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PAnDA: A Reconfigurable Architecture that Adapts to Physical Substrate Variations

IEEE TRANSACTIONS ON COMPUTERS 2013年第8期62卷 1584-1596页

作者： Walker, James Alfred Trefzer, Martin A. Bale, Simon J. Tyrrell, Andy M. Univ York Dept Elect Intelligent Syst Grp York YO10 5DD N Yorkshire England

Field programmable gate arrays (FPGAs) are widely used in applications where online reconfigurable signal processing is required. Speed and function density of FPGAs are increasing as transistor sizes shrink to the nanoscale. As these transistors reduce in size intrinsic variability becomes more of a problem and to reliably create electronic designs according to specification time consuming statistical simulations become necessary;and even with accurate models and statistical simulation, the fabrication yield will decrease as every physical instance of a design behaves differently. This paper describes an adaptive, evolvable architecture that allows for correction and optimization of circuits directly in hardware using bioinspired techniques. Similar to FPGAs, the programmable analog and digital array (PAnDA) architecture introduced provides a digital configuration layer for circuit design. Accessing additional configuration options of the underlying analog layer enables continuous adjustment of circuit characteristics at runtime, which enables dynamic optimization of the mapped design's performance. Moreover, the yield of devices can be improved postfabrication via reconfiguration of the analog layer, which can overcome faults induced due to variability and process defects. Since optimization goals are generic, i.e., not restricted to reducing stochastic variability, power consumption or increasing speed, the same mechanisms can also enhance the device's fault tolerant abilities in the case of component degradation and failures during its lifetime or when exposed to hazardous environments.

关键词： Reconfigurable architectures intrinsic variability bio-inspired algorithms fault tolerance evolvable hardware adaptive hardware

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Special Issue: bio-inspired Optimization Techniques for High Performance Computing Preface

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NEW GENERATION COMPUTING 2011年第2期29卷 125-128页

作者： Folino, Gianluigi Mastroianni, Carlo ICAR CNR I-87036 Arcavacata Di Rende CS Italy

An introduction is presented in which the editor discusses various reports within the issue on topics including the distributed techniques for optimization problems, the use of ant-based algorithm for mapping Virtual ... 详细信息

关键词： bio-inspired algorithms Optimization Swarm Intelligence Ant Colony Optimization Parallel and Distributed algorithms

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A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings

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COMPUTERS & INDUSTRIAL ENGINEERING 2013年第1期64卷 403-411页

作者： Cortes, Pablo Onieva, Luis Munuzuri, Jesus Guadix, Jose Univ Seville Escuela Tecn Super Ingn Ingn Org Seville 41092 Spain

Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Elevator Lift Viral system bio-inspired algorithms Elevator group control system Vertical transportation

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A bio-inspired hierarchical clustering algorithm with backtracking strategy

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APPLIED INTELLIGENCE 2015年第2期42卷 174-194页

作者： Elkamel, Akil Gzara, Mariem Ben-Abdallah, Hanene Multimedia Informat Syst & Adv Comp Lab Miracl Sfax Tunisia Univ Monastir Higher Sch Comp Sci & Math Monastir Tunisia King Abdulaziz Univ Fac Comp & Informat Technol Jeddah 21413 Saudi Arabia

biological entities, such as birds with their flocking behavior, ants with their social colonies, fish with their shoaling behavior and honey bees with their complex nest construction, represent a great source of inspiration in the optimization and data mining domains. Following this line of thought, we propose the Communicating Ants for Clustering with Backtracking strategy (CACB) algorithm, which is based on a dynamic and an adaptive aggregation threshold and a backtracking strategy where artificial ants are allowed to turn back in their previous aggregation decisions. The CACB algorithm is a hierarchical clustering algorithm that generates compact dendrograms since it allows the aggregation of more than two clusters at a time. Its high performance is experimentally shown through several real benchmark data sets and a content-based image retrieval system.

关键词： Data mining Clustering Hierarchical clustering ACO Ant-based clustering bio-inspired algorithms Artificial intelligence CBIR MPEG-7

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Hybrid ant colony optimization algorithm applied to the multi-depot vehicle routing problem

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NATURAL COMPUTING 2020年第2期19卷 463-475页

作者： Stodola, Petr Univ Def Brno Czech Republic

The article deals with the hybrid Ant Colony Optimization algorithm and its application to the Multi-Depot Vehicle Routing Problem (MDVRP). The algorithm combines both probabilistic and exact techniques. The former implements the bio-inspired approach based on the behaviour of ants in the nature when searching for food together with simulated annealing principles. The latter complements the former. The algorithm explores the search space in a finite number of iterations. In each iteration, the deterministic local optimization process may be used to improve the current solution. Firstly, the key parts and features of the algorithm are presented, especially in connection with the exact optimization process. Next, the article deals with the results of experiments on MDVRP problems conducted to verify the quality of the algorithm;moreover, these results are compared to other state-of-the-art methods. As experiments, Cordreau's benchmark instances were used. The experiments showed that the proposed algorithm overcomes the other methods as it has the smallest average error (the difference between the found solution and the best known solution) on the entire set of benchmark instances.

关键词： Ant colony optimization Local optimization process Multi-depot vehicle routing problem Hybrid algorithms bio-inspired algorithms

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Towards a social and context-aware mobile recommendation system for tourism

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PERVASIVE AND MOBILE COMPUTING 2017年第Part2期38卷 505-515页

作者： Colomo-Palacios, Ricardo Jose Garcia-Penalvo, Francisco Stantchev, Vladimir Misra, Sanjay Ostfold Univ Coll Fac Comp Sci Postboks 700 N-1757 Halden Norway Univ Salamanca Dept Comp Sci Plaza Caidos S-N E-37008 Salamanca Spain SRH Univ Berlin Inst Informat Syst Ernst Reuter Pl 10 D-10587 Berlin Germany Atilim Univ Dept Comp Engn TR-06836 Ankara Turkey

Loyalty in tourism is one of the main concerns for tourist organizations and researchers alike. Recently, technology in general and CRM and social networks in particular have been identified as important enablers for loyalty in tourism. This paper presents POST-VIA 360, a platform devoted to support the whole life-cycle of tourism loyalty after the first visit. The system is designed to collect data from the initial visit by means of pervasive approaches. Once data is analysed, POST-VIA 360 produces accurate after visit data and, once returned, is able to offer relevant recommendations based on positioning and bio-inspired recommender systems. To validate the system, a case study comparing recommendations from the POST-VIA 360 and a group of experts was conducted. Results show that the accuracy of system's recommendations is remarkable compared to previous efforts in the field. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Tourism Customer relationship management bio-inspired algorithms Pervasive Geographic information systems

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A novel Physarum-inspired competition algorithm for discrete multi-objective optimisation problems

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SOFT COMPUTING 2023年第20期27卷 14699-14719页

作者： Awad, Abubakr Coghill, George M. Pang, Wei Univ Nottingham Sch Comp Sci Nottingham England Univ Aberdeen Sch Nat & Comp Sci Aberdeen Scotland Heriot Watt Univ Sch Math & Comp Sci Edinburgh Scotland

Many real-world problems can be naturally formulated as discrete multi-objective optimisation (DMOO) problems. We have proposed a novel Physarum-inspired competition algorithm (PCA) to tackle these DMOO problems. Our algorithm is based on hexagonal cellular automata (CA) as a representation of problem search space and reaction-diffusion systems that control the Physarum motility. Physarum's decision-making power and the discrete properties of CA have made our algorithm a perfectly suitable approach to solve DMOO problems. Each cell in the CA grid will be decoded as a solution (objective function) and will be regarded as a food resource to attract Physarum. The n-dimensional generalisation of the hexagonal CA grid has allowed us to extend the solving capabilities of our PCA from only 2-D to n-D optimisation problems. We have implemented a novel restart procedure to select the global Pareto frontier based on both personal experience and shared information. Extensive experimental and statistical analyses were conducted on several benchmark functions to assess the performance of our PCA against other evolutionary algorithms. As far as we know, this study is the first attempt to assess algorithms that solve DMOO problems, with a large number of benchmark functions and performance indicators. Our PCA has confirmed our assumption that individual skills of competing Physarum are more efficient in exploration and increase the diversity of the solutions. It has achieved the best performance for the Spread indicator (diversity), similar performance results compared to the strength Pareto evolutionary algorithm (SPEA2) and even outperformed other well-established genetic algorithms.

关键词： Discrete multi-objective optimisation Physarum Competition bio-inspired algorithms Evolutionary algorithms

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AI-assisted bio-inspired algorithm for secure IoT communication networks

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第3期25卷 1805-1816页

作者： Alroobaea, Roobaea Arul, Rajakumar Rubaiee, Saeed Alharithi, Fahd S. Tariq, Usman Fan, Xincan Taif Univ Coll Comp & Informat Technol Dept Comp Sci POB 11099 At Taif 21944 Saudi Arabia Vellore Inst Technol VIT Sch Comp Sci & Engn SCOPE Chennai Campus Chennai Tamil Nadu India Univ Jeddah Dept Ind & Syst Engn POB 80327 Jeddah 21589 Saudi Arabia Prince Sattam bin Abdulaziz Univ Coll Comp Engn & Sci Al Kharj 11942 Saudi Arabia Shenzen Polytech Shenzhen 518055 Peoples R China

The Internet of Things (IoT) is made up of intelligent devices that interact with each other. It allows information to be gathered and shared by these devices. In addition, IoT now has a wide range of life applications such as business, logistics, health monitoring, smart ecosystem, and information concerning individuals, social and private environments. Security-related issues involved in such a communication network are drastically increasing day by day. Therefore, existing authentication and authorization concepts must grow along with technological evolution. This paper presents an artificial intelligence (AI)-assisted bio-inspired algorithm for secure IoT communication networks (AI-BIAS) to improve future communication. The proposed framework is categorized into two sections: First, the bio-inspired algorithm-assisted blockchain technology for authentication and authorization in the IoT communication network is presented. Further, the Artificial Intelligence algorithm monitors the proposed IoT communication network. The experimental results show that the proposed model enhances the accuracy ratio of 98.3%, average latency of 26 ms, average cost of 25.7%, authentication time 0.04 ms, multistep prediction's MAPE, and RMSE values are 0.7645 and 0.9878 when compared to other popular methods.

关键词： Internet of Things Artificial intelligence bio-inspired algorithms Authentication Authorization Blockchain

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Hybrid evolutionary algorithms for classification data mining

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NEURAL COMPUTING & APPLICATIONS 2015年第3期26卷 507-523页

作者： Panda, Mrutyunjaya Abraham, Ajith Gandhi Inst Technol Adv GITA Bhubaneswar 752054 Orissa India MIR Labs Sci Network Innovat & Res Excellence Auburn WA 98071 USA

In this paper, we propose novel methods to find the best relevant feature subset using fuzzy rough set-based attribute subset selection with biologically inspired algorithm search such as ant colony and particle swarm optimization and the principles of an evolutionary process. We then propose a hybrid fuzzy rough with K-nearest neighbor (K-NN)-based classifier (FRNN) to classify the patterns in the reduced datasets, obtained from the fuzzy rough bio-inspired algorithm search. While exploring other possible hybrid evolutionary processes, we then conducted experiments considering (i) same feature selection algorithm with support vector machine (SVM) and random forest (RF) classifier;(ii) instance based selection using synthetic minority over-sampling technique with fuzzy rough K-nearest neighbor (K-NN), SVM and RF classifier. The proposed hybrid is subsequently validated using real-life datasets obtained from the University of California, Irvine machine learning repository. Simulation results demonstrate that the proposed hybrid produces good classification accuracy. Finally, parametric and nonparametric statistical tests of significance are carried out to observe consistency of the classifiers.

关键词： Evolutionary algorithms Fuzzy rough Neural network bio-inspired algorithms Classification Statistical test

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A Lion's Pride inspired Algorithm for VLSI Floorplanning

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2020年第1期29卷

作者： Laudis, Lalin L. Ramadass, N. Mar Ephraem Coll Engn & Technol Sch Elect & Commun Engn Marthandam Tamil Nadu India Anna Univ Dept Elect & Commun Engn Chennai Tamil Nadu India

The complexity of any integrated circuit pushes the researchers to optimize the various parameters in the design process. Usually, the Nondeterministic Polynomial problems in the design process of Very Large Scale Integration (VLSI) are considered as a Single Objective Optimization Problem (SOOP). However, due to the increasing demand for the multi-criterion optimization, researchers delve up on Multi-Objective Optimization methodologies to solve a problem with multiple objectives. Moreover, it is evident from the literature that biologically inspired algorithm works very well in optimizing a Multi-Objective Optimization Problem (MOOP). This paper proposes a new Lion's pride inspired algorithm to solve any MOOP. The methodologies mimic the traits of a Lion which always strives to become the Pride Lion. The Algorithm was tested with VLSI floorplanning problem wherein the area and dead space are the objectives. The algorithm was also tested with several standard test problems. The tabulated results justify the ruggedness of the proposed algorithm in solving any MOOP.

关键词： VLSI multi-objective optimization bio-inspired algorithms

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