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IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus)

作者： Shoshina, Anastasiia, V Borzunov, Georgii, I Ivanova, Ekaterina Y. Natl Res Nucl Univ MEPhI Moscow Russia

ISBN: (纸本)9781665404761

In some cases, the confidentiality of cryptographic algorithms used in digital communication is related to computational complexity mathematical problems, such as calculating the discrete logarithm, the knapsack problem, decomposing a composite number into prime divisors etc. This article describes the application of insolvability of factorization of a large composite number, and reviews previous work integer factorization using either the deterministic or the bio-inspired algorithms. This article focuses on the possibility of using bio-inspired methods to solve the problem of cryptanalysis of asymmetric encryption algorithms, which ones based on factorization of composite numbers. The purpose of this one is to reviewing previous work in integer factorization algorithms, developing a prototype of either the deterministic and the bio-inspired algorithm and the effectiveness of the developed algorithms and recommendations are made for future research paths.

关键词： bio-inspired algorithms integer factorization fitness function genetic algorithm RSA quadratic sieve

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A New Library of bio-inspired algorithms 20th

A New Library of Bio-Inspired Algorithms

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20th International Conference on Computational Science and Its Applications (ICCSA)

作者： Lucca, Natiele Schepke, Claudio Fed Univ Pampa UNIPAMPA Lab Adv Studies Postgrad Program Software Engn Alegrete RS Brazil

ISBN: (纸本)9783030587994;9783030587987

Real engineering, science, and economics problems cannot be ever solved exactly due to the high computation time to find the optimal solution. One way to solve such problems is to apply bio-inspired algorithms, to minimize the time to search for potential solutions. bio-inspired algorithms are based on the collective behavior of social organisms and are used to solve optimization problems. This article presents a new library of bio-inspired algorithms. The library offers the implementation of some algorithms, being easily extensible through interfaces. An evaluation was made using 7 test functions applied to each of the implemented algorithms. The tests have shown that the ABC algorithm obtained the best convergence results in 5 tests and the ACO algorithm in 2 tests.

关键词： bio-inspired algorithms Library Artificial bee colony Ant colony optimization Particle swarm optimization

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Tabu Search vs. bio-inspired algorithms for Antenna Selection in Spatially Correlated Massive MIMO Uplink Channels 24

Tabu Search vs. Bio-inspired Algorithms for Antenna Selectio...

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24th European Signal Processing Conference (EUSIPCO)

作者： Abdullah, Zaid Tsimenidis, Charalampos C. Johnston, Martin Newcastle Univ Sch Elect & Elect Engn Newcastle Upon Tyne NE1 7RU Tyne & Wear England

ISBN: (纸本)9780992862657

Massive Multiple Input Multiple Output (MIMO) systems can significantly improve the system performance and capacity by using a large number of antenna elements at the base station (BS). To reduce the system complexity and hardware cost, low complexity antenna selection techniques can be used to choose the best antenna subset while keeping the system performance at a certain required level. In this paper, Tabu Search (TS) and three bio-inspired optimization algorithms were used for antenna selection in Massive MIMO systems. The three bio-inspired algorithms were: Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Artificial Bee Colony (ABC). Simulations showed promising results for the TS by achieving higher capacity with GA than PSO and ABC, and much shorter CPU time than any of the bio-inspired techniques.

关键词： Massive MIMO Antenna selection bio-inspired algorithms Particle Swarm Optimization (PSO) Genetic algorithm (GA) Artificial Bee colony (ABC) Tabu search (TS)

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Design of Photonic Devices Using bio-inspired algorithms

Design of Photonic Devices Using Bio-Inspired Algorithms

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SBMO/IEEE MTT-S International Microwave and Optoelectronics Conference

作者： Silva-Santos, Carlos H. Claudio, Kleucio Hernandez-Figueroa, Hugo E. Goncalves, Marcos Sergio Univ Estadual Campinas Dept Microwave & Opt FEEC Campinas SP Brazil Univ Estadual Campinas Sch Technol Limeria Brazil

ISBN: (纸本)9781424453566

The design of photonic devices in computer simulators is a complex task and, in some cases, there are not analytical solutions to support. An alternative is the application of some optimizations techniques. For this reason, this work presents three different bio-inspired algorithms (GA, ES and AIS) to optimize three different photonic devices. The main objectives are providing a flexible and efficient computational optimization tool to design different photonic devices and benchmarking the efficiency of each one. In addition, the efficiency results compare the optimization convergence and computational efforts necessary for each optimization.

关键词： Photonic Devices bio-inspired algorithms Optimization Bechmark

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Eight bio-inspired algorithms Evaluated for Solving Optimization Problems 17th

Eight Bio-inspired Algorithms Evaluated for Solving Optimiza...

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17th International Conference on Artificial Intelligence and Soft Computing (ICAISC)

作者： Barbosa, Carlos Eduardo M. Vasconcelos, Germano C. Univ Fed Pernambuco Ctr Informat Recife PE Brazil

ISBN: (纸本)9783319912530;9783319912523

Many bio-inspired algorithms have been proposed to solve optimization problems. However, there is still no conclusive evidence of superiority of particular algorithms in different problems, diverse experimental situations and varied testing scenarios. Here, eight methods are investigated through extensive experimentation in three problems: (1) benchmark functions optimization, (2) wind energy forecasting and (3) data clustering. Genetic algorithms, ant colony optimization, particle swarm optimization, artificial bee colony, firefly algorithm, cuckoo search algorithm, bat algorithm and self-adaptive cuckoo search algorithm are compared, concerning, the quality of solutions according to several performance metrics and convergence to best solution. A bio-inspired technique for automatic parameter tuning was developed to estimate the optimal values for each algorithm, allowing consistent performance comparison. Experiments with thousands of configurations, 12 performance metrics and Friedman and Nemenyi statistical tests consistently evidenced that cuckoo search works efficiently, robustly and superior to the other methods in the vast majority of experiments.

关键词： bio-inspired algorithms Swarm intelligence Optimization Automatic parameter tuning

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Image Enhancement using bio-inspired algorithms on Mammogram for Cancer Detection

Image Enhancement using Bio-Inspired Algorithms on Mammogram...

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International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT)

作者： Sushma, S. J. Kumar, S. C. Prasanna Visvesvaraya Technol Univ Belgavi India RVCE Dept Instrumentat Technol Bangalore Karnataka India

ISBN: (纸本)9781467395632

Image preprocessing and image enhancement plays a critical role in medical image processing. Considering the case study of breast cancer detection, it was found that there are various schemes of optimization techniques which is either training based or leads to recursive iterations leading to computationally complex process. Hence, the proposed system implements a unique and novel optimization technique called as Image Enhancement using bio-inspired algorithms. Different from existing bio-inspired algorithm, the proposed system doesn't use any training sequences, or depends on single fitness function or performs recursive operation for exploring elite population. The algorithm performs automatic segmentation process followed by three level of enhancement operation for achieving local to global best optimization without using any forms of recursive functions. The outcomes are visually defined and well resolution to prove success factor.

关键词： Breast cancer Detection Image Enhancement Segmentation bio-inspired algorithms

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Comparison of bio-inspired algorithms in a Case Study for Optimizing Capacitor Bank Allocation in Electrical Power Distribution 25

Comparison of Bio-Inspired Algorithms in a Case Study for Op...

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25th International Carpathian Control Conference (ICCC)

作者： Neto, Wolmar Araujo dos Santos, Murillo Ferreira Olivi, Leonardo Rocha Costa, Exuperry Barros Mercorelli, Paolo Fed Univ Espirito Santo UFES Vitoria ES Brazil Fed Ctr Technol Educ CEFET Leopoldina Brazil Fed Univ Juiz De Fora UFJF Juiz De Fora Brazil Leuphana Univ Luneburg Inst Prod Technol & Syst IPTS Luneburg Germany

ISBN: (纸本)9798350350708;9798350350715

This paper investigates the Fish School Search (FSS) algorithm's effectiveness for optimizing capacitor placement in electrical distribution networks, focusing on the challenges of increasing demand and aging infrastructures. The study assesses FSS's unique capability to evade local optima, contrasting its performance with established methods like Ant Colony (AC), Constructive Heuristic Algorithm (CHA), and Genetic Algorithm (GA) in various systems, including IEEE-14, IEEE-33, and IEEE-69 buses. Results highlight FSS's superior performance in reducing active losses, using fewer resources and iterations. This research underscores the potential of bio-inspired algorithms in complex power distribution challenges and suggests new directions for algorithmic efficiency and broader application.

关键词： Capacitor Bank Allocation Electrical Distribution System bio-inspired algorithms

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Identity – Attribute Inference in Online Social Network(s) Using bio-inspired algorithms and Machine Learning Approaches

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Journal of Mobile Multimedia 2023年第3期19卷 645-678页

作者： Shetty, Nisha P. Muniyal, Balachandra Teja, Daita Ravi Maben, Leander Melroy Vinil, Tummala Srinag Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal 567104 India Department of Computer Science and Engineering Manipal Institute of Technology Manipal Academy of Higher Education Manipal 567104 India

Twitter is one of the most popular social networking sites today, and it has become a critical tool for gathering data from numerous individuals throughout the world. The platform hosts a variety of debates spanning from current events and news to entertainment, advertising, and technology. In contrast to earlier approaches, the proposed work employs the concept of both direct (via tweets) and indirect stance detection (via homophily elements) to infer sensitive attributes. Along with attribute-based inference, the proposed work also matches user profiles across cross platforms via user-generated posts. Unlike prior efforts, usernames are not included in the feature set here since they are a bit of a giveaway. bio-inspired algorithms are used along with ensemble methods to extract the best set of features. © 2023 River Publishers.

关键词： attribute inference bio-inspired algorithms ensemble homophily Identity inference

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Multi-agent bio-inspired algorithms for Wireless Sensor Network Design 17

Multi-agent Bio-inspired Algorithms for Wireless Sensor Netw...

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17th IEEE INTERNATIONAL CONFERENCE ON ADVANCED COMMUNICATIONS TECHNOLOGY (ICACT)

作者： Mochalov, Vladimir Megaputer Intelligence Moscow Russia

ISBN: (纸本)9788996865056

The process of designing a wireless sensor network (WSN) is rather complicated. This process is not formalized in the form of a hard set of rules, algorithms and standards that guarantee the construction of WSN satisfying different requirements of the designer. This paper discusses the problem of constructing a WSN structure. In the proposed functional diagram of the WSN design we can allocate the place of synthesis WSN structure functional block. bio-inspired algorithms simulate natural processes of self-organization and evolution. The author proposes to use several multi-agent bio-inspired algorithms for synthesis of WSN structure. Fitness function performs multi-objective fuzzy expert evaluation of various WSN parameters. All considered algorithms modify the global pheromone memory. The work shows the results of the synthesis of WSN topology on an object with space constraints. The results illustrate the possibility of using different self-organization animal models to solve some problems arising in the process of building self-organizing wireless sensor networks.

关键词： sensor networks graphs bio-inspired algorithms network synthesis

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Improving software effort estimation using bio-inspired algorithms to select relevant features: An empirical study

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SCIENCE OF COMPUTER PROGRAMMING 2021年 205卷 102621-102621页

作者： Ali, Asad Gravino, Carmine Univ Salerno Dept Comp Sci Via Giovanni Paolo II132 I-84084 Fisciano SA Italy

Context: bio-inspired feature selection algorithms got the attention of the researchers in the domain of Software Development Effort Estimations (SDEE) because they can improve the prediction accuracy of existing estimation techniques, such as machine learning methods. Objective: This paper aims to analyze different feature selection algorithms and assess the role they can play to increase the accuracy of software development effort predictions. Method: We have performed an empirical study considering commonly used bio-inspired feature selection algorithms in the domain of SDEE, i.e., Genetic Algorithm (GA), Particle Swarm Optimization, Ant Colony Optimization, Tabu Search, Harmony Search (HS), and Firefly algorithm, and four traditional non-bio-inspired algorithms, i.e., Best-First Search (BFS), Greedy Stepwise, Subset Forward Selection, and Random Search, used in combination with five widely used estimation techniques and applied to eight widely used SDEE datasets. Results: The performed analysis suggests that almost all (bio-inspired) feature selection algorithms have outperformed the baseline estimation techniques (i.e., techniques employed without any feature selection algorithms) in the majority of the experiments and hence we can conclude that feature selection algorithms can help in the domain of SDEE to increase the prediction accuracy. Similarly, HS and GA are considered as best performed bio-inspired algorithms because they provided significantly better results than the non-bio-inspired algorithms in a greater number of experiments. Moreover, we also compared the results of various employed bio-inspired algorithms, and, again, GA and HS came out as the best performed bio-inspired feature selection algorithms. Conclusion: From our results, if we have to pick feature selection algorithms (from both bio- and non-bio-inspired) and recommend them for future investigations, we would suggest HS because it provided better effort predictions in more combinations o

关键词： bio-inspired algorithms Feature selection Effort estimation Estimation techniques

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