Relevant bibliographies by topics / Nature-Inspired Algorithms

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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'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 June 2025

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Journal articles on the topic "Nature-Inspired Algorithms"

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Gautam, Raj Kumar. "Nature Inspired Metaheuristic based Optimization." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32390.

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This paper is a comprehensive study on nature inspired Hyperparameter optimization, with a distinct focus on Honey Badger Algorithm, along with Aquila Optimizer algorithm. The study involves in-depth analysis of the above algorithms, their weaknesses and strengths and comparing them with the theoretical advantages. The implementation of these algorithms, This paper demonstrate the promise of these algorithms on optimization of Hyperparameters like learning rate, number of hidden layers for our various datasets. The findings of this paper show that HBA and Aquila Optimization algorithms offer potential alternatives to the existing approaches, providing more effective and efficient solutions for hyperparameter optimization. This paper contributes to ongoing discourse on the place of nature inspired algorithms and their place in solutions to unconventional places
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Kumar, Deepak, Sushil Kumar, Rohit Bansal, and Parveen Singla. "A Survey to Nature Inspired Soft Computing." International Journal of Information System Modeling and Design 8, no. 2 (2017): 112–33. http://dx.doi.org/10.4018/ijismd.2017040107.

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This article describes how swarm intelligence (SI) and bio-inspired techniques shape in-vogue topics in the advancements of the latest algorithms. These algorithms can work on the basis of SI, using physical, chemical and biological frameworks. The authors can name these algorithms as SI-based, inspired by biology, physics and chemistry as per the basic concept behind the particular algorithm. A couple of calculations have ended up being exceptionally effective and consequently have turned out to be the mainstream devices for taking care of real-world issues. In this article, the reason for this survey is to show a moderately complete list of the considerable number of algorithms in order to boost research in these algorithms. This article discusses Ant Colony Optimization (ACO), the Cuckoo Search, the Firefly Algorithm, Particle Swarm Optimization and Genetic Algorithms in detail. For ACO a real-time problem, known as Travelling Salesman Problem, is considered while for other algorithms a min-sphere problem is considered, which is well known for comparison of swarm techniques.
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Jain, Vidhi. "Nature-inspired approaches in Software Fault Prediction." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34235.

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In software engineering, predicting software faults is a crucial task for ensuring high software quality and reducing costs. In recent years, nature inspired approaches have been increasingly used in software fault prediction. In this paper, we explore the effectiveness of six nature inspired algorithms, namely Ant Colony, Particle Swarm Optimization, Firefly, Bat, Harris Hawks, and Genetic Algorithm, for software fault prediction. We evaluate the algorithms using three commonly used datasets, JM1, CM1, and PC1. Our experimental results show that nature inspired approaches can effectively predict software faults, with some algorithms performing better than others depending on the dataset used. Our findings suggest that these approaches have potential to be used as a practical and efficient means for software fault prediction. Keywords— nature inspired algorithms; PSO; Ant Colony Optimization; Harris Hawks; Genetic Algorithm (GA); python programming; Jupyter Notebook; confusion matrix;
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Sukale, Sakshi, and Tanaji D. Biradar. "Review of Nature Inspired Algorithms." International Journal of Computer Applications 109, no. 3 (2015): 6–8. http://dx.doi.org/10.5120/19166-0625.

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POLAK, Iwona, and Mariusz BORYCZKA. "CRYPTANALYSIS USING NATURE-INSPIRED ALGORITHMS." National Security Studies 6, no. 2 (2014): 185–97. http://dx.doi.org/10.37055/sbn/135230.

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W dzisiejszych czasach ochrona informacji jest niezwykle istotna, a jednym z elementów zapewniających ową ochronę jest kryptografia. Tu z kolei ważną rolę odgrywa kryptoanaliza, która pozwala badać bezpieczeństwo używanych szyfrów. Oprócz typowo analitycznego podejścia do łamania szyfrów (jak kryptoanaliza różnicowa, kryptoanaliza liniowa czy analiza statystyczna) od kilkunastu lat do tego celu zaprzęga się różnego rodzaju niedeterministyczne systemy inspirowane naturą. Użycie takich technik nie jest do końca intuicyjne – w kryptoanalizie często ważne jest znalezienie jednego konkretnego klucza (rozwiązania optymalnego), a każde inne rozwiązanie daje kiepskie rezultaty, nawet jeśli jest blisko optimum globalnego.
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Yadav, SanehLata, and Manu Phogat. "Study of Nature Inspired Algorithms." International Journal of Computer Trends and Technology 49, no. 2 (2017): 100–105. http://dx.doi.org/10.14445/22312803/ijctt-v49p115.

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Bujok, Petr, Josef Tvrdik, and Radka Polakova. "Nature-Inspired Algorithms in Real-World Optimization Problems." MENDEL 23, no. 1 (2017): 7–14. http://dx.doi.org/10.13164/mendel.2017.1.007.

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Eight popular nature inspired algorithms are compared with the blind random search and three advanced adaptive variants of differential evolution (DE) on real-world problems benchmark collected for CEC 2011 algorithms competition. The results show the good performance of the adaptive DE variants and their superiority over the other algorithms in the test problems. Some of the nature-inspired algorithms perform even worse that the blind random search in some problems. This is a strong argument for recommendation for application, where well-verified algorithm successful in competitions should be preferred instead of developing some new algorithms.
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Yazdani, Maziar, and Fariborz Jolai. "Lion Optimization Algorithm (LOA): A nature-inspired metaheuristic algorithm." Journal of Computational Design and Engineering 3, no. 1 (2015): 24–36. http://dx.doi.org/10.1016/j.jcde.2015.06.003.

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Abstract During the past decade, solving complex optimization problems with metaheuristic algorithms has received considerable attention among practitioners and researchers. Hence, many metaheuristic algorithms have been developed over the last years. Many of these algorithms are inspired by various phenomena of nature. In this paper, a new population based algorithm, the Lion Optimization Algorithm (LOA), is introduced. Special lifestyle of lions and their cooperation characteristics has been the basic motivation for development of this optimization algorithm. Some benchmark problems are selected from the literature, and the solution of the proposed algorithm has been compared with those of some well-known and newest meta-heuristics for these problems. The obtained results confirm the high performance of the proposed algorithm in comparison to the other algorithms used in this paper.
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Abualigah, Laith, Amir H. Gandomi, Mohamed Abd Elaziz, et al. "Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis." Algorithms 13, no. 12 (2020): 345. http://dx.doi.org/10.3390/a13120345.

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Text clustering is one of the efficient unsupervised learning techniques used to partition a huge number of text documents into a subset of clusters. In which, each cluster contains similar documents and the clusters contain dissimilar text documents. Nature-inspired optimization algorithms have been successfully used to solve various optimization problems, including text document clustering problems. In this paper, a comprehensive review is presented to show the most related nature-inspired algorithms that have been used in solving the text clustering problem. Moreover, comprehensive experiments are conducted and analyzed to show the performance of the common well-know nature-inspired optimization algorithms in solving the text document clustering problems including Harmony Search (HS) Algorithm, Genetic Algorithm (GA), Particle Swarm Optimization (PSO) Algorithm, Ant Colony Optimization (ACO), Krill Herd Algorithm (KHA), Cuckoo Search (CS) Algorithm, Gray Wolf Optimizer (GWO), and Bat-inspired Algorithm (BA). Seven text benchmark datasets are used to validate the performance of the tested algorithms. The results showed that the performance of the well-known nurture-inspired optimization algorithms almost the same with slight differences. For improvement purposes, new modified versions of the tested algorithms can be proposed and tested to tackle the text clustering problems.
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Deepika, N., and O. S. Abdul Qadir. "A Study on Nature Inspired Task Scheduling Algorithms in Cloud Environment." Asian Journal of Computer Science and Technology 8, S2 (2019): 79–82. http://dx.doi.org/10.51983/ajcst-2019.8.s2.2019.

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Cloud computing is an encouraging paradigm which offers resources to customers on their demand with least cost. Task scheduling is the key difficult in cloud computing which decreases the performance of the system. To develop performance of the system, there is necessity of an effective task-scheduling algorithm. Nature inspired computing is a technique that is inspired by practices detected from nature. These computing techniques led to the growth of algorithms called Nature Inspired Algorithms (NIA). These algorithms are theme of computational intelligence. The persistence of raising such algorithms is to enhance engineering problems. Nature inspired algorithms have enlarged huge popularity in recent years to challenge hard real world (NP hard and NP complete) problems and resolve complex optimization functions whose actual solution doesn’t occur. The paper presents a complete review of 12 nature inspired algorithms. This study offers the researchers with a single platform to analyze the conventional and contemporary nature inspired algorithms in terms of essential input parameters, their key evolutionary strategies and application areas. This study would support the research community to recognize what all algorithms could be observed for big scale global optimization to overwhelm the problem of ‘curse of dimensionality’.
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Dissertations / Theses on the topic "Nature-Inspired Algorithms"

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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 applications. This thesis improves the convergence properties of particles by a novel scale free correlation approach. By altering the fundamental definition of a particle and by avoiding the nostalgic operations the tracking was expedited to a rate of 250 FPS. There is a reasonable amount of similarity between the tracking landscapes and the ones generated by three dimensional evolutionary test cases. Several experimental studies are conducted that compares the performances of the novel optimisation to the ones observed with the swarming methods. It is therefore concluded that the modified particle behaviour outclassed the traditional approaches by huge margins in almost every test scenario.
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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 to present the strengths and weaknesses of an algorithm. The methodology was tested by developing a portfolio of six nature-inspired algorithms commonly used to solve continuous optimisation problems. This portfolio includes the performance of these algorithms with parameters both “out of the box" and after they have been tuned using an automated tuning technique. Each of the algorithms shows a different “resilience" profile to the landscape characteristics, and responds differently to the tuning process. In order to provide a more practical way to utilise the portfolio an automated “ranking" methodology based on two machine learning techniques was developed. Using estimates of the fitness landscape characteristics on benchmark problems, the best algorithm to use is estimated, and compared with the actual performance of each algorithm. While results show that predicting algorithm performance is difficult, the results are promising, and show that this is an area worth exploring further. This methodology has significant advantages over the current practice of demonstrating novel algorithm performance on benchmark problems, most importantly offering a practical, generalised overview of the algorithm to a potential practitioner. Choosing to use a technique such as the one demonstrated here when presenting a novel algorithm could greatly ease the problem of algorithm selection.
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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 in similar banks. An example of this phenomenon is the failure of Lehman Brothers and the subsequent turmoil in the US financial markets. International financial contagion happens in both advanced economies and developing economies, and is the transmission of financial crises across financial markets. Within the current globalise financial system, with large volumes of cash flow and cross-regional operations of large banks and hedge funds, financial contagion usually happens simultaneously among both domestic institutions and across countries. There is no conclusive definition of financial contagion, most research papers study contagion by analyzing the change in the variance-covariance matrix during the period of market turmoil. King and Wadhwani (1990) first test the correlations between the US, UK and Japan, during the US stock market crash of 1987. Boyer (1997) finds significant increases in correlation during financial crises, and reinforces a definition of financial contagion as a correlation changing during the crash period. Forbes and Rigobon (2002) give a definition of financial contagion. In their work, the term interdependence is used as the alternative to contagion. They claim that for the period they study, there is no contagion but only interdependence. Interdependence leads to common price movements during periods both of stability and turmoil. In the past two decades, many studies (e.g. Kaminsky et at., 1998; Kaminsky 1999) have developed early warning systems focused on the origins of financial crises rather than on financial contagion. Further authors (e.g. Forbes and Rigobon, 2002; Caporale et al, 2005), on the other hand, have focused on studying contagion or interdependence. In this thesis, an overall mechanism is proposed that simulates characteristics of propagating crisis through contagion. Within that scope, a new co-evolutionary market model is developed, where some of the technical traders change their behaviour during crisis to transform into herd traders making their decisions based on market sentiment rather than underlying strategies or factors. The thesis focuses on the transformation of market interdependence into contagion and on the contagion effects. The author first build a multi-national platform to allow different type of players to trade implementing their own rules and considering information from the domestic and a foreign market. Traders’ strategies and the performance of the simulated domestic market are trained using historical prices on both markets, and optimizing artificial market’s parameters through immune - particle swarm optimization techniques (I-PSO). The author also introduces a mechanism contributing to the transformation of technical into herd traders. A generalized auto-regressive conditional heteroscedasticity - copula (GARCH-copula) is further applied to calculate the tail dependence between the affected market and the origin of the crisis, and that parameter is used in the fitness function for selecting the best solutions within the evolving population of possible model parameters, and therefore in the optimization criteria for contagion simulation. The overall model is also applied in predictive mode, where the author optimize in the pre-crisis period using data from the domestic market and the crisis-origin foreign market, and predict in the crisis period using data from the foreign market and predicting the affected domestic market.
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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) Previous issue date: 2014-07-24<br>CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Neste trabalho é proposto uma metodologia multi-estágio utilizando algoritmos bio-inspirados para a resolução do processo de Restabelecimento de Sistemas Elétricos de Distribuição. O primeiro estágio consiste na solução de uma função multi-objetivo visando a determinação da configuração final das chaves do sistema após isolados os ramos defeituosos (configuração de pós-contingência). Neste estágio, a modelagem da função multi-objetivo busca uma configuração adequada de chaves para minimizar a carga não suprida, as perdas do sistema, o número de chaveamentos, penalizando as violações aos limites operativos do sistema e considerando a presença de consumidores prioritários. Adicionalmente, a restrição de radialidade é assegurada em cada configuração utilizando, caso necessário, uma técnica de abertura de laço. A partir da configuração final obtida no primeiro estágio, são identificadas as chaves que foram manobradas. O segundo estágio da metodologia busca a determinação da sequência de chaveamento levando em conta a minimização da energia não suprida. Essa formulação permite que o tempo de manobra das chaves possa ser considerado. Sendo necessário, é realizado, ainda neste estágio, cortes mínimos discretos de carga para cada manobra executada. Em ambos os estágios foram utilizadas algoritmos bio-inspirados como métodos de solução dos respectivos problemas de otimização não-lineares inteiros mistos. As técnicas utilizadas são: Algoritmos Genéticos, Método da Eco Localização de Morcegos (Bat Algorithm) e Método da Reprodução dos Pássaros Cuco (Cuckoo Search). Os desenvolvimentos do algoritmo proposto foi implementado no ambiente MatLab®. Os resultados obtidos foram comparados com outras metodologias conhecidas da literatura comprovando a eficiência e robustez da técnica proposta.<br>This dissertation proposes a methodology for solving multi-stage process of Restoration on Power Distribution Systems using Nature-Inspired Algorithms. The first stage consists in solving a fitness multi-objective function in order to determine the final configuration of the switches after the faulted branches were isolated (post-contingency configuration). In this stage the multi-objective function seeks through the suitable configuration to minimize the undelivered power, the power losses, the number of switching, penalizing for violation in the system operational limits and taking in consideration the presence of priority load in the system. Additionally the radiality constraint is improved using an open loop technique. After the final configuration is obtained, for the first stage, the switches which were maneuvered are identified. The second stage of the methodology is to determine the sequence of switching taking into account the minimization of energy not supplied. This formulation allows to consider the switching operation time. If necessary, the minimum discrete load shedding procedure is made for each maneuvered switch. In both stages Nature-Inspired Algorithms to solve mixed integer nonlinear programming problems were used. The techniques used are: Genetic Algorithms, Bat Algorithm and Cuckoo Search. The developments of the proposed algorithm were implemented in MatLab ® environment. The results obtained were compared with other well-known methodologies showing the efficiency and robustness of the proposed technique.
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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, specifically, sophisticated collective behaviour of social insects borne out of primitive interactions amongst members of the group to solve problems beyond the capability of individual members. Consequently, the approach is self-organising, adaptive and robust. A new scheme for server ensemble orchestration is introduced in this thesis. This scheme exploits the many similarities between server orchestration in an internet hosting centre and forager allocation in a honeybee (Apis mellifera) colony. The scheme mimics the way a honeybee colony distributes foragers amongst flower patches to maximise nectar influx, to orchestrate servers amongst hosted internet services to maximise revenue. The scheme is extended by further exploiting inherent feedback loops within the colony to introduce self-tuning and energy-aware server ensemble orchestration. In order to evaluate the new server ensemble orchestration scheme, a collection of server ensemble orchestration methods is developed, including a classical technique that relies on past history to make time varying orchestration decisions and two theoretical techniques that omnisciently make optimal time varying orchestration decisions or an optimal static orchestration decision based on complete knowledge of the future. The efficacy of the new biomimetic scheme is assessed in terms of adaptiveness and versatility. The performance study uses representative classes of internet traffic stream behaviour, service user's behaviour, demand intensity, multiple services co-hosting as well as differentiated hosting fee schedule. The biomimetic orchestration scheme is compared with the classical and the theoretical optimal orchestration techniques in terms of revenue stream. This study reveals that the new server ensemble orchestration approach is adaptive in a widely varying external internet environments. The study also highlights the versatility of the biomimetic approach over the classical technique. The self-tuning scheme improves on the original performance. The energy-aware scheme is able to conserve significant energy with minimal revenue performance degradation. The simulation results also indicate that the new scheme is competitive or better than classical and static methods.
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Sholedolu, Michael O. "Nature-inspired optimisation : improvements to the Particle Swarm Optimisation Algorithm and the Bees Algorithm." Thesis, Cardiff University, 2009. http://orca.cf.ac.uk/55013/.

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This research focuses on nature-inspired optimisation algorithms, in particular, the Particle Swarm Optimisation (PSO) Algorithm and the Bees Algorithm. The PSO Algorithm is a population-based stochastic optimisation technique first invented in 1995. It was inspired by the social behaviour of birds flocking or a school of fish. The Bees Algorithm is a population-based search algorithm initially proposed in 2005. It mimics the food foraging behaviour of swarms of honey bees. The thesis presents three algorithms. The first algorithm called the PSO-Bees Algorithm is a cross between the PSO Algorithm and the Bees Algorithm. The PSO-Bees Algorithm enhanced the PSO Algorithm with techniques derived from the Bees Algorithm. The second algorithm called the improved Bees Algorithm is a version of the Bees Algorithm that incorporates techniques derived from the PSO Algorithm. The third algorithm called the SNTO-Bees Algorithm enhanced the Bees Algorithm using techniques derived from the Sequential Number-Theoretic Optimisation (SNTO) Algorithm. To demonstrate the capability of the proposed algorithms, they were applied to different optimisation problems. The PSO-Bees Algorithm is used to train neural networks for two problems, Control Chart Pattern Recognition and Wood Defect Classification. The results obtained and those from tests on well known benchmark functions provide an indication of the performance of the algorithm relative to that of other swarm-based stochastic optimisation algorithms. The improved Bees Algorithm was applied to mechanical design optimisation problems (design of welded beams and coil springs) and the mathematical benchmark problems used previously to test the PSO-Bees Algorithm. The algorithm incorporates cooperation and communication between different neighbourhoods. The results obtained show that the proposed cooperation and communication strategies adopted enhanced the performance and convergence of the algorithm. The SNTO-Bees Algorithm was applied to a set of mechanical design optimisation problems (design of welded beams, coil springs and pressure vessel) and mathematical benchmark functions used previously to test the PSO-Bees Algorithm and the improved Bees Algorithm. In addition, the algorithm was tested with a number of deceptive multi modal benchmark functions. The results obtained help to validate the SNTO-Bees Algorithm as an effective global optimiser capable of handling problems that are deceptive in nature with high dimensions.
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Lakshminarayanan, Srinivasan. "Nature Inspired Discrete Integer Cuckoo Search Algorithm for Optimal Planned Generator Maintenance Scheduling." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1438101954.

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Lakshminarayanan, Srivathsan. "Nature Inspired Grey Wolf Optimizer Algorithm for Minimizing Operating Cost in Green Smart Home." University of Toledo / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1438102173.

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Ayo, Babatope S. "Data-driven flight path rerouting during adverse weather: Design and development of a passenger-centric model and framework for alternative flight path generation using nature inspired techniques." Thesis, University of Bradford, 2018. http://hdl.handle.net/10454/17387.

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A major factor that negatively impacts flight operations globally is adverse weather. To reduce the impact of adverse weather, avoidance procedures such as finding an alternative flight path can usually be carried out. However, such procedures usually introduce extra costs such as flight delay. Hence, there exists a need for alternative flight paths that efficiently avoid adverse weather regions while minimising costs. Existing weather avoidance methods used techniques, such as Dijkstra’s and artificial potential field algorithms that do not scale adequately and have poor real time performance. They do not adequately consider the impact of weather and its avoidance on passengers. The contributions of this work include a new development of an improved integrated model for weather avoidance, that addressed the impact of weather on passengers by defining a corresponding cost metric. The model simultaneously considered other costs such as flight delay and fuel burn costs. A genetic algorithm (GA)-based rerouting technique that generates optimised alternative flight paths was proposed. The technique used a modified mutation strategy to improve global search. A discrete firefly algorithm-based rerouting method was also developed to improve rerouting efficiency. A data framework and simulation platform that integrated aeronautical, weather and flight data into the avoidance process was developed. Results show that the developed algorithms and model produced flight paths that had lower total costs compared with existing techniques. The proposed algorithms had adequate rerouting performance in complex airspace scenarios. The developed system also adequately avoided the paths of multiple aircraft in the considered airspace.
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Books on the topic "Nature-Inspired Algorithms"

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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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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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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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Yang, Xin-She, and Xing-Shi He. Mathematical Foundations of Nature-Inspired Algorithms. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16936-7.

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Bozorg-Haddad, Omid, ed. Advanced Optimization by Nature-Inspired Algorithms. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5221-7.

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Yadav, Neha, Anupam Yadav, Jagdish Chand Bansal, Kusum Deep, and Joong Hoon Kim, eds. Harmony Search and Nature Inspired Optimization Algorithms. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0761-4.

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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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Dey, Nilanjan, Amira S. Ashour, and Siddhartha Bhattacharyya, eds. Applied Nature-Inspired Computing: Algorithms and Case Studies. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-13-9263-4.

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Garg, Vanita, and Kusum Deep, eds. Role of Nature-Inspired Algorithms in Real-life Problems. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-97-4715-3.

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Book chapters on the topic "Nature-Inspired Algorithms"

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Yang, Xin-She, and Xing-Shi He. "Nature-Inspired Algorithms." In SpringerBriefs in Optimization. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-16936-7_2.

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Shandilya, Shishir Kumar, Agni Datta, and Atulya K. Nagar. "Nature-inspired Algorithms." In Studies in Computational Intelligence. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7081-0_1.

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Siddique, Nazmul, and Hojjat Adeli. "Miscellaneous Algorithms." In Nature-Inspired Computing. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315118628-10.

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Mishra, Krishn Kumar. "Binary Genetic Algorithms." In Nature-Inspired Algorithms. CRC Press, 2022. http://dx.doi.org/10.1201/9781003313649-2.

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Siddique, Nazmul, and Hojjat Adeli. "Spiral Dynamics Algorithms." In Nature-Inspired Computing. Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315118628-7.

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Mishra, Krishn Kumar. "Application of Genetic Algorithms, Partial Swarm Optimization, and Differential Evolution in Software Testing." In Nature-Inspired Algorithms. CRC Press, 2022. http://dx.doi.org/10.1201/9781003313649-9.

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Mishra, Krishn Kumar. "Application of Genetic Algorithms and Partial Swarm Optimization in Cloud Computing." In Nature-Inspired Algorithms. CRC Press, 2022. http://dx.doi.org/10.1201/9781003313649-11.

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Mishra, Krishn Kumar. "Grey Wolf Optimization." In Nature-Inspired Algorithms. CRC Press, 2022. http://dx.doi.org/10.1201/9781003313649-6.

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Mishra, Krishn Kumar. "Differential Evolution." In Nature-Inspired Algorithms. CRC Press, 2022. http://dx.doi.org/10.1201/9781003313649-4.

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Mishra, Krishn Kumar. "Environmental Adaptation Method." In Nature-Inspired Algorithms. CRC Press, 2022. http://dx.doi.org/10.1201/9781003313649-7.

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Conference papers on the topic "Nature-Inspired Algorithms"

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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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Albeanu, Grigore, Henrik Madsen, and Florin Popentiuvladicescu. "LEARNING FROM NATURE: NATURE-INSPIRED ALGORITHMS." In eLSE 2016. Carol I National Defence University Publishing House, 2016. http://dx.doi.org/10.12753/2066-026x-16-158.

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Abstract:
During last decade, the nature has inspired researchers to develop new algorithms [1, 2, 3]. The largest collection of nature-inspired algorithms is biology-inspired: swarm intelligence (particle swarm optimization, ant colony optimization, cuckoo search, bees algorithm, bat algorithm, firefly algorithm etc.), genetic and evolutionary strategies, artificial immune systems etc. As well-known examples, the following have to be mentioned: aircraft wing design, wind turbine design, bionic car, bullet train, optimal decisions related to traffic, appropriate strategies to survive under a well-adapted immune system etc. Based on collective social behavior of organisms, researchers had developed optimization strategies taking into account not only the individuals, but also groups and environment [1]. However, learning from nature, new classes of approaches can be identified, tested and compared against already available algorithms. After a short introduction, this work review the most effective, according to their performance, nature-inspired algorithms, in the second section. The third section is dedicated to learning strategies based on nature oriented thinking. Examples and the benefits obtained from applying nature-inspired strategies in problem solving are given in the fourth section. Concluding remarks are given in the final section. References 1. G. Albeanu, B. Burtschy, Fl. Popentiu-Vladicescu, Soft Computing Strategies in Multiobjective Optimization, Ann. Spiru Haret Univ., Mat-Inf Ser., 2013, 2, http://anale-mi.spiruharet.ro/upload/full_2013_2_a4.pdf 2. H. Madsen, G. Albeanu, and Fl. Popentiu-Vladicescu, BIO Inspired Algorithms in Reliability, In H. Pham (ed.) Proceedings of the 20th ISSAT International Conference on Reliability and Quality in Design, Reliability and Quality in Design, August 7-9, 2014, Seattle, WA, U.S.A. 3. N. Shadbolt, Nature-Inspired Computing, http://www.agent.ai/doc/upload/200402/shad04_1.pdf
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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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Bentley, Peter J. "Building a Nature-Inspired Computer." In 2015 17th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC). IEEE, 2015. http://dx.doi.org/10.1109/synasc.2015.12.

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Dhruve, Keyuri, and Devinder Kaur. "Nature-Inspired Algorithms for Image Enhancement." In 2021 IEEE International Midwest Symposium on Circuits and Systems (MWSCAS). IEEE, 2021. http://dx.doi.org/10.1109/mwscas47672.2021.9531785.

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Bejinariu, Silviu-Ioan, Ramona Luca, and Hariton Costin. "Nature-inspired algorithms based multispectral image fusion." In 2016 International Conference and Exposition on Electrical and Power Engineering (EPE). IEEE, 2016. http://dx.doi.org/10.1109/icepe.2016.7781293.

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