Optimization in the ANSYS Environment." Applied Sciences 15, no. 12 (2025): 6750. https://doi.org/10.3390/app15126750.

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Nature-inspired metaheuristics have proven effective for addressing complex structural optimization challenges where traditional deterministic or gradient-based methods often fall short. This study investigates the feasibility and benefits of embedding three prominent metaheuristic algorithms, the Genetic Algorithm (GA), the Firefly Algorithm (FA), and the Group Search Optimizer (GSO) embedded into the ANSYS Parametric Design Language (APDL). The performance of each optimizer was assessed in three case studies. The first two are spatial truss structures, one comprising 22 bars and the other 25
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Roeva, Olympia, Dafina Zoteva, and Velislava Lyubenova. "Escherichia coli Cultivation Process Modelling Using ABC-GA Hybrid Algorithm." Processes 9, no. 8 (2021): 1418. http://dx.doi.org/10.3390/pr9081418.

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In this paper, the artificial bee colony (ABC) algorithm is hybridized with the genetic algorithm (GA) for a model parameter identification problem. When dealing with real-world and large-scale problems, it becomes evident that concentrating on a sole metaheuristic algorithm is somewhat restrictive. A skilled combination between metaheuristics or other optimization techniques, a so-called hybrid metaheuristic, can provide more efficient behavior and greater flexibility. Hybrid metaheuristics combine the advantages of one algorithm with the strengths of another. ABC, based on the foraging behav
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Gulić, Marko, Livia Maglić, and Sanjin Valčić. "Nature Inspired Metaheuristics for Optimizing Problems at a Container Terminal." Pomorstvo 32, no. 1 (2018): 10–20. http://dx.doi.org/10.31217/p.32.1.16.

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Nowadays, maritime transport is the backbone of the international trade of goods. Therefore, seaports play a very important role in global transport. The use of containers is significantly represented in the maritime transport. Considering the increased number of container shipments in the global transport, seaport container terminals have to be adapted to a new situation and provide the best possible service of container transfer by reducing the transfer cost and the container transit time. Therefore, there is a need for optimization of the whole container transport process within the termina
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Dissertations / Theses on the topic "Nature inspired algorithms metaheuristics"

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Alam, Intekhab Asim. "Real time tracking using nature-inspired algorithms." Thesis, University of Birmingham, 2018. http://etheses.bham.ac.uk//id/eprint/8253/.

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This thesis investigates the core difficulties in the tracking field of computer vision. The aim is to develop a suitable tuning free optimisation strategy so that a real time tracking could be achieved. The population and multi-solution based approaches have been applied first to analyse the convergence behaviours in the evolutionary test cases. The aim is to identify the core misconceptions in the manner the search characteristics of particles are defined in the literature. A general perception in the scientific community is that the particle based methods are not suitable for the real time
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Crossley, Matthew James. "Fitness landscape-based analysis of nature-inspired algorithms." Thesis, Manchester Metropolitan University, 2014. http://e-space.mmu.ac.uk/47/.

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As the number of nature-inspired algorithms increases so does the need to characterise these algorithms. A rigorous process to characterise algorithms helps practitioners decide which algorithms may offer a good fit for their given problem. One approach is to relate the characteristics of a problem's associated fitness landscape with the performance of an algorithm. The aim of this thesis is to capitalise on the notion of fitness landscape characteristics as a technique for analysing algorithm performance, and to provide a novel algorithm- and problem-independent methodology that can be used t
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Julai, Sabariah. "Nature-inspired algorithms for vibration control of flexible plate structures." Thesis, University of Sheffield, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.531231.

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Liu, Fang. "Nature inspired computational intelligence for financial contagion modelling." Thesis, Brunel University, 2014. http://bura.brunel.ac.uk/handle/2438/8208.

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Financial contagion refers to a scenario in which small shocks, which initially affect only a few financial institutions or a particular region of the economy, spread to the rest of the financial sector and other countries whose economies were previously healthy. This resembles the “transmission” of a medical disease. Financial contagion happens both at domestic level and international level. At domestic level, usually the failure of a domestic bank or financial intermediary triggers transmission by defaulting on inter-bank liabilities, selling assets in a fire sale, and undermining confidence
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Arcanjo, Diego Nascimento. "Metodologia multi-estágio para restabelecimento de sistemas elétricos de distribuição utilizando algoritmos bio-inspirados." Universidade Federal de Juiz de Fora, 2014. https://repositorio.ufjf.br/jspui/handle/ufjf/697.

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Submitted by Renata Lopes (renatasil82@gmail.com) on 2016-02-05T17:32:25Z No. of bitstreams: 1 diegonascimentoarcanjo.pdf: 1706072 bytes, checksum: 2329ddd810b5aca8da733c7793937d65 (MD5)<br>Approved for entry into archive by Adriana Oliveira (adriana.oliveira@ufjf.edu.br) on 2016-02-26T11:52:47Z (GMT) No. of bitstreams: 1 diegonascimentoarcanjo.pdf: 1706072 bytes, checksum: 2329ddd810b5aca8da733c7793937d65 (MD5)<br>Made available in DSpace on 2016-02-26T11:52:47Z (GMT). No. of bitstreams: 1 diegonascimentoarcanjo.pdf: 1706072 bytes, checksum: 2329ddd810b5aca8da733c7793937d65 (MD5) Previ
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Nakrani, Sunil. "Biomimetic and autonomic server ensemble orchestration." Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.534214.

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This thesis addresses orchestration of servers amongst multiple co-hosted internet services such as e-Banking, e-Auction and e-Retail in hosting centres. The hosting paradigm entails levying fees for hosting third party internet services on servers at guaranteed levels of service performance. The orchestration of server ensemble in hosting centres is considered in the context of maximising the hosting centre's revenue over a lengthy time horizon. The inspiration for the server orchestration approach proposed in this thesis is drawn from nature and generally classed as swarm intelligence, speci
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Santana, Laura Emmanuella Alves dos Santos. "Otimiza??o em comit?s de classificadores: uma abordagem baseada em filtro para sele??o de subconjuntos de atributos." Universidade Federal do Rio Grande do Norte, 2012. http://repositorio.ufrn.br:8080/jspui/handle/123456789/17946.

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Made available in DSpace on 2014-12-17T15:46:59Z (GMT). No. of bitstreams: 1 LauraEASS_TESE.pdf: 2447411 bytes, checksum: 3e442431965058383423623bc7751de0 (MD5) Previous issue date: 2012-02-02<br>Conselho Nacional de Desenvolvimento Cient?fico e Tecnol?gico<br>Traditional applications of feature selection in areas such as data mining, machine learning and pattern recognition aim to improve the accuracy and to reduce the computational cost of the model. It is done through the removal of redundant, irrelevant or noisy data, finding a representative subset of data that reduces its dimensionalit
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Neshat, Mehdi. "The Application of Nature-inspired Metaheuristic Methods for Optimising Renewable Energy Problems and the Design of Water Distribution Networks." Thesis, 2020. http://hdl.handle.net/2440/130439.

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This work explores the technical challenges that emerge when applying bio-inspired optimisation methods to real-world engineering problems. A number of new heuristic algorithms were proposed and tested to deal with these challenges. The work is divided into three main dimensions: i) One of the most significant industrial optimisation problems is optimising renewable energy systems. Ocean wave energy is a promising technology for helping to meet future growth in global energy demand. However, the current technologies of wave energy converters (WECs) are not fully developed because of technical
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Freitas, Diogo Nuno Teixeira. "Nature-inspired algorithms for solving some hard numerical problems." Master's thesis, 2020. http://hdl.handle.net/10400.13/3010.

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Optimisation is a branch of mathematics that was developed to find the optimal solutions, among all the possible ones, for a given problem. Applications of optimisation techniques are currently employed in engineering, computing, and industrial problems. Therefore, optimisation is a very active research area, leading to the publication of a large number of methods to solve specific problems to its optimality. This dissertation focuses on the adaptation of two nature inspired algorithms that, based on optimisation techniques, are able to compute approximations for zeros of polynomials and
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Miele, Raffaele. "Nature inspired Optimization Algorithms for Classification and Regression Trees." Tesi di dottorato, 2006. http://www.fedoa.unina.it/609/1/tesi_dottorato_Miele.pdf.

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During the past 10 years Computational Statistics techniques' importance has been always increasing and it is actually still doing. This is due to more than one factor. On one side, the technological growth of the last decades has made computational power and storage capacity incredibly affordable and accessible. In particular, the storage capacity has improved much more than the computational resources. This resulted in an unbalanced situation in which there are huge masses of data but there is not (and, probably, there won't be for a long time) enough computing power to exhaustively process
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Books on the topic "Nature inspired algorithms metaheuristics"

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Bansal, Sulabh, Aprna Tripathi, Shilpa Srivastava, and Prem Prakash Vuppuluri. Nature-inspired Metaheuristic Algorithms. CRC Press, 2025. https://doi.org/10.1201/9781003612858.

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Carbas, Serdar, Abdurrahim Toktas, and Deniz Ustun, eds. Nature-Inspired Metaheuristic Algorithms for Engineering Optimization Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6773-9.

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Okwu, Modestus O., and Lagouge K. Tartibu. Metaheuristic Optimization: Nature-Inspired Algorithms Swarm and Computational Intelligence, Theory and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-61111-8.

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Dorronsoro, Bernabé, Farouk Yalaoui, El-Ghazali Talbi, and Grégoire Danoy, eds. Metaheuristics and Nature Inspired Computing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94216-8.

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Dorronsoro, Bernabé, Rachid Ellaia, and El-Ghazali Talbi, eds. Metaheuristics and Nature Inspired Computing. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-69257-4.

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Bennis, Fouad, and Rajib Kumar Bhattacharjya, eds. Nature-Inspired Methods for Metaheuristics Optimization. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-26458-1.

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Chiong, Raymond, ed. Nature-Inspired Algorithms for Optimisation. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00267-0.

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Castillo, Oscar, and Patricia Melin, eds. Fuzzy Logic Augmentation of Nature-Inspired Optimization Metaheuristics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-10960-2.

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Jain, Shashank. Nature-Inspired Optimization Algorithms with Java. Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-7401-9.

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Yang, Xin-She, ed. Nature-Inspired Algorithms and Applied Optimization. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-67669-2.

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Book chapters on the topic "Nature inspired algorithms metaheuristics"

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Aupetit, Sébastien, and Mohamed Slimane. "Nature Inspires New Algorithms." In Metaheuristics. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45403-0_10.

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Rather, Ahmad Sajad, and Sujit Das. "Quantum computing-based metaheuristics for medical image segmentation." In Nature-inspired Metaheuristic Algorithms. CRC Press, 2025. https://doi.org/10.1201/9781003612858-8.

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Kushwah, Ashok, Amrit Tiwary, and Prem Prakash Vuppuluri. "Balancing optimization and emissions in heuristics and metaheuristics for hard combinatorial problems." In Nature-inspired Metaheuristic Algorithms. CRC Press, 2025. https://doi.org/10.1201/9781003612858-13.

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Iturra, Sergio, Carlos Contreras-Bolton, and Victor Parada. "Automatic Generation of Metaheuristic Algorithms." In Metaheuristics and Nature Inspired Computing. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94216-8_4.

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Bansal, Sulabh, and Amrit Kaur. "Quantum-inspired evolutionary algorithms." In Nature-inspired Metaheuristic Algorithms. CRC Press, 2025. https://doi.org/10.1201/9781003612858-2.

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Raj, Abhishek, Bobby Tyagi, Arpit Kapoor, et al. "Exploring additive manufacturing parameters for improved tensile strength and functional electrode fabrication." In Nature-inspired Metaheuristic Algorithms. CRC Press, 2025. https://doi.org/10.1201/9781003612858-10.

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Falae, Esther Adekemi. "Particle swarm optimization for protein structure prediction and refinement." In Nature-inspired Metaheuristic Algorithms. CRC Press, 2025. https://doi.org/10.1201/9781003612858-7.

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Kumari, Rashmi, Subhranil Das, Kishore Raghwendra Singh, Arnav Talaan, Tanzeel Ahmad, and Vishal Gola. "Harnessing metaheuristic algorithms for advanced optimization and design solutions in complex real-world applications." In Nature-inspired Metaheuristic Algorithms. CRC Press, 2025. https://doi.org/10.1201/9781003612858-3.

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Anand, Garima, Shilpa Srivastava, Anish Shandilya, Garima Singh, and Aprna Tripathi. "Particle swarm optimization-based support vector regression for predictions." In Nature-inspired Metaheuristic Algorithms. CRC Press, 2025. https://doi.org/10.1201/9781003612858-14.

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Agarwal, Parul, Shikha Mehta, Shubh Garg, Samarth Bhardwaj, and Arushi Sharma. "Hybrid metaheuristic approach for community detection." In Nature-inspired Metaheuristic Algorithms. CRC Press, 2025. https://doi.org/10.1201/9781003612858-9.

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Conference papers on the topic "Nature inspired algorithms metaheuristics"

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Dasgupta, Anubhab, Vedant Zope, and Asrul Harun Ismail. "Implementation of the Bees Algorithm for UAV Mission Plan." In 14th International Seminar on Industrial Engineering and Management. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-nad6qa.

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The increasing use of Unmanned Aerial Vehicles (UAVs) in various applications, such as military operations and civilian tasks, has created a demand for efficient algorithms to plan their mission. This study explores the use of the Bees Algorithm (BA), a nature-inspired optimisation method, to solve two main problems in UAV mission planning: the UAV collision avoidance problem (UCAP) and the Asymmetric TSP (ATSP). Results of comparison between the proposed algorithm and other metaheuristic algorithms indicate that BA can produce competitive solutions. However, the study also found that the BA h
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Dulf, Eva-H., Alexandru George Berciu, Lehel Dénes-Fazakas, and Levente Kovacs. "Nature Inspired Optimization Algorithms in Fractional Order Controller Design." In 2024 IEEE 28th International Conference on Intelligent Engineering Systems (INES). IEEE, 2024. http://dx.doi.org/10.1109/ines63318.2024.10629099.

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Tasnádi, Zoltán. "Detecting Triangle-Densest-K-Subgraphs with Simple Nature-Inspired Algorithms." In 2024 26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2024. https://doi.org/10.1109/synasc65383.2024.00073.

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Sunori, Sandeep Kumar, Shilpa Jain, Pradeep Juneja, and Amit Mittal. "Microstrip Patch Antenna Design Based on Nature Inspired Optimization Algorithms." In 2024 9th International Conference on Communication and Electronics Systems (ICCES). IEEE, 2024. https://doi.org/10.1109/icces63552.2024.10859951.

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Gwebu, Marcia, Peter Olukamni, and Nkateko Mabunda. "Application of Nature-inspired Algorithms for Optimising Photovoltaic System Energy Production." In 2025 33rd Southern African Universities Power Engineering Conference (SAUPEC). IEEE, 2025. https://doi.org/10.1109/saupec65723.2025.10944454.

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Jena, Alok Kumar, K. Murali Gopal, and Abinash Tripathy. "Feature Selection based Sentiment analysis using Combination of Nature Inspired Algorithms." In 2024 Global Conference on Communications and Information Technologies (GCCIT). IEEE, 2024. https://doi.org/10.1109/gccit63234.2024.10862816.

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Lones, Michael A. "Metaheuristics in nature-inspired algorithms." In GECCO '14: Genetic and Evolutionary Computation Conference. ACM, 2014. http://dx.doi.org/10.1145/2598394.2609841.

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Yokoyama, André, Antonio Mury, Mariza Ferro, and Bruno Schulze. "Evaluation of Nature Inspired Metaheuristics for Search and Reconnaissance Operations by Rotary-wing Aircrafts." In XI Brazilian e-Science Workshop. Sociedade Brasileira de Computação - SBC, 2020. http://dx.doi.org/10.5753/bresci.2017.9918.

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The main objective of this work is the evaluation of two nature inspired meta-heuristics, Genetic Algorithms and Ant Colony, for the development of an application that can generate optimized routes for aircraft, attending the requirements of the Brazilian Navy. This work presents the methods developed, complying with two main constraints: checkpoints mobility and limited aircraft autonomy. It also presents the results of tests performed with the methods developed and an evaluation of their performances.
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Tuličić, Domagoj, and Nikola Ivković. "The Concept of Cognition as Categorization in the Development of New Metaheuristics and Algorithms Inspired by Nature." In 2023 46th MIPRO ICT and Electronics Convention (MIPRO). IEEE, 2023. http://dx.doi.org/10.23919/mipro57284.2023.10159646.

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Poveda-Pulla, Danilo Fernando, Jefferson Vicente Dominguez-Paute, Luis Fernando Guerrero-Vasquez, Paul Andres Chasi-Pesantez, Jorge Osmani Ordonez-Ordonez, and Paul Esteban Vintimilla-Tapia. "Optimization designs in patch antennas using nature-inspired metaheuristic algorithms: A review." In 2018 IEEE Biennial Congress of Argentina (ARGENCON). IEEE, 2018. http://dx.doi.org/10.1109/argencon.2018.8646110.

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