Relevant bibliographies by topics / NATURE INSPIRED ALGORITHM

Contents

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

Academic literature on the topic 'NATURE INSPIRED ALGORITHM'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "NATURE INSPIRED ALGORITHM"

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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 (June 16, 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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Goudhaman, M. "Cheetah chase algorithm (CCA): a nature-inspired metaheuristic algorithm." International Journal of Engineering & Technology 7, no. 3 (August 22, 2018): 1804. http://dx.doi.org/10.14419/ijet.v7i3.18.14616.

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In recent years, appreciable attention among analysts to take care of the extraordinary enhancement issues utilizing metaheuristic algorithms in the domain area of Swarm Intelligence. Many metaheuristic algorithms have been developed by inspiring various nature phenomena’s. Exploration and exploitation are distinctive capacities and confine each other, along these lines, customary calculations require numerous parameters and bunches of expenses to accomplish the adjust, and furthermore need to modify parameters for various enhancement issues. In this paper, another populace based algorithm, the Cheetah Chase Algorithm (CCA), is presented. Distinctive features of Cheetah and their characteristics has been the essential motivation for advancement of this optimization algorithm. Cheetah Chase Algorithm (CCA) has awesome capacities both in exploitation and exploration, is proposed to address these issues. To start with, CCA endeavours to locate the optimal solution in the assigned hunt territory. It at that point utilizes history data to pursue its prey. CCA can, hence, decide the situation of the worldwide ideal. CCA accomplishes solid exploitation and exploration with these highlights. Additionally, as indicated by various issues, CCA executes versatile parameter change. The self-examination and analysis of this exploration show that each CCA capacity can have different beneficial outcomes, while the execution correlation exhibits CCAs predominance over conventional metaheuristic algorithms. The proposed Cheetah Chase Algorithm is developed by the process of hunting and chasing of Cheetah to capture its prey with the parameters of high speed, velocity and greater accelerations.
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Abualigah, Laith, Amir H. Gandomi, Mohamed Abd Elaziz, Abdelazim G. Hussien, Ahmad M. Khasawneh, Mohammad Alshinwan, and Essam H. Houssein. "Nature-Inspired Optimization Algorithms for Text Document Clustering—A Comprehensive Analysis." Algorithms 13, no. 12 (December 18, 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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Gencal, Mashar, and Mustafa Oral. "Roosters Algorithm: A Novel Nature-Inspired Optimization Algorithm." Computer Systems Science and Engineering 42, no. 2 (2022): 727–37. http://dx.doi.org/10.32604/csse.2022.023018.

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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 (April 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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Hussein, Eslam, Ahmed Ibrahem Hafez, Aboul Ella Hassanien, and Aly A. Fahmy. "Nature inspired algorithms for solving the community detection problem." Logic Journal of the IGPL 25, no. 6 (October 26, 2017): 902–14. http://dx.doi.org/10.1093/jigpal/jzx043.

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Abstract Nature inspired Swarm algorithms have proven to be effective in solving recent complex optimization problems. Comparing such algorithm is a difficult task due to many facts, the nature of the swarm, the nature of the optimization problem itself and number of controlling parameters of the swarm algorithm. In this work we compared two recent swarm algorithms applied to the community detection problem which are the Bat Algorithm (BA) and Artificial Fish Swarm Algorithm (AFSA). Community detection is an active problem in social network analysis. The problem of detecting communities can be represented as an optimization problem where a quality fitness function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. We also investigated the application of the BA and AFSA in solving the community section problem. And introduced a comparative analysis between the two algorithms and other well-known methods. The study show the effectiveness and the limitations of both algorithms.
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Carreon-Ortiz, Hector, Fevrier Valdez, and Oscar Castillo. "A New Discrete Mycorrhiza Optimization Nature-Inspired Algorithm." Axioms 11, no. 8 (August 9, 2022): 391. http://dx.doi.org/10.3390/axioms11080391.

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This paper presents the discrete version of the Mycorrhiza Tree Optimization Algorithm (MTOA), using the Lotka–Volterra Discrete Equation System (LVDES) formed by the Predator–Prey, Cooperative and Competitive Models. The Discrete Mycorrhizal Optimization Algorithm (DMOA) is a stochastic metaheuristic that integrates randomness in its search processes. These algorithms are inspired by nature, specifically by the symbiosis between plant roots and a fungal network called the Mycorrhizal Network (MN). The communication in the network is performed using chemical signals of environmental conditions and hazards, the exchange of resources, such as Carbon Dioxide (CO2) that plants perform through photosynthesis to the MN and to other seedlings or growing plants. The MN provides water (H2O) and nutrients to plants that may or may not be of the same species; therefore, the colonization of plants in arid lands would not have been possible without the MN. In this work, we performed a comparison with the CEC-2013 mathematical functions between MTOA and DMOA by conducting Hypothesis Tests to obtain the efficiency and performance of the algorithms, but in future research we will also propose optimization experiments in Neural Networks and Fuzzy Systems to verify with which methods these algorithms perform better.
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Karunanidy, Dinesh, Subramanian Ramalingam, Ankur Dumka, Rajesh Singh, Mamoon Rashid, Anita Gehlot, Sultan S. Alshamrani, and Ahmed Saeed AlGhamdi. "JMA: Nature-Inspired Java Macaque Algorithm for Optimization Problem." Mathematics 10, no. 5 (February 23, 2022): 688. http://dx.doi.org/10.3390/math10050688.

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In recent years, optimization problems have been intriguing in the field of computation and engineering due to various conflicting objectives. The complexity of the optimization problem also dramatically increases with respect to a complex search space. Nature-Inspired Optimization Algorithms (NIOAs) are becoming dominant algorithms because of their flexibility and simplicity in solving the different kinds of optimization problems. Hence, the NIOAs may be struck with local optima due to an imbalance in selection strategy, and which is difficult when stabilizing exploration and exploitation in the search space. To tackle this problem, we propose a novel Java macaque algorithm that mimics the natural behavior of the Java macaque monkeys. The Java macaque algorithm uses a promising social hierarchy-based selection process and also achieves well-balanced exploration and exploitation by using multiple search agents with a multi-group population, male replacement, and learning processes. Then, the proposed algorithm extensively experimented with the benchmark function, including unimodal, multimodal, and fixed-dimension multimodal functions for the continuous optimization problem, and the Travelling Salesman Problem (TSP) was utilized for the discrete optimization problem. The experimental outcome depicts the efficiency of the proposed Java macaque algorithm over the existing dominant optimization algorithms.
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Rajakumar, B. R. "The Lion's Algorithm: A New Nature-Inspired Search Algorithm." Procedia Technology 6 (2012): 126–35. http://dx.doi.org/10.1016/j.protcy.2012.10.016.

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Maghayreh, Eslam Al, Sallam Abu Al Haija, Faisal Alkhateeb, Shadi Aljawarneh, and Emad Al Shawakfa. "BeesAnts: a new nature-inspired routing algorithm." International Journal of Communication Networks and Distributed Systems 10, no. 1 (2013): 83. http://dx.doi.org/10.1504/ijcnds.2013.050614.

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Dissertations / Theses on the topic "NATURE INSPIRED ALGORITHM"

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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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Bozhinovski, Konstantin. "Generative design of a nature-inspired geometry manipulated by an algorithm in a BIM-environment, applied in a façade system for a residential building in Bologna, Italy." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/21501/.

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In terms of technology, BIM is also part of the worldwide change Industry 4.0, which in essence is the trend toward automation and data exchange in manufacturing technologies and processes. Generative design is an iterative process that involves a program that will generate a certain number of outputs that meet certain constraints, so that a designer is able to fine tune the feasible project by changing minimal and maximal values of an interval in which a variable of the program meets the set of constraints, in order to reduce or augment the number of outputs to choose from. The initial idea of this thesis work was to manipulate few of the most basic geometric elements in order to get a complex parametric shape inspired from the honeycomb as the natures perfectly generated the element. This preliminary idea, together with the ambition to use this transformation for a façade system in a structural building led us to a series of decisions to try and connect two “worlds”, in the sense that we have a CAD environment that lets us create the geometry and a BIM environment where everything is represented by a specific level of information. This geometry is given a specific set of rules that drive and manipulate each of the elements it contains in a certain fashion. This methodology, as well as the communication and the interaction between the software adopted and their programming environments, is what makes the generative design possible. This result from the Grasshopper algorithm is then being created in the CAD environment in Rhinoceros3D, which then can be opened through Rhino.Inside.Revit and give us a direct real-time preview in the BIM environment in Revit. Through a long series of testing and experimenting with the geometry, we get to a point where we have a functional algorithm that creates and manipulates the geometry, in order to foster many design opportunities for structural and architectural designers.
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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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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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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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Books on the topic "NATURE INSPIRED ALGORITHM"

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Caraveo, Camilo, Fevrier Valdez, and Oscar Castillo. A New Bio-inspired Optimization Algorithm Based on the Self-defense Mechanism of Plants in Nature. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-05551-6.

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

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Gavrilova, Marina L. Transactions on Computational Science XV: Special Issue on Advances in Autonomic Computing: Formal Engineering Methods for Nature-Inspired Computing Systems. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Jain, Shashank. Nature-Inspired Optimization Algorithms with Java. Berkeley, CA: 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. Cham: 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. Cham: 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. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-5221-7.

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Clever algorithms: Nature-inspired programming recipes. [S.l.]: Lulu, 2011.

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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. Singapore: 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. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6773-9.

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Book chapters on the topic "NATURE INSPIRED ALGORITHM"

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Siddique, Nazmul, and Hojjat Adeli. "Gravitational Search Algorithm." In Nature-Inspired Computing, 51–118. Boca Raton : CRC Press, 2017.: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315118628-2.

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Siddique, Nazmul, and Hojjat Adeli. "Water Drop Algorithm." In Nature-Inspired Computing, 291–328. Boca Raton : CRC Press, 2017.: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315118628-6.

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Mishra, Krishn Kumar. "Real-Parameter Genetic Algorithm." In Nature-Inspired Algorithms, 59–89. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003313649-3.

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Ahlawat, Rohit Kumar, Amita Malik, and Archana Sadhu. "Sybil Attack Prevention Algorithm for Body Area Networks." In Nature Inspired Computing, 125–34. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6747-1_15.

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Bala, Yogesh, and Amita Malik. "Biometric Inspired Homomorphic Encryption Algorithm for Secured Cloud Computing." In Nature Inspired Computing, 13–21. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6747-1_2.

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Mafarja, Majdi, Ali Asghar Heidari, Hossam Faris, Seyedali Mirjalili, and Ibrahim Aljarah. "Dragonfly Algorithm: Theory, Literature Review, and Application in Feature Selection." In Nature-Inspired Optimizers, 47–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12127-3_4.

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Mirjalili, Seyedali, Jin Song Dong, Ali Safa Sadiq, and Hossam Faris. "Genetic Algorithm: Theory, Literature Review, and Application in Image Reconstruction." In Nature-Inspired Optimizers, 69–85. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12127-3_5.

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Faris, Hossam, Seyedali Mirjalili, Ibrahim Aljarah, Majdi Mafarja, and Ali Asghar Heidari. "Salp Swarm Algorithm: Theory, Literature Review, and Application in Extreme Learning Machines." In Nature-Inspired Optimizers, 185–99. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12127-3_11.

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Saremi, Shahrzad, Seyedehzahra Mirjalili, Seyedali Mirjalili, and Jin Song Dong. "Grasshopper Optimization Algorithm: Theory, Literature Review, and Application in Hand Posture Estimation." In Nature-Inspired Optimizers, 107–22. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12127-3_7.

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Mirjalili, Seyedehzahra, Seyed Mohammad Mirjalili, Shahrzad Saremi, and Seyedali Mirjalili. "Whale Optimization Algorithm: Theory, Literature Review, and Application in Designing Photonic Crystal Filters." In Nature-Inspired Optimizers, 219–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12127-3_13.

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Conference papers on the topic "NATURE INSPIRED ALGORITHM"

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Ajay Adithyan, T., Vasudha Sharma, B. Gururaj, and Chandrasegar Thirumalai. "Nature inspired algorithm." In 2017 International Conference on Trends in Electronics and Informatics (ICOEI). IEEE, 2017. http://dx.doi.org/10.1109/icoei.2017.8300889.

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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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Joshi, Nishtha, Nagma Arora, Divya Upadhyay, and Ashwani Kumar Dubey. "Optimized Time Synchronization Algorithm Inspired By Nature." In 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2018. http://dx.doi.org/10.1109/spin.2018.8474208.

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Abraham, Ajith. "Tuning Evolutionary Algorithm Performance Using Nature Inspired Heuristics." In 2006 Eighth International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. IEEE, 2006. http://dx.doi.org/10.1109/synasc.2006.78.

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Xiang Feng, Francis C. M. Lau, and Dianxun Shuai. "A new nature-inspired algorithm for load balancing." In 2008 11th IEEE Singapore International Conference on Communication Systems (ICCS). IEEE, 2008. http://dx.doi.org/10.1109/iccs.2008.4737190.

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Jump, Michael, and Gareth Padfield. "Development of a Nature-Inspired Flare Command Algorithm." In AIAA Guidance, Navigation and Control Conference and Exhibit. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2007. http://dx.doi.org/10.2514/6.2007-6769.

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Shilpi and Arvind Kumar. "Nature Inspired Node Localization Algorithm for Anisotropic WSNs." In 2022 IEEE 9th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE, 2022. http://dx.doi.org/10.1109/upcon56432.2022.9986387.

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Shareef, Hussain, Md Mainul Islam, Ahmad Asrul Ibrahim, and Ammar Hussein Mutlag. "A Nature Inspired Heuristic Optimization Algorithm Based on Lightning." In 2015 3rd International Conference on Artificial Intelligence, Modelling & Simulation (AIMS). IEEE, 2015. http://dx.doi.org/10.1109/aims.2015.12.

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Mekhaznia, T. "Nature inspired heuristics for attack of simplified DES algorithm." In the 6th International Conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2523514.2527010.

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Liu, Changjun, Yingni Zhai, Lichen Shi, Yixing Gao, and Junhu Wei. "Group area search: A novel nature-inspired optimization algorithm." In 2013 IEEE International Conference on Information and Automation (ICIA). IEEE, 2013. http://dx.doi.org/10.1109/icinfa.2013.6720504.

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