106 results on '"Bio-inspired computation"'
Search Results
2. Novel quartic spline method for boundary layer fluid flow problem of Falkner-Skan model with wall stretching and transfer of mass effects
- Author
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Fazle Subhan, Kottakkaran Sooppy Nisar, Muhammad Asif Zahoor Raja, Iftikhar Uddin, Muhammad Shoaib, Kashif Ullah, Saeed Islam, and Shankar Rao Munjam
- Subjects
Quartic splines method ,Active-Set algorithms ,Genetic-Algorithm ,FSFM Hybrid computing ,Bio-inspired computation ,HAM ,Engineering (General). Civil engineering (General) ,TA1-2040 - Abstract
The non-linear ordinary differential equations (NODEs) in this article are estimated and analyzed numerically using the capability of the Quartic Splines Method (QSM) for mathematical modeling of the Falkner-Skan fluidic system and its optimization through global search Genetic Algorithms (GAs) and local search Active-Set (AS) techniques. The concept of hybridization is used to optimize the obtained results and provide a boost to the suggested method, QSM, which allows for rapid iteration. Falkner-Skan fluid model (FSFM) is solved by the proposed technique QSM-GAs-AS. The FSFM is solved for three, seven, and twelve splines successfully. The problem is analyzed for three scenarios, in which each scenario is based on the variation of a parameter out of the three involved parameters, namely the wall mass transfer parameter (γ), the wall movement parameter (λ), and the stream-wise pressure gradient parameter (β), appearing in FSFM. The QSM-GAs-AS produces an interpolated function that is continuous up to its fourth derivative. The solution outcomes of FSFM, treated by the designed scheme QSM-GAs-AS, are presented graphically. The evaluation of the planned solution is done with a deterministic numerical solver, the Homotopy Analysis Method (HAM). Statistical analysis for multiple runs is used to examine the proposed scheme's convergence, exactness, and accuracy.
- Published
- 2024
- Full Text
- View/download PDF
3. Game Theory and Other Unconventional Approaches to Biological Systems
- Author
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Kastampolidou, Kalliopi, Andronikos, Theodore, Vlamos, Panagiotis, editor, Kotsireas, Ilias S., editor, and Tarnanas, Ioannis, editor
- Published
- 2023
- Full Text
- View/download PDF
4. Evolutionary Continuous Optimization of Hybrid Gene Regulatory Networks
- Author
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Michelucci, Romain, Comet, Jean-Paul, Pallez, Denis, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Legrand, Pierrick, editor, Liefooghe, Arnaud, editor, Keedwell, Edward, editor, Lepagnot, Julien, editor, Idoumghar, Lhassane, editor, Monmarché, Nicolas, editor, and Lutton, Evelyne, editor
- Published
- 2023
- Full Text
- View/download PDF
5. Bio-Inspired Internet of Things: Current Status, Benefits, Challenges, and Future Directions.
- Author
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Alabdulatif, Abdullah and Thilakarathne, Navod Neranjan
- Subjects
- *
INTERNET of things , *BIOLOGICAL systems , *WIRELESS sensor networks - Abstract
There is no doubt that the involvement of the Internet of Things (IoT) in our daily lives has changed the way we live and interact as a global community, as IoT enables intercommunication of digital objects around us, creating a pervasive environment. As of now, this IoT is found in almost every domain that is vital for human survival, such as agriculture, medical care, transportation, the military, and so on. Day by day, various IoT solutions are introduced to the market by manufacturers towards making our life easier and more comfortable. On the other hand, even though IoT now holds a key place in our lives, the IoT ecosystem has various limitations in efficiency, scalability, and adaptability. As such, biomimicry, which involves imitating the systems found in nature within human-made systems, appeared to be a potential remedy to overcome such challenges pertaining to IoT, which can also be referred to as bio-inspired IoT. In the simplest terms, bio-inspired IoT combines nature-inspired principles and IoT to create more efficient and adaptive IoT solutions, that can overcome most of the inherent challenges pertaining to traditional IoT. It is based on the idea that nature has already solved many challenging problems and that, by studying and mimicking biological systems, we might develop better IoT systems. As of now, this concept of bio-inspired IoT is applied to various fields such as medical care, transportation, cyber-security, agriculture, and so on. However, it is noted that only a few studies have been carried out on this new concept, explaining how these bio-inspired concepts are integrated with IoT. Thus, to fill in the gap, in this study, we provide a brief review of bio-inspired IoT, highlighting how it came into play, its ecosystem, its latest status, benefits, challenges, and future directions. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
6. A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics.
- Author
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Jakšić, Zoran, Devi, Swagata, Jakšić, Olga, and Guha, Koushik
- Subjects
- *
MICROELECTRONICS , *NANOPHOTONICS , *MACHINE learning , *ARTIFICIAL neural networks , *CONVOLUTIONAL neural networks - Abstract
The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
7. The input-dependent variable sampling (I-DEVS) energy-efficient digital neuron implementation method.
- Author
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Leigh, Alexander J., Heidarpur, Moslem, and Mirhassani, Mitra
- Abstract
A method is proposed by which the power consumption of a biologically detailed digital neuron implementation can be reduced without modification to the digital neuron's hardware architecture and independent of the neuron model. This method results in substantial power savings by causing the neuron to enter a quasi-functional state when low input stimulus is received. This approach is analogous to the function of real biological neurons as they enter a low-activity state for low stimulus. The shifts in neuronal activity created by the novel method allow for the membrane potential to remain uncorrupted over a large domain of input synaptic current, while avoiding unnecessary computations and switching activity. The digital hardware implementation results are presented and discussed, and it is shown that the behaviour of the neuron is unaffected using the novel method. The power consumption of the implemented digital neurons is compared with traditional implementations, and considerable power savings are shown. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
8. Covariance Features Improve Low-Resource Reservoir Computing Performance in Multivariate Time Series Classification
- Author
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Lawrie, Sofía, Moreno-Bote, Rubén, Gilson, Matthieu, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Smys, S., editor, Tavares, João Manuel R. S., editor, and Balas, Valentina Emilia, editor
- Published
- 2022
- Full Text
- View/download PDF
9. On evolving environment of 2D P colonies: ant colony simulation
- Author
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Langer, Miroslav and Valenta, Daniel
- Published
- 2023
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10. Applied Optimization and Swarm Intelligence: A Systematic Review and Prospect Opportunities
- Author
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Osaba, Eneko, Yang, Xin-She, Yang, Xin-She, Series Editor, Dey, Nilanjan, Series Editor, Fong, Simon, Series Editor, and Osaba, Eneko, editor
- Published
- 2021
- Full Text
- View/download PDF
11. Bio-Inspired Internet of Things: Current Status, Benefits, Challenges, and Future Directions
- Author
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Abdullah Alabdulatif and Navod Neranjan Thilakarathne
- Subjects
IoT ,Internet of Things ,bio-inspired IoT ,bio-inspired computation ,bio-inspired algorithms ,artificial intelligence ,Technology - Abstract
There is no doubt that the involvement of the Internet of Things (IoT) in our daily lives has changed the way we live and interact as a global community, as IoT enables intercommunication of digital objects around us, creating a pervasive environment. As of now, this IoT is found in almost every domain that is vital for human survival, such as agriculture, medical care, transportation, the military, and so on. Day by day, various IoT solutions are introduced to the market by manufacturers towards making our life easier and more comfortable. On the other hand, even though IoT now holds a key place in our lives, the IoT ecosystem has various limitations in efficiency, scalability, and adaptability. As such, biomimicry, which involves imitating the systems found in nature within human-made systems, appeared to be a potential remedy to overcome such challenges pertaining to IoT, which can also be referred to as bio-inspired IoT. In the simplest terms, bio-inspired IoT combines nature-inspired principles and IoT to create more efficient and adaptive IoT solutions, that can overcome most of the inherent challenges pertaining to traditional IoT. It is based on the idea that nature has already solved many challenging problems and that, by studying and mimicking biological systems, we might develop better IoT systems. As of now, this concept of bio-inspired IoT is applied to various fields such as medical care, transportation, cyber-security, agriculture, and so on. However, it is noted that only a few studies have been carried out on this new concept, explaining how these bio-inspired concepts are integrated with IoT. Thus, to fill in the gap, in this study, we provide a brief review of bio-inspired IoT, highlighting how it came into play, its ecosystem, its latest status, benefits, challenges, and future directions.
- Published
- 2023
- Full Text
- View/download PDF
12. Particular Biomolecular Processes as Computing Paradigms
- Author
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Giannakis, Konstantinos, Theocharopoulou, Georgia, Papalitsas, Christos, Fanarioti, Sofia, Andronikos, Theodore, Crusio, Wim E., Series Editor, Lambris, John D., Series Editor, Radeke, Heinfried H., Series Editor, Rezaei, Nima, Series Editor, and Vlamos, Panayiotis, editor
- Published
- 2020
- Full Text
- View/download PDF
13. A Comprehensive Review of Bio-Inspired Optimization Algorithms Including Applications in Microelectronics and Nanophotonics
- Author
-
Zoran Jakšić, Swagata Devi, Olga Jakšić, and Koushik Guha
- Subjects
bio-inspired computation ,multiparameter optimization ,metaheuristic algorithms ,genetic algorithms ,artificial intelligence ,deep learning ,Technology - Abstract
The application of artificial intelligence in everyday life is becoming all-pervasive and unavoidable. Within that vast field, a special place belongs to biomimetic/bio-inspired algorithms for multiparameter optimization, which find their use in a large number of areas. Novel methods and advances are being published at an accelerated pace. Because of that, in spite of the fact that there are a lot of surveys and reviews in the field, they quickly become dated. Thus, it is of importance to keep pace with the current developments. In this review, we first consider a possible classification of bio-inspired multiparameter optimization methods because papers dedicated to that area are relatively scarce and often contradictory. We proceed by describing in some detail some more prominent approaches, as well as those most recently published. Finally, we consider the use of biomimetic algorithms in two related wide fields, namely microelectronics (including circuit design optimization) and nanophotonics (including inverse design of structures such as photonic crystals, nanoplasmonic configurations and metamaterials). We attempted to keep this broad survey self-contained so it can be of use not only to scholars in the related fields, but also to all those interested in the latest developments in this attractive area.
- Published
- 2023
- Full Text
- View/download PDF
14. Dynamic Partitioning of Evolving Graph Streams Using Nature-Inspired Heuristics
- Author
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Osaba, Eneko, Bilbao, Miren Nekane, Iglesias, Andres, Del Ser, Javier, Galvez, Akemi, Fister, Iztok, Jr., Fister, Iztok, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Rodrigues, João M. F., editor, Cardoso, Pedro J. S., editor, Monteiro, Jânio, editor, Lam, Roberto, editor, Krzhizhanovskaya, Valeria V., editor, Lees, Michael H., editor, Dongarra, Jack J., editor, and Sloot, Peter M.A., editor
- Published
- 2019
- Full Text
- View/download PDF
15. Artificial intelligence and game theory controlled autonomous UAV swarms.
- Author
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Kusyk, Janusz, Uyar, M. Umit, Ma, Kelvin, Samoylov, Eltan, Valdez, Ricardo, Plishka, Joseph, Hoque, Sagor E., Bertoli, Giorgio, and Boksiner, Jeffrey
- Abstract
Autonomous unmanned aerial vehicles (uavs) operating as a swarm can be deployed in austere environments, where cyber electromagnetic activities often require speedy and dynamic adjustments to swarm operations. Use of central controllers, uav synchronization mechanisms or pre-planned set of actions to control a swarm in such deployments would hinder its ability to deliver expected services. We introduce artificial intelligence and game theory based flight control algorithms to be run by each autonomous uav to determine its actions in near real-time, while relying only on local spatial, temporal and electromagnetic (em) information. Each uav using our flight control algorithms positions itself such that the swarm maintains mobile ad-hoc network (manet) connectivity and uniform asset distribution over an area of interest. Typical tasks for swarms using our algorithms include detection, localization and tracking of mobile em transmitters. We present a formal analysis showing that our algorithms can guide a swarm to maintain a connected manet, promote a uniform network spreading, while avoiding overcrowding with other swarm members. We also prove that they maintain manet connectivity and, at the same time, they can lead a swarm of autonomous uavs to follow or avoid an em transmitter. Simulation experiments in opnet modeler verify the results of formal analysis that our algorithms are capable of providing an adequate area coverage over a mobile em source and maintain manet connectivity. These algorithms are good candidates for civilian and military applications that require agile responses to the changes in dynamic environments for tasks such as detection, localization and tracking mobile em transmitters. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
16. Hallmarks of Criticality in Neuronal Networks Depend on Cell Type and the Temporal Resolution of Neuronal Avalanches.
- Author
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HEINEY, KRISTINE, VALDERHAUG, VIBEKE DEVOLD, RAMSTAD, OLA HUSE, SANDVIG, IOANNA, SANDVIG, AXEL, and NICHELE, STEFANO
- Subjects
NEURAL circuitry ,INDUCED pluripotent stem cells ,PLURIPOTENT stem cells ,NEURAL stem cells ,DATA warehousing ,COMPUTER performance ,NEURON development - Abstract
The human brain has a remarkable capacity for computation, and it has been theorized that this capacity arises from the brain self-organizing into the critical state, a dynamical state poised between ordered and disordered behavior and widely considered to be well-suited for computation. Criticality is commonly identified in in vitro neuronal networks using an analytical approach based on the size distribution of cascades of activity called neuronal avalanches. In this study, criticality analysis was applied to different in vitro neuronal networks with two areas of focus: evaluating the effect of the size of the time bins used for neuronal avalanche detection and observation of the development of networks of neurons derived from human induced pluripotent stem cells. This preliminary study is expected to aid in the construction of models capable of emulating neuronal behaviors identified as well-suited for computation and ultimately inform the development of brain-inspired computing substrates that are better able to keep pace with increased demand for data storage and processing power. [ABSTRACT FROM AUTHOR]
- Published
- 2021
17. BENCHMARKING BIO-INSPIRED COMPUTATION ALGORITHMS AS WRAPPERS FOR FEATURE SELECTION
- Author
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Drazen BAJER, Bruno ZORIĆ, Mario DUDJAK, and Goran MARTINOVIĆ
- Subjects
bio-inspired computation ,classification ,dimensionality reduction ,feature selection ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Reducing the number of features when applying machine learning algorithms may be beneficial not only from the standpoint of computational cost but also of overall quality. Wrapper-based procedures are widely utilised to achieve this. The choice of the wrapper is of utmost importance. Bio-inspired computation algorithms represent a viable choice and are widely adopted. Due to the sheer number of available algorithms, this choice could prove to be somewhat difficult, especially since not all are made equally. The aim of this paper is to explore several optimisers on diverse datasets representing classification problems in order to evaluate their performance and suitability for the task of feature selection
- Published
- 2020
- Full Text
- View/download PDF
18. Robustness of Bio-Inspired Visual Systems for Collision Prediction in Critical Robot Traffic
- Author
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Qinbing Fu, Xuelong Sun, Tian Liu, Cheng Hu, and Shigang Yue
- Subjects
bio-inspired computation ,collision prediction ,robust visual systems ,LGMDs ,micro-robot ,critical robot traffic ,Mechanical engineering and machinery ,TJ1-1570 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
Collision prevention sets a major research and development obstacle for intelligent robots and vehicles. This paper investigates the robustness of two state-of-the-art neural network models inspired by the locust’s LGMD-1 and LGMD-2 visual pathways as fast and low-energy collision alert systems in critical scenarios. Although both the neural circuits have been studied and modelled intensively, their capability and robustness against real-time critical traffic scenarios where real-physical crashes will happen have never been systematically investigated due to difficulty and high price in replicating risky traffic with many crash occurrences. To close this gap, we apply a recently published robotic platform to test the LGMDs inspired visual systems in physical implementation of critical traffic scenarios at low cost and high flexibility. The proposed visual systems are applied as the only collision sensing modality in each micro-mobile robot to conduct avoidance by abrupt braking. The simulated traffic resembles on-road sections including the intersection and highway scenes wherein the roadmaps are rendered by coloured, artificial pheromones upon a wide LCD screen acting as the ground of an arena. The robots with light sensors at bottom can recognise the lanes and signals, tightly follow paths. The emphasis herein is laid on corroborating the robustness of LGMDs neural systems model in different dynamic robot scenes to timely alert potential crashes. This study well complements previous experimentation on such bio-inspired computations for collision prediction in more critical physical scenarios, and for the first time demonstrates the robustness of LGMDs inspired visual systems in critical traffic towards a reliable collision alert system under constrained computation power. This paper also exhibits a novel, tractable, and affordable robotic approach to evaluate online visual systems in dynamic scenes.
- Published
- 2021
- Full Text
- View/download PDF
19. Bio-Inspired Computation for Optimizing Scheduling
- Author
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Madan, Mamta, Kacprzyk, Janusz, Series editor, Pal, Nikhil R., Advisory editor, Bello Perez, Rafael, Advisory editor, Corchado, Emilio S., Advisory editor, Hagras, Hani, Advisory editor, Kóczy, László T., Advisory editor, Kreinovich, Vladik, Advisory editor, Lin, Chin-Teng, Advisory editor, Lu, Jie, Advisory editor, Melin, Patricia, Advisory editor, Nedjah, Nadia, Advisory editor, Nguyen, Ngoc Thanh, Advisory editor, Wang, Jun, Advisory editor, Panigrahi, Bijaya Ketan, editor, Hoda, M. N., editor, Sharma, Vinod, editor, and Goel, Shivendra, editor
- Published
- 2018
- Full Text
- View/download PDF
20. Community Detection in Weighted Directed Networks Using Nature-Inspired Heuristics
- Author
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Osaba, Eneko, Del Ser, Javier, Camacho, David, Galvez, Akemi, Iglesias, Andres, Fister, Iztok, Jr., Fister, Iztok, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Yin, Hujun, editor, Camacho, David, editor, Novais, Paulo, editor, and Tallón-Ballesteros, Antonio J., editor
- Published
- 2018
- Full Text
- View/download PDF
21. Sequence generation for learning: a transformation from past to future
- Author
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Rasheed, Fareeha and Wahid, Abdul
- Published
- 2019
- Full Text
- View/download PDF
22. Editorial: Biology-Inspired Engineering and Engineering-Inspired Biology
- Author
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Jan-Matthias Braun, Poramate Manoonpong, and Xiaofeng Xiong
- Subjects
biology-inspired engineering ,engineering-inspired biology ,bio-inspired computation ,bio-inspired sensors ,bio-inspired materials ,bio-inspired structure ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Published
- 2020
- Full Text
- View/download PDF
23. BENCHMARKING BIO-INSPIRED COMPUTATION ALGORITHMS AS WRAPPERS FOR FEATURE SELECTION.
- Author
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BAJER, Drazen, ZORIC, Bruno, DUDJAK, Mario, and MARTINOVIC, Goran
- Subjects
WRAPPERS ,MACHINE learning ,ALGORITHMS ,BENCHMARKING (Management) ,TASK performance ,BIOLOGICALLY inspired computing ,FEATURE selection - Abstract
Reducing the number of features when applying machine learning algorithms may be beneficial not only from the standpoint of computational cost but also of overall quality. Wrapper-based procedures are widely utilised to achieve this. The choice of the wrapper is of utmost importance. Bio-inspired computation algorithms represent a viable choice and are widely adopted. Due to the sheer number of available algorithms, this choice could prove to be somewhat difficult, especially since not all are made equally. The aim of this paper is to explore several optimisers on diverse datasets representing classification problems in order to evaluate their performance and suitability for the task of feature selection. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
24. Hybrid Bees Approach Based on Improved Search Sites Selection by Firefly Algorithm for Solving Complex Continuous Functions.
- Author
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Nemmich, Mohamed Amine, Debbat, Fatima, and Slimane, Mohamed
- Subjects
ALGORITHMS ,BEES algorithm ,CONTINUOUS functions ,MATHEMATICAL optimization ,BEE behavior ,SWARM intelligence ,BEES ,HONEYBEES - Abstract
Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2020
- Full Text
- View/download PDF
25. Analysing the effects of various switching probability characteristics in flower pollination algorithm for solving unconstrained function minimization problems.
- Author
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Ozsoydan, Fehmi Burcin and Baykasoglu, Adil
- Subjects
- *
ALGORITHMS , *SEARCH algorithms , *PROBABILITY theory , *FLOWERS , *PROBLEM solving - Abstract
Due to their unique offerings, bio-inspired algorithms have become popular in problem solving. Flower pollination algorithm (FPA), which is relatively a new member of this family, is shown to be one promising algorithm and this optimizer is still open to possible enhancements. One of the reasons that adds to the popularity of FPA is indeed the simplicity in implementation. It has two basic procedures, namely global and local pollination, which correspond to global and local search, respectively. Moreover, a single parameter, referred to as switching probability, puts control on these search procedures. Thus, the mentioned switching probability actually defines the search characteristics throughout generations, which directly affects the success of FPA. Accordingly, the present work analyses the effects of various switching probability characteristics, including exponentially, linearly and sawtooth changing patterns. This is the main motivation of the present study. Secondarily, a systematically intensifying step size procedure, which is commonly ignored by most of the stochastic search algorithms, is adopted along with these strategies. The aim of the proposed step size function is to encourage a more intensified search towards the end, while providing a more diversified search at the initialization stage to avoid local optima and premature convergence. Thus, more promising results might be obtained. All developed modifications are tested by using well-known unconstrained function minimization problems. As demonstrated by several nonparametric statistical tests, results point out significant improvements over the standard FPA. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
26. Parkinson's Disease Detection Using Biogeography-Based Optimization.
- Author
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Hessam, Somayeh, Vahdat, Shaghayegh, Asl, Irvan Masoudi, Kazemipoor, Mahnaz, Aghaei, Atefeh, Shamshirband, Shahaboddin, and Rabczuk, Timon
- Subjects
PARKINSON'S disease ,NERVOUS system - Abstract
In recent years, Parkinson's Disease (PD) as a progressive syndrome of the nervous system has become highly prevalent worldwide. In this study, a novel hybrid technique established by integrating a Multi-layer Perceptron Neural Network (MLP) with the Biogeography-based Optimization (BBO) to classify PD based on a series of biomedical voice measurements. BBO is employed to determine the optimal MLP parameters and boost prediction accuracy. The inputs comprised of 22 biomedical voice measurements. The proposed approach detects two PD statuses: 0-disease status and 1-good control status. The performance of proposed methods compared with PSO, GA, ACO and ES method. The outcomes affirm that the MLP-BBO model exhibits higher precision and suitability for PD detection. The proposed diagnosis system as a type of speech algorithm detects early Parkinson's symptoms, and consequently, it served as a promising new robust tool with excellent PD diagnosis performance. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
27. Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions
- Author
-
Torre-Bastida, Ana I., Díaz-de-Arcaya, Josu, Osaba, Eneko, Muhammad, Khan, Camacho, David, and Del Ser, Javier
- Published
- 2021
- Full Text
- View/download PDF
28. Bio-inspired computation: Where we stand and what's next.
- Author
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Del Ser, Javier, Osaba, Eneko, Molina, Daniel, Yang, Xin-She, Salcedo-Sanz, Sancho, Camacho, David, Das, Swagatam, Suganthan, Ponnuthurai N., Coello Coello, Carlos A., and Herrera, Francisco
- Subjects
SOCIAL facts ,BIOLOGICALLY inspired computing ,SCIENTIFIC community ,MATHEMATICAL optimization ,INDUSTRY 4.0 ,SOCIAL networks - Abstract
In recent years, the research community has witnessed an explosion of literature dealing with the mimicking of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
29. A clustering algorithm applied to the binarization of Swarm intelligence continuous metaheuristics.
- Author
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García, José, Crawford, Broderick, Soto, Ricardo, and Astorga, Gino
- Subjects
SWARM intelligence ,CELLULAR automata ,AGGREGATION (Robotics) ,DISTRIBUTED artificial intelligence ,METAHEURISTIC algorithms - Abstract
Abstract The binarization of Swarm intelligence continuous metaheuristics is an area of great interest in operations research. This interest is mainly due to the application of binarized metaheuristics to combinatorial problems. In this article we propose a general binarization algorithm called K-means Transition Algorithm (KMTA). KMTA uses K-means clustering technique as learning strategy to perform the binarization process. In particular we apply this mechanism to Cuckoo Search and Black Hole metaheuristics to solve the Set Covering Problem (SCP). A methodology is developed to perform the tuning of parameters. We provide necessary experiments to investigate the role of key ingredients of the algorithm. In addition, with the intention of evaluating the behavior of the binarizations while the algorithms are executed, we use the Page's trend test. Finally to demonstrate the efficiency of our proposal, Set Covering benchmark instances of the literature show that KMTA competes clearly with the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
30. Bike sharing demand prediction using artificial immune system and artificial neural network.
- Author
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Chang, Pei-Chann, Wu, Jheng-Long, Xu, Yahui, Zhang, Min, and Lu, Xiao-Yong
- Subjects
- *
BICYCLE sharing programs , *ECONOMIC demand , *PREDICTION models , *IMMUNOCOMPUTERS , *ARTIFICIAL neural networks - Abstract
From the viewpoint of bike sharing service, the rental number is a critical performance indicator for managers and controllers to assess the demand. Bike demand prediction in bike sharing systems is hence a key indicator in economic systems. In this study, a novel prediction framework integrating AIS and the artificial neural network forecasting technique is developed for numerical predication; it is named AIS-ANN. In this proposed AIS-ANN prediction framework, there are three major mechanisms applied to build the predication system which includes cell creation by ANN, antibody generation by clonal selection, and antibody's center adaption by similarity measuring. The experimental results show that our proposed AIS-ANN has better performance when compared with other 6 forecasting models. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
31. Editorial: Biology-Inspired Engineering and Engineering-Inspired Biology.
- Author
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Braun, Jan-Matthias, Manoonpong, Poramate, and Xiong, Xiaofeng
- Subjects
BIOENGINEERING ,INSECT locomotion ,SYNTHETIC biology ,CENTRAL pattern generators ,ROBOT hands ,BIONICS - Abstract
Keywords: biology-inspired engineering; engineering-inspired biology; bio-inspired computation; bio-inspired sensors; bio-inspired materials; bio-inspired structure EN biology-inspired engineering engineering-inspired biology bio-inspired computation bio-inspired sensors bio-inspired materials bio-inspired structure N.PAG N.PAG 3 11/17/20 20201113 NES 201113 The term biology-inspired engineering refers to the fact that biology has been an important inspiration for developments in all aspects of engineering, for example the design of robots. This special issue reports the results and reviews of biology-inspired engineering and engineering-inspired biology research. The results suggest that Lilibot can be considered as a friendly and generic quadrupedal platform for biology-inspired engineering and engineering-inspired biology studies. Biology-inspired engineering, engineering-inspired biology, bio-inspired computation, bio-inspired sensors, bio-inspired materials, bio-inspired structure. [Extracted from the article]
- Published
- 2020
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32. On the Idea of a New Artificial Intelligence Based Optimization Algorithm Inspired From the Nature of Vortex
- Author
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Utku Kose and Ahmet Arslan
- Subjects
artificial intelligence ,optimization ,bio-inspired computation ,swarm intelligence ,evolutional computation ,vortex optimization algorithm ,vortex ,Neurology. Diseases of the nervous system ,RC346-429 ,Electronic computers. Computer science ,QA75.5-76.95 - Abstract
In this paper, the idea of a new artificial intelligence based optimization algorithm, which is inspired from the nature of vortex, has been provided briefly. As also a bio-inspired computation algorithm, the idea is generally focused on a typical vortex flow / behavior in nature and inspires from some dynamics that are occurred in the sense of vortex nature. Briefly, the algorithm is also a swarm-oriented evolutional problem solution approach; because it includes many methods related to elimination of weak swarm members and trying to improve the solution process by supporting the solution space via new swarm members. In order have better idea about success of the algorithm; it has been tested via some benchmark functions. At this point, the obtained results show that the algorithm can be an alternative to the literature in terms of single-objective optimizationsolution ways. Vortex Optimization Algorithm (VOA) is the name suggestion by the authors; for this new idea of intelligent optimization approach.
- Published
- 2015
33. Systemic Computation Using Graphics Processors
- Author
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Rouhipour, Marjan, Bentley, Peter J, Shayani, Hooman, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Tempesti, Gianluca, editor, Tyrrell, Andy M., editor, and Miller, Julian F., editor
- Published
- 2010
- Full Text
- View/download PDF
34. Tug-of-War Model for Multi-armed Bandit Problem
- Author
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Kim, Song-Ju, Aono, Masashi, Hara, Masahiko, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Calude, Cristian S., editor, Hagiya, Masami, editor, Morita, Kenichi, editor, Rozenberg, Grzegorz, editor, and Timmis, Jon, editor
- Published
- 2010
- Full Text
- View/download PDF
35. Hybrid bio-Inspired computational intelligence techniques for solving power system optimization problems: A comprehensive survey.
- Author
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Rahman, Imran and Mohamad-Saleh, Junita
- Subjects
BIOLOGICALLY inspired computing ,COMPUTATIONAL intelligence ,MATHEMATICAL optimization ,ELECTRIC power systems ,PROBLEM solving - Abstract
Optimization problems of modern day power system are very challenging to resolve because of its design complexity, wide geographical dispersion and influence from many unpredictable factors. For that reason, it is essential to apply most effective optimization techniques by taking full benefits of simplified formulation and execution of a particular problem. This study presents a summary of significant hybrid bio-inspired computational intelligence (CI) techniques utilized for power system optimization. Authors have reviewed an extensive range of hybrid CI techniques and examined the motivations behind their improvements. Various applications of hybrid bio-inspired CI algorithms have been highlighted in this paper. In addition, few drawbacks regarding the hybrid CI algorithms are explained. Current trends in CI techniques from the past researches have also been discussed in the domain of power system optimization. Lastly, some future research directions are suggested for further advancement of hybrid techniques. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
36. Multilayer embedded bat algorithm for B-spline curve reconstruction.
- Author
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Iglesias, Andrés, Gálvez, Akemi, and Collantes, Marta
- Subjects
- *
METAHEURISTIC algorithms , *MULTILAYERS , *SPLINES , *DATA recovery , *LEAST squares - Abstract
This paper presents a new method called multilayer embedded bat algorithm (ME-BAT) to solve the general curve reconstruction problem with free-form parametric B-splines. Opposed to previous approaches in the literature, this method computes the optimal values of all free variables (data parameters, breakpoints, and poles), a very difficult task because they are strongly intertwined in a highly nonlinear way. The method is based on the idea of applying the bat algorithm at different layers: a main bat algorithm at an upper layer to compute the breakpoints and a second bat algorithm at a lower layer to compute the data parameters. This second bat algorithm is embedded into the first one and executed for each breakpoint vector of the population and at each iteration step of the main algorithm. Then, the poles are calculated by least-squares minimization through SVD. The method has been applied to three real-world engineering examples. The experimental results show that the method performs very well, being able to recover the underlying shape of data with high accuracy. A comparison with eleven alternative methods (including six classical methods in the field and all the metaheuristic methods applied so far to this problem) shows that this method outperforms the previous approaches in the field for all instances in the benchmark. [ABSTRACT FROM AUTHOR]
- Published
- 2017
- Full Text
- View/download PDF
37. Chaos and Budworm Dynamics of Agent Interactions: A Biologically-Inspired Approach to Digital Ecosystems
- Author
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Lopardo, Gabriel Alejandro, Rateb, Fátima N., Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Gelbukh, Alexander, editor, and Morales, Eduardo F., editor
- Published
- 2008
- Full Text
- View/download PDF
38. Evolutionary continuous optimization of hybrid Gene Regulatory Networks
- Author
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Michelucci, Romain, Comet, Jean-Paul, Pallez, Denis, and Pallez, Denis
- Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI] ,Continuous single-objective optimization ,Real-world application ,[INFO.INFO-RO] Computer Science [cs]/Operations Research [cs.RO] ,Bio-inspired computation ,Continuous single-objective optimization Fitness formulation hybrid GRN Real-world application Bio-inspired computation ,hybrid GRN ,[INFO.INFO-LG] Computer Science [cs]/Machine Learning [cs.LG] ,Fitness formulation - Abstract
The study of gene regulatory networks (GRNs) allows us to better understand biological systems such as the adaptation of the organism to a disturbance in the environment. Hybrid GRNs (hGRNs) are of interest because they integrate the continuous time evolution in GRN modeling which is convenient in biology. This study focuses on the problem of identifying the variables of hGRN models. In a large-scale case, previous work using constraint-based programming has failed to solve the minimal constraints on such variables which reflect the biological knowledge on the system behavior. In this work, we propose to transform a Constraint Satisfaction Problem (CSP) into a Free Optimization Problem (FOP) by formulating an adequate fitness function and validate the approach on an abstract model of the circadian cycle. We compare several continuous optimization algorithms and show that these first experimental results are in agreement with the specifications coming from biological expertise: evolutionary algorithms are able to identify a solution equivalent to the ones found by continuous constraint solvers.
- Published
- 2022
39. BENCHMARKING BIO-INSPIRED COMPUTATION ALGORITHMS AS WRAPPERS FOR FEATURE SELECTION
- Author
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Bruno Zoric, Goran Martinović, Drazen Bajer, and Mario Dudjak
- Subjects
Polymers and Plastics ,Computer science ,business.industry ,Computation ,bio-inspired computation ,Feature selection ,Benchmarking ,Machine learning ,computer.software_genre ,Industrial and Manufacturing Engineering ,classification ,dimensionality reduction ,feature selection ,wrapper model ,Artificial intelligence ,lcsh:Electrical engineering. Electronics. Nuclear engineering ,Business and International Management ,business ,computer ,lcsh:TK1-9971 - Abstract
Reducing the number of features when applying machine learning algorithms may be beneficial not only from the standpoint of computational cost but also of overall quality. Wrapper-based procedures are widely utilised to achieve this. The choice of the wrapper is of utmost importance. Bio-inspired computation algorithms represent a viable choice and are widely adopted. Due to the sheer number of available algorithms, this choice could prove to be somewhat difficult, especially since not all are made equally. The aim of this paper is to explore several optimisers on diverse datasets representing classification problems in order to evaluate their performance and suitability for the task of feature selection.
- Published
- 2020
40. Critical mass in the emergence of collective intelligence: a parallelized simulation of swarms in noisy environments.
- Author
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Drozd, Aleksandr, Witkowski, Olaf, Matsuoka, Satoshi, and Ikegami, Takashi
- Abstract
We extend an abstract agent-based swarming model based on the evolution of neural network controllers, to explore further the emergence of swarming. Our model is grounded in the ecological situation, in which agents can access some information from the environment about the resource location, but through a noisy channel. Swarming critically improves the efficiency of group foraging, by allowing agents to reach resource areas much more easily by correcting individual mistakes in group dynamics. As high levels of noise may make the emergence of collective behavior depend on a critical mass of agents, it is crucial to reach sufficient computing power to allow for the evolution of the whole set of dynamics in simulation. Since simulating neural controllers and information exchanges between agents are computationally intensive, to scale up simulations to model critical masses of individuals, the implementation requires careful optimization. We apply techniques from astrophysics known as treecodes to compute the signal propagation, and efficiently parallelize for multi-core architectures. Our results open up future research on signal-based emergent collective behavior as a valid collective strategy for uninformed search over a domain space. [ABSTRACT FROM AUTHOR]
- Published
- 2016
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- View/download PDF
41. MRoCS: A new multi-robot communication system based on passive action recognition.
- Author
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Das, Barnali, Couceiro, Micael S., and Vargas, Patricia A.
- Subjects
- *
ROBOTICS , *PATTERN recognition systems , *ROBOT motion , *COMPUTER simulation , *DRONE aircraft , *AGGREGATION (Robotics) - Abstract
Multi-robot search-and-rescue missions often face major challenges in adverse environments due to the limitations of traditional implicit and explicit communication. This paper proposes a novel multi-robot communication system (MRoCS), which uses a passive action recognition technique that overcomes the shortcomings of traditional models. The proposed MRoCS relies on individual motion, by mimicking the waggle dance of honey bees and thus forming and recognising different patterns accordingly. The system was successfully designed and implemented in simulation and with real robots. Experimental results show that, the pattern recognition process successfully reported high sensitivity with good precision in all cases for three different patterns thus corroborating our hypothesis. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
42. Bacterially inspired evolution of intelligent systems under constantly changing environments.
- Author
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Barrios Rolanía, D., Font, J., and Manrique, D.
- Subjects
- *
BIOLOGICALLY inspired computing , *EVOLUTIONARY computation , *NATURAL computation , *ADAPTIVE control systems , *FAULT tolerance (Engineering) , *ROBUST control - Abstract
This paper explores the capabilities of open-ended bio-inspired evolutionary construction of intelligent systems under changing environments. We present and analyze extensive results of the bacterial evolutionary system. This system creates 3D environments that simulate real constantly changing environments. Populations of artificial bacteria constantly evolve their inner biological processes in these environments as they perform every action programmed in their life cycle. This results in a decentralized, asynchronous, parallel and self-adapting general-purpose evolutionary process whose only goal is the survival of the bacterial population under successive, continuously changing environmental conditions. Results show the problem independence and general-purpose capabilities of the system by making it evolve fuzzy rule-based systems under different environments. Robustness and fault tolerance capabilities are also tested by subjecting the bacterial evolutionary system to sudden changes in the environment. Evolution is open-ended as there is no need to restart the system when changes take place. Artificial bacteria self-adapt themselves in real time in order to guarantee their survival. [ABSTRACT FROM AUTHOR]
- Published
- 2015
- Full Text
- View/download PDF
43. On the Idea of a New Artificial Intelligence Based Optimization Algorithm Inspired From the Nature of Vortex.
- Author
-
Kose, Utku and Arslan, Ahmet
- Subjects
- *
ARTIFICIAL intelligence , *MATHEMATICAL optimization - Abstract
In this paper, the idea of a new artificial intelligence based optimization algorithm, which is inspired from the nature of vortex, has been provided briefly. As also a bio-inspired computation algorithm, the idea is generally focused on a typical vortex flow / behavior in nature and inspires from some dynamics that are occurred in the sense of vortex nature. Briefly, the algorithm is also a swarm-oriented evolutional problem solution approach; because it includes many methods related to elimination of weak swarm members and trying to improve the solution process by supporting the solution space via new swarm members. In order have better idea about success of the algorithm; it has been tested via some benchmark functions. At this point, the obtained results show that the algorithm can be an alternative to the literature in terms of single-objective optimization solution ways. Vortex Optimization Algorithm (VOA) is the name suggestion by the authors; for this new idea of intelligent optimization approach. [ABSTRACT FROM AUTHOR]
- Published
- 2014
44. Dealing QAP & KSP with Green Heron optimization algorithm — A new bio-inspired meta-heuristic.
- Author
-
Sur, Chiranjib and Shukla, Anupam
- Abstract
In this paper a new biological phenomenon following meta-heuristics called Green Heron Optimization Algorithm (GHOA) is being discussed, for the first time, which acquired its inspiration from the Green Heron birds, their intelligence, perception analysis capability and technique for food acquisition. The natural phenomenon of the bird has been capped into some unique operations which favour the graph based and discrete combinatorial optimization problems but with slight modification can also be utilized for other wide variety of problems of the real world which have discrete representation of data and variables having several constraints. In this work we have mainly concentrated on the description, mathematical representations, presentations, features, limitations and performance analysis of the algorithm on the scattered dimensional datasets of the Quadratic Assignment Problem (QAP) & 0/1 Knapsack Problem (KSP) to clearly demarcate its performance with change in dimension that is scalability. The results of the simulation clearly reveal how the algorithm has worked optimally for the various datasets of the problem. GHOA is one of the few members in the discrete domain algorithms of the bio-inspired computation family which favours suitably the graph based problems like path planning, process scheduling etc and has the capability of recombination and local search for global optimization and refinement of the solutions. [ABSTRACT FROM PUBLISHER]
- Published
- 2013
- Full Text
- View/download PDF
45. Memristors for energy-efficient, bioinspired processing.
- Author
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Georgiou, Julius and Kyriakides, Evripides
- Abstract
Although memristors have been around, both theoretically and physically for a long time, they have not really been exploited in today's computational infrastructure. In this paper we demonstrate how a loosely memristive NiTi structure can be used to implement synaptic and other neural components that are required in the next generation of neuromorphic systems. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
- Full Text
- View/download PDF
46. Bio-inspired and Voronoi-based algorithms for self-positioning autonomous mobile nodes.
- Author
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Zou, Jianmin, Kusyk, Janusz, Uyar, M. Umit, Gundry, Stephen, and Sahin, Ceni Safak
- Abstract
We introduce two new self-positioning techniques for autonomous nodes in a mobile ad hoc network to spread over unknown two-dimensional deployment terrains. In our first node self-spreading algorithm, called NSVA, each node moves according to the Voronoi tessellation of its sensing area. Our second self-positioning technique, called NSVGA, is based on a genetic algorithm that utilizes the area of moving node's Voronoi cell as a fitness function. To establish a basis for our comparisons, we also include the results for nodes moving to the next positions by means of the distributed self-spreading algorithm, called DSSA. We present formal analysis of NSVA, NSVGA, and DSSA to evaluate the area covered by all nodes (NAC) and the average distance traveled (ADT) by nodes until a desired network topology is reached. Simulation experiments demonstrate that both NSVA and NSVGA perform well with respect to NAC, ADT, and convergence speed. Our NSVGA is able to improve NAC considerably faster in the initial steps of the experiments than NSVA and DSSA. On the other hand, a node running NSVA travels a shorter distance on the average than a NSVGA node before reaching a desired network topology. We show that our NSVA and NSVGA are good candidates for self-spreading autonomous nodes that provide power-efficient solutions for many military and civilian applications. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
- Full Text
- View/download PDF
47. A novel meta-heuristic algorithm: Dynamic Virtual Bats Algorithm.
- Author
-
Topal, Ali Osman and Altun, Oguz
- Subjects
- *
METAHEURISTIC algorithms , *WAVELENGTHS , *SOUND waves , *PROBABILITY theory , *CONSTRAINED optimization - Abstract
Nature-inspired algorithms are a very important part of meta-heuristics. A novel nature inspired algorithm called the Dynamic Virtual Bats Algorithm (DVBA) is presented in this paper. DVBA is inspired by a bat’s ability to manipulate frequency and wavelength of the emitted sound waves when hunting. A role based search has been developed to improve the diversification and intensification capability of Bat Algorithm. In the DVBA, there are only two bats: explorer and exploiter bat. While the explorer bat explores the search space, the exploiter bat makes an intensive search of the local with the highest probability of locating the desired target. Depending on their location, bats exchange the roles dynamically. The performance of the DVBA is extensively evaluated on a suite of 30 bound-constrained optimization problems from CEC 2014 and compared favorably with other well-known meta-heuristics algorithms. The experimental results demonstrated that the proposed DVBA outperform, or is comparable to, its competitors in terms of the quality of final solution and its convergence rates. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
48. Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions
- Author
-
David Camacho, Eneko Osaba, Josu Díaz-de-Arcaya, Ana I. Torre-Bastida, Javier Del Ser, and Khan Muhammad
- Subjects
Process (engineering) ,business.industry ,Computer science ,Big data ,Swarm intelligence ,Context (language use) ,Evolutionary computation ,Data fusion ,Sensor fusion ,Data science ,Field (computer science) ,Visualization ,Fuzzy logic ,Identification (information) ,Artificial Intelligence ,S.I. : Data Fusion in the era of Data Science ,Bio-inspired computation ,business ,Software ,Neural networks - Abstract
This overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research. This work has received funding support from the Basque Government (Eusko Jaurlaritza) through the Consolidated Research Group MATHMODE (IT1294-19), EMAITEK and ELK ARTEK programs. D. Camacho also acknowledges support from the Spanish Ministry of Science and Education under PID2020-117263GB-100 grant (FightDIS), the Comunidad Autonoma de Madrid under S2018/TCS-4566 grant (CYNAMON), and the CHIST ERA 2017 BDSI PACMEL Project (PCI2019-103623, Spain).
- Published
- 2020
49. ARTIFICIAL ENDOCRINE SYSTEM: A NEW PARADIGM OF KNOWLEDGE DISCOVERY.
- Author
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PANDEY, SUBHASH CHANDRA and NANDI, GORA CHAND
- Subjects
ARTIFICIAL organs ,ENDOCRINE system ,ARTIFICIAL intelligence ,DATA mining ,DECISION making ,MATHEMATICAL models - Abstract
We propose an artificial endocrine system (AES) for extracting the knowledge from database so that effective and reliable decision rules can be constructed. The proposed AES mimics the functionality of biological endocrine system (BES) to some extent. A mathematical model is proposed for expressive representation of endocrine system as well as for homeostasis. Further, different aspects of our proposed "Artificial endocrine system for knowledge discovery" (AESKD) have been compared with state of art classifiers e.g., support vector machine, neural network, radial basis function (RBF) network and K-NN for some bench mark datasets. [ABSTRACT FROM AUTHOR]
- Published
- 2013
- Full Text
- View/download PDF
50. Bacterially inspired evolving system with an application to time series prediction.
- Author
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Rolanía, D. Barrios, Font, J.M., and Manrique, D.
- Subjects
TIME series analysis ,PREDICTION theory ,SYNTHETIC biology ,SILICON ,BACTERIAL population ,EVOLUTIONARY computation - Abstract
Abstract: This paper explores the synergies between evolutionary computation and synthetic biology, developing an in silico evolutionary system that is inspired by the behavior of bacterial populations living in continuously changing environments. This system creates a 3D environment seeded with a simulated population of bacteria that eat, reproduce, interact with each other and with the environment and eventually die. This provides a 3D framework implementing an evolutionary process. The subject of the evolution is each bacterium''s internal process, defining its interactions with the environment. The evolutionary goal is the survival of the population under successive, continuously changing environmental conditions. The key advantage of this bacterial evolutionary system is its decentralized, asynchronous, parallel and self-adapting general-purpose evolutionary process. We describe this system and present the results of an application to the evolution of a bacterial population that learns how to predict the presence or absence of food in the environment by analyzing three input signals from the environment. The resulting populations successfully evolve by continuously improving their fitness under different environmental conditions, demonstrating their adaptability to a fluctuating medium. [Copyright &y& Elsevier]
- Published
- 2013
- Full Text
- View/download PDF
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