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Chaotic metaheuristic algorithms for learning and reproduction of robot motion trajectories

NEURAL COMPUTING & APPLICATIONS 2018年第4期30卷 1065-1083页

作者： Mitic, Marko Vukovic, Najdan Petrovic, Milica Miljkovic, Zoran Univ Belgrade Fac Mech Engn Prod Engn Dept Kraljice Marije 16 Belgrade 11120 35 Serbia Univ Belgrade Fac Mech Engn Innovat Ctr Kraljice Marije 16 Belgrade 11120 35 Serbia

Most of today's mobile robots operate in controlled environments prone to various unpredictable conditions. Programming or reprogramming of such systems is time-consuming and requires significant efforts by number of experts. One of the solutions to this problem is to enable the robot to learn from human teacher through demonstrations or observations. This paper presents novel approach that integrates Learning from Demonstrations methodology and chaotic bioinspired optimization algorithms for reproduction of desired motion trajectories. Demonstrations of the different trajectories to reproduce are gathered by human teacher while teleoperating the mobile robot in working environment. The learning (optimization) goal is to produce such sequence of mobile robot actuator commands that generate minimal error in the final robot pose. Four different chaotic methods are implemented, namely chaotic Bat Algorithm, chaotic Firefly Algorithm, chaotic Accelerated Particle Swarm Optimization and newly developed chaotic Grey Wolf Optimizer (CGWO). In order to determine the best map for CGWO, this algorithm is tested on ten benchmark problems using ten well-known chaotic maps. Simulations compare aforementioned algorithms in reproduction of two complex motion trajectories with different length and shape. Moreover, these tests include variation of population in swarm and demonstration examples. Real-world experiment on a nonholonomic mobile robot in indoor environment proves the applicability of the proposed approach.

关键词： bioinspired algorithms Metaheuristic optimization Chaos Mobile robot Learning from Demonstration Grey Wolf Optimizer Particle Swarm Optimization Bat Algorithm Firefly Algorithm

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VMMISD: An Efficient Load Balancing Model for Virtual Machine Migrations via Fused Metaheuristics With Iterative Security Measures and Deep Learning Optimizations

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IEEE ACCESS 2024年 12卷 39351-39374页

作者： Brahmam, Madala Guru Anand, Vijay R. Vellore Inst Technol Sch Comp Sci Engn & Informat Syst Vellore 632014 Tamil Nadu India

As cloud computing continues to evolve, the need for efficient and secure management of virtual machine (VM) migrations has become increasingly evident. Traditional models often fall short in optimizing load balancing and energy consumption while ensuring a high level of security. In this work, we propose the load balancing and energy-efficient migration model, an innovative approach that leverages bioinspired algorithms and advanced security measures to optimize VM migrations. The initial novelty of our model is the integration of Genetic algorithms with Ant Colony Optimization for resource scheduling operations. These algorithms were specifically chosen for their proven effectiveness in solving complex optimization problems by simulating natural processes. Additionally, our model incorporates a deep reinforcement learning-based iterative-learning contextual side chaining model to enhance security measures. This approach not only learns and adapts to new security threats over time but also utilizes contextual side-chaining to link related security events, thereby providing a robust defense mechanism against potential threats. The affinity between VMs and physical machines is quantified using K Means clustering and fuzzy logic, which ensures optimal load balancing while accounting for the uncertainty inherent in the migration process. Furthermore, we employ bidirectional long short-term memory networks with recurrent graph neural network, for accurate workload prediction and informed migration decision making process. The selection of these techniques is grounded in their proven capability to analyze historical data and predict future trends with high accuracy levels. Our proposed model demonstrates marked improvements in several key performance metrics. We achieved a 4.5% reduction in makespan, a 4.9% increase in deadline hit ratio, and a 3.9% improvement in task diversity. Furthermore, computational complexity was reduced by 8.3%, VM migration efficiency improved

关键词： Virtual machine migrations bioinspired algorithms advanced security measures machine learning techniques cloud computing optimization

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Lens-Oppositional Wild Geese Optimization Based Clustering Scheme for Wireless Sensor Networks Assists Real Time Disaster Management

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Computer Systems Science & Engineering 2023年第7期46卷 835-851页

作者： R.Surendran Youseef Alotaibi Ahmad F.Subahi Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennaiIndia Department of Computer Science College of Computer and Information SystemsUmm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia

Recently,wireless sensor networks(WSNs)find their applicability in several real-time applications such as disaster management,military,surveillance,healthcare,*** utilization of WSNs in the disaster monitoring process has gained significant attention among research communities and ***-time monitoring of disaster areas using WSN is a challenging process due to the energy-limited sensor ***,the clustering process can be utilized to improve the energy utilization of the nodes and thereby improve the overall functioning of the *** this aspect,this study proposes a novel Lens-Oppositional Wild Goose Optimization based Energy Aware Clustering(LOWGO-EAC)scheme for WSN-assisted real-time disaster *** major intention of the LOWGO-EAC scheme is to perform effective data collection and transmission processes in disaster *** achieve this,the LOWGOEAC technique derives a novel LOWGO algorithm by the integration of the lens oppositional-based learning(LOBL)concept with the traditional WGO algorithm to improve the convergence *** addition,the LOWGO-EAC technique derives a fitness function involving three input parameters like residual energy(RE),distance to the base station(BS)(DBS),and node degree(ND).The proposed LOWGO-EAC technique can accomplish improved energy efficiency and lifetime of WSNs in real-time disaster management *** experimental validation of the LOWGO-EAC model is carried out and the comparative study reported the enhanced performance of the LOWGO-EAC model over the recent approaches.

关键词： Disaster management real-time applications wireless sensor networks clustering bioinspired algorithms

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A Hybrid bioinspired Approach for Advanced Image Reconstruction

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IEEE ACCESS 2024年 12卷 165948-165962页

作者： Lobato-Larios, Salvador Starostenko, Oleg Alarcon-Aquino, Vicente Univ Sierra Juarez Inst Environm Studies Ixtlan de Juarez 68725 Oaxaca Mexico Univ las Amer Puebla Dept Comp Elect & Mechatron Cholula 72810 Mexico

With the exponential growth of digital imagery, some novel techniques for visual information reconstruction are needed since the development of high-speed and precision methods is still an open problem for several applications such as medical diagnosis, satellite imaging, and general image processing. This paper proposes a new hybrid approach for reconstructing images using Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Wavelet Fusion (WF) techniques. While PSO seeks a solution modeled on the flocking and schooling patterns in birds and fish, GA helps to explore the solution space, by reducing the risk of local minima as well as improving the process of searching by modeling specific natural mechanisms at play in evolution, and WF enhances image quality by lessening noise. This is considered as the major contribution of the work, which lies in the bio-inspired algorithm by integrating swarm intelligence and wavelet fusion techniques applied to multiple initial reconstruction steps for an image to approximate the intended reconstructed one. Experimental results show that this hybrid approach converges fast and gives better reconstruction with a low Mean Squared Error (MSE). The proposed methodology provides a strong foundation for developing image reconstruction techniques by demonstrating that swarm intelligence can be integrated with wavelet-based techniques.

关键词： Image reconstruction Image reconstruction bioinspired algorithms bioinspired algorithms particle swarm optimization particle swarm optimization wavelet fusion wavelet fusion

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OKSP: A Novel Deep Learning Automatic Event Detection Pipeline for Seismic Monitoring in Costa Rica 3

OKSP: A Novel Deep Learning Automatic Event Detection Pipeli...

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3rd IEEE International Conference on bioinspired Processing (BIP)

作者： van der Laat, Leonardo Baldares, Ronald J. L. Chaves, Esteban J. Meneses, Esteban Costa Rica High Technol Ctr San Jose Costa Rica Costa Rica Inst Technol San Jose Costa Rica Natl Univ Volcanol & Seismol Observ Costa Rica Heredia Costa Rica

ISBN: (纸本)9781665427227

Small magnitude earthquakes are the most abundant but the most difficult to locate robustly and well due to their low amplitudes and high frequencies usually obscured by heterogeneous noise sources. They highlight crucial information about the stress state and the spatio-temporal behavior of fault systems during the earthquake cycle, therefore, its full characterization is then crucial for improving earthquake hazard assessment. Modern deep learning algorithms along with the increasing computational power and efficiency are exploiting the continuously growing seismological databases, worldwide, allowing scientists to improve the completeness for earthquake catalogs, systematically detecting and locating smaller magnitude earthquakes and reducing the errors introduced mainly by human intervention through traditional approaches in seismological observatories. In this work, we introduce OKSP, a novel deep learning automatic earthquake detection pipeline for seismic monitoring in Costa Rica. Using Kabre supercomputer from the Costa Rica High Technology Center, we applied OKSP to the day before and the first 5 days following the Puerto Armuelles, M6.5, earthquake that occurred on 26 June, 2019, along the Costa Rica-Panama border and found 1100 more earthquakes previously unidentified by the Volcanological and Seismological Observatory of Costa Rica. From these events, a total of 23 earthquakes with magnitudes below 1.0 occurred a day to hours prior to the mainshock, shedding light about the rupture initiation and earthquake interaction leading to the occurrence of this productive seismic sequence. Our observations show that for the study period, the model was 100% exhaustive and 82% precise, resulting in an F1 score of 0.90. This effort represents the very first attempt for automatically detecting earthquakes in Costa Rica using deep learning methods and demonstrates that, in the near future, earthquake monitoring routines will be carried out entirely by AI algorithms.

关键词： deep learning automatic earthquake detection phase picking foreshock mainshock aftershock bioinspired algorithms

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Parallel Hybrid Genetic Algorithm for Solving Design and Optimization Problems

Parallel Hybrid Genetic Algorithm for Solving Design and Opt...

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International Symposium on Computer Science, Digital Economy and Intelligent Systems (CSDEIS)

作者： Gladkov, L. A. Gladkova, N., V Semushin, E. Y. Southern Fed Univ Taganrog Russia

ISBN: (纸本)9783030392161;9783030392154

The paper considers a problem of building the hybrid algorithm for solving the optimization design tasks on the basis of integration of different methods of computation intelligence. The authors describe the definition and the main approaches to building the hybrid systems and demonstrate the possibilities of integration of the evolutionary design and multi-agent systems methods The different approaches to evolutionary design of the agents are considered. Different methods of parallelizing the computational process and the main models of parallel genetic algorithms, their benefits and shortcomings are described and analyzed in the paper. A hybrid parallel genetic algorithm for searching and optimization of the design decisions is developed in the paper. The algorithm is implemented as software subsystem and investigated in terms of its effectiveness.

关键词： Design tasks bioinspired algorithms Hybrid methods Parallel genetic algorithm Multiagent systems

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FaiNet: An Immune Algorithm for Fuzzy Clustering

FaiNet: An Immune Algorithm for Fuzzy Clustering

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Szabo, Alexandre de Castro, Leandro Nunes Delgado, Myriam Regattieri Univ Prebiteriana Mackenzie Nat Comp Lab LCoN Sao Paulo Brazil Univ Tecnol Fed Parana Curitiba Parana Brazil

ISBN: (纸本)9781467315067

Data clustering is useful in several areas, such as web mining, biology, climate, medical diagnosis, computer vision, marketing and others. Thus, in real problems, data can simultaneously belong to more than one cluster, being necessary to use fuzzy clustering concepts as decision mechanisms to assign data into clusters. Moreover, nature-based intelligent mechanisms have been used to increase the effectiveness of several machine learning algorithms. This paper proposes improvements on aiNet (Artificial Immune Network), a bioinspired clustering algorithm, and its extension to be applied to fuzzy partitions. The modified algorithm to be applied in fuzzy partitions was thus named FaiNet (Fuzzy aiNet). It uses immune system concepts to allow it to automatically detect a suitable number of clusters in the datasets, what is not possible for most clustering algorithms. FaiNet was applied to seven databases from the literature with the purpose of benchmarking and its performance was compared with that of Fuzzy C-Means, a Fuzzy particle swarm clustering algorithm (FPSC) and the improved crisp aiNet. Purity and Entropy were the main metrics used to evaluate performance. The FaiNet algorithm showed to be competitive with the other algorithms used for comparison.

关键词： fuzzy clustering artificial immune system dynamic population bioinspired algorithms

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Using Swarm Intelligence in Unmanned Aerial Vehicles for Unknown Location Fixed Target Search 10

Using Swarm Intelligence in Unmanned Aerial Vehicles for Unk...

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10th Euro-American Conference on Telematics and Information Systems (EATIS)

作者： Paula, Patricia de Sousa Ferreira Sarmento, Wellington W. Louis Paillard, Gabriel A. de Castro, Miguel Franklin Univ Fed Ceara Fortaleza Ceara Brazil

ISBN: (纸本)9781450377119

The context of this research is the use of bioinspired algorithms applied to unmanned aerial vehicles (UAV) to search for a fixed target of unknown location. A target can be a lost human being or a broken vehicle, for example. Swarm algorithms used with UAVs can be adapted to perform better than a simple scanning algorithm such as Parallel Path Finder. The Particle Swarm Optimization and Bat Algorithm algorithms are compared using constraints such as UAV battery life and the size of the search area. Thus, the best solution to this problem is shown, among the adapted ones, considering the applied restrictions.

关键词： Swarm intelligence UAV bioinspired algorithms Mobile

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Bacterial Foraging Optimization for VLSI Fragments Placement 2nd

Bacterial Foraging Optimization for VLSI Fragments Placement

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2nd International Scientific Conference on Intelligent Information Technologies for Industry (IITI)

作者： Zaporozhets, Dmitry Zaruba, Daria Southern Fed Univ Rostov Na Donu Russia

ISBN: (纸本)9783319683218

The paper deals with one of the most significant problem of computer design - a VLSI placement problem. The authors prove a relevance of new heuristic methods for the given problem. The VLSI placement problem is formulated and an optimization criterion is suggested. As an optimization method there is suggested an algorithm based on bacterial colony behavior in nature according with environment conditions. On the basis of these observations it is developed a new bioinspired algorithm for the VLSI placement problem. There are conducted series of computational experiments for comparison of the developed algorithm with known VLSI placement algorithms on the basis of IBM benchmarks. Obtained results are represented in a table. Conducted researches shown the efficiency of the suggested approach better then 8-10% on average.

关键词： VLSI Optimization bioinspired algorithms Bacterial foraging optimization IBM benchmarks

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CIP-aiNet: An Entropy-based Immune Network for Multiple Clustering

CIP-aiNet: An Entropy-based Immune Network for Multiple Clus...

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IEEE Congress on Evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Borges, Ederson Coelho, Guilherme Palermo Univ Campinas SP Sch Technol FT Limeira Brazil

ISBN: (纸本)9781509060177

Datasets interpretation can be a hard task due to their complexity and heterogeneity. Several works have dealt with multiple clustering to bring in new information from databases, which differs from traditional approaches that present a single partition as a solution. This work proposes a novel algorithm based on the Immune Network Theory and also in Information Theoretical Learning. This approach employs the Cross-Information Potential (CIP), based on Renyi's Entropy, to evaluate possible solutions and does not impose any assumption about the data distribution. Besides that, the proposed algorithm, named CIP-aiNet, maintains a diverse set of solutions to allow multiple possible interpretations. CIP-aiNet was compared with the minCEntropy algorithm, which also uses Information Theory concepts, and with an Immune Network-based algorithm that uses the Euclidean distance to evaluate the partitions. The novel algorithm presents better solutions for complex datasets compared to the Immune Network-based algorithm, and returns multiple good solutions in a single execution, which improves its outcomes.

关键词： artificial immune systems cross-information potential cluster evaluation function information theoretical learning bioinspired algorithms machine learning

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