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27th European Signal Processing Conference (EUSIPCO)

作者： Jimenez-Galindo, Daniel Casaseca-de-la-Higuera, Pablo San-Jose-Revuelta, Luis M. Univ Valladolid Valladolid Spain Univ Valladolid Dept Signal Theory & Commun Valladolid Spain

ISBN: (纸本)9789082797039

The design of both FIR and IIR digital filters is a multi-variable optimization problem, where traditional algorithms fail to obtain optimal solutions. A modified Shuffled Frog Leaping algorithm (SFLA) is here proposed for the design of FIR and IIR discrete-time filters as close as possible to the desired filter frequency response. This algorithm can be considered a type of memetic algorithm. In this paper, simulations prove the obtained filters outperform those designed using the traditional bilinear Z transform (BZT) method with elliptic approximation. Besides, results are close to, and even slightly better, than those reported in recent bio-inspired approaches using algorithms such as particle swarm optimization (PSO), differential evolution (DE) and regularized global optimization (RGA).

关键词： FIR design IIR design shuffled flog-leaping algorithm memetic algorithm bio-inspired algorithm

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Optimization of fuzzy controllers for autonomous mobile robots using the grey wolf optimizer

Optimization of fuzzy controllers for autonomous mobile robo...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Hernandez, Eufronio Castillo, Oscar Soria, Jose Tijuana Inst Technol Div Grad Studies Tijuana Mexico

ISBN: (纸本)9781538617281

Through the advancement of science and technology, every day new methods or computational techniques are emerging that allow to solve problems in widely different areas, such as medicine, engineering, even in any industrial process. Optimization is of vital importance in applications and in industry, the main objective being to find the best possible solution to a particular problem of interest. In this work we propose to use the Grey Wolf Optimizer (GWO), which is a relatively new meta-heuristic, inspired by the hunting behavior and leadership hierarchy of grey wolves, for the optimization of fuzzy controllers for mobile autonomous robots. In addition to analyzing and explaining the proposed methodology based on GWO we are presenting simulation results for a two wheeled autonomous mobile robot to validate the efficiency of the proposed approach.

关键词： Grey wolf optimizer (GWO) autonomous mobile robots fuzzy controllers optimization fuzzy system bio-inspired algorithm

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A New Optimization algorithm and Its Comparison on Traditional Optimization algorithms 38

A New Optimization Algorithm and Its Comparison on Tradition...

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38th Chinese Control Conference (CCC)

作者： Yin Yumin Liao Bolin Li Shuai Jishou Univ Coll Informat Sci & Engn Jishou 416000 Hunan Peoples R China Hong Kong Polytech Univ Dept Comp Hong Kong Peoples R China

ISBN: (纸本)9789881563972

bio-inspired algorithm is a kind of algorithm proposed by simulating biological evolution or social behavior in nature. This kind of algorithm can solve many complex optimization problems, such as assignment problem, knapsack problem, travel path problem and so on, which are difficult to solve by traditional methods. Presently, bio-inspired algorithms have received a significant amount of attention in both academic and engineering societies. This paper presents a new bio-inspired algorithm, termed beetle antennae search (BAS) algorithm. First, inspired by the foraging behavior of beetles, the principle of BAS algorithm is presented. Then, two application examples are conducted to evaluate the effectiveness of BAS algorithm. Importantly, to substantiate the advantage of BAS algorithm, abundant comparative experiments are provided. Experiment results reveal that the BAS algorithm is superior to the other algorithms.

关键词： bio-inspired algorithm Optimization solution BAS algorithm PSO algorithm

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A new fuzzy bee colony optimization with dynamic adaptation of parameters using interval type-2 fuzzy logic for tuning fuzzy controllers

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SOFT COMPUTING 2018年第2期22卷 571-594页

作者： Amador-Angulo, Leticia Castillo, Oscar Tijuana Inst Technol Div Grad Studies Tijuana 22500 BC Mexico

This paper presents a new fuzzy bee colony optimization method to find the optimal distribution of the membership functions in the design of fuzzy controllers for complex nonlinear plants. We used interval type-2 and type-1 fuzzy logic systems in dynamically adapting the alpha and beta parameter values of the bee colony optimization algorithm (BCO). Simulation results with a type-1 fuzzy logic controller for benchmark control plants are presented. The advantage of using interval type-2 fuzzy logic systems for dynamic adjustment of parameters in BCO applied in fuzzy controller design is verified with two benchmark problems. We considered different levels and types of noise in the simulations to analyze the approach of interval type-2 fuzzy logic systems to find the best values of alpha and beta for BCO when applied in the design of fuzzy controllers.

关键词： Interval type-2 fuzzy logic system Fuzzy controller bio-inspired algorithm Footprint uncertainty Bee colony optimization

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A novel method for Bayesian networks structure learning based on Breeding Swarm algorithm

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SOFT COMPUTING 2018年第9期22卷 3049-3060页

作者： Khanteymoori, Ali Reza Olyaee, Mohammad-H. Abbaszadeh, Omid Valian, Maryam Univ Zanjan Dept Comp Engn Zanjan Iran Islamic Azad Univ Karaj Branch Karaj Iran

Bayesian networks (BNs) are widely used as one of the most effective models in bioinformatics, artificial intelligence, text analysis, medical diagnosis, etc. Learning the structure of BNs from data can be viewed as an optimization problem and is proved that this problem is NP-hard. Therefore, heuristic methods can be used as powerful tools to find high-quality networks. In this paper, an interesting approach which is based on Breeding Swarm has been used to learn BNs. Breeding Swarm is a hybrid GA/PSO which enable us to benefits the strengths of particle swarm optimization with genetic algorithms. In order to assess the proposed method, several real-world and benchmark applications are used. Results show that our method is a clear improvement on genetic algorithm and particle swarm optimization.

关键词： Graphical models Bayesian networks Structure learning bio-inspired algorithm Particle Swarm optimization Breeding Swarm

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bio-inspired for Features Optimization and Malware Detection

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2018年第12期43卷 6963-6979页

作者： Ab Razak, Mohd Faizal Anuar, Nor Badrul Othman, Fazidah Firdaus, Ahmad Afifi, Firdaus Salleh, Rosli Univ Malaya Fac Comp Sci & Informat Technol Dept Comp Syst & Technol Kuala Lumpur 50603 Malaysia Univ Malaysia Pahang Fac Comp Syst & Software Engn Kuantan 26300 Pahang Malaysia

The leaking of sensitive data on Android mobile device poses a serious threat to users, and the unscrupulous attack violates the privacy of users. Therefore, an effective Android malware detection system is necessary. However, detecting the attack is challenging due to the similarity of the permissions in malware with those seen in benign applications. This paper aims to evaluate the effectiveness of the machine learning approach for detecting Android malware. In this paper, we applied the bio-inspired algorithm as a feature optimization approach for selecting reliable permission features that able to identify malware attacks. A static analysis technique with machine learning classifier is developed from the permission features noted in the Android mobile device for detecting the malware applications. This technique shows that the use of Android permissions is a potential feature for malware detection. The study compares the bio-inspired algorithm [particle swarm optimization (PSO)] and the evolutionary computation with information gain to find the best features optimization in selecting features. The features were optimized from 378 to 11 by using bio-inspired algorithm: particle swarm optimization (PSO). The evaluation utilizes 5000 Drebin malware samples and 3500 benign samples. In recognizing the Android malware, it appears that AdaBoost is able to achieve good detection accuracy with a true positive rate value of 95.6%, using Android permissions. The results show that particle swarm optimization (PSO) is the best feature optimization approach for selecting features.

关键词： Android Mobile devices bio-inspired algorithm Features optimization Machine learning

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For a refoundation of Artificial Immune System research: AIS is a Design Pattern 8

For a refoundation of Artificial Immune System research: AIS...

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8th IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Parrend, Pierre Guigou, Fabio Navarro, Julio Deruyver, Aline Collet, Pierre ECAM Strasbourg Europe Schiltigheim France Univ Strasbourg ICube Lab UNESCO Unitwin Complex Syst Digital Campus Strasbourg France ICube Lab Strasbourg France IPLine Caluire Et Cuire France Univ Strasbourg ICube Lab Strasbourg France

ISBN: (纸本)9781538692769

Despite the rich research efforts and the expressive power of existing Artificial Immune System models, the community is struggling to devise operational tools that go beyond 'immunity marketing'. We claim that this matter of fact lays in the inadequacy between the analysis metaphors used for AIS algorithms and the actual requirements of distributed systems that we want to see 'immunized'. We strongly believe that building efficient immune systems require going back to the source of immune model analysis: the work of Francesco Varela on cognitive sciences and the need for a shift of cognitive analysis towards enactement, i.e. the analysis of system cognition as the control of the very interactions of the system with its environment. This radical shift back to the origin of artificial immunity requires to extract the properties of interest of such an enacting system, and to characterize its implementation. Such a 'general, reusable solution to a commonly occurring problem within a given context in software design' is a well-known artefact in software engineering: a Design Pattern. Artificial Immune Systems are thus a Design Pattern for enacting software, rather than a family of bio-inspired algorithms as we usually consider.

关键词： Artificial Immune Systems Enactment Design Pattern bio-inspired algorithm

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Adapting principles of developmental biology and agent-based modelling for automated urban residential layout design

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ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE 2018年第5期45卷 973-993页

作者： Sun, Yuchao Taplin, John Univ Western Australia Planning & Transport Res Ctr Crawley WA Australia Univ Western Australia Business Sch Crawley WA Australia

The objective is to automate the design of residential layouts as an aid for planners dealing with complex situations. The algorithm COmputational Urban Layout Design, applied to sites with various shapes, is guided by the goal of many mutually accessible residences and can be set to generate orthogonal or irregular road layouts. Using biological principles of genomic equivalence, conditional differentiation and induction, it grows from an embryonic 'adaptive cell' into a plan. Cells are 'genetically identical' with full development potential and can simultaneously lay roads and residential lots, using the gene set to change cell expression and adapt to local contexts. Cells can be seen as self-propagating agents that sort out their dependencies through local interactions. When COmputational Urban Layout Design is set to grow a non-orthogonal layout, the plan has winding roads and irregular residential lots. Such a plan achieves the objective of relatively high residential density and accessibility, leading to walkable and coherent communities.

关键词： Agent-based modelling automated subdivision bio-inspired algorithm generative design urban residential layout design

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A New Optimization algorithm and Its Comparison on Traditional Optimization algorithms

A New Optimization Algorithm and Its Comparison on Tradition...

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第三十八届中国控制会议

作者： YIN Yumin LIAO Bolin LI Shuai College of Information Science and Engineering Jishou University Department of Computing Hong Kong Polytechnic University

bio-inspired algorithm is a kind of algorithm proposed by simulating biological evolution or social behavior in *** kind of algorithm can solve many complex optimization problems, such as assignment problem, knapsack problem, travel path problem and so on, which are difficult to solve by traditional methods. Presently, bio-inspired algorithms have received a significant amount of attention in both academic and engineering societies. This paper presents a new bio-inspired algorithm,termed beetle antennae search(BAS) algorithm. First, inspired by the foraging behavior of beetles, the principle of BAS algorithm is presented. Then, two application examples are conducted to evaluate the effectiveness of BAS algorithm. Importantly, to substantiate the advantage of BAS algorithm, abundant comparative experiments are provided. Experiment results reveal that the BAS algorithm is superior to the other algorithms.

关键词： bio-inspired algorithm Optimization solution BAS algorithm PSO algorithm

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Self-Synchronization in Duty-Cycled Internet of Things (IoT) Applications

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IEEE INTERNET OF THINGS JOURNAL 2017年第6期4卷 2058-2069页

作者： Yadav, Poonam McCann, Julie A. Pereira, Tiago Univ Cambridge Comp Lab Cambridge CB3 9DD England Imperial Coll London Dept Comp London SW7 2AZ England Univ Sao Paulo Inst Math & Comp Sci BR-05508010 Sao Paulo Brazil

In recent years, the networks of low-power devices have gained popularity. Typically, these devices are wireless and interact to form large networks such as the machine to machine networks, Internet of Things, wearable computing, and wireless sensor networks. The collaboration among these devices is a key to achieving the full potential of these networks. A major problem in this field is to guarantee robust communication between elements while keeping the whole network energy efficient. In this paper, we introduce an extended and improved emergent broadcast slot (EBS) scheme, which facilitates collaboration for robust communication and is energy efficient. In the EBS, nodes communication unit remains in sleeping mode and are awake just to communicate. The EBS scheme is fully decentralized, that is, nodes coordinate their wake-up window in a partially overlapped manner within each duty-cycle to avoid message collisions. We show the theoretical convergence behavior of the scheme, which is confirmed through real test-bed experimentation.

关键词： bio-inspired algorithm broadcast messaging firefly based self-synchronization Internet of Things (IoT) machine to machine (M2M) networks media access control (MAC) radio interference

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