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11th Biennial International Conference on Artificial Evolution (EA)

作者： Lopez, Alejandro Cienfuegos, Miguel Ainseba, Bedreddine Bendahmane, Mostafa Univ Bordeaux 2 Inst Math Bordeaux F-33076 Bordeaux France Univ Guadalajara Dept Elect CUCEI Guadalajara Mexico

ISBN: (纸本)9783319116839;9783319116822

In this paper we present a nearest neighbor particle swarm optimization (PSO) algorithm applied to the numerical analysis of the inverse problem in electrocardiography. A two-step algorithm is proposed based on the application of the modified PSO algorithm with the Tikhonov regularization method to calculate the potential distribution in the heart. The PSO improvements include the use of the neighborhood particles as a strategy to balance exploration and exploitation in order to prevent premature convergences and produce a better local search. In the literature the inverse problem in electrocardiography is solved using the minimum energy norm in a Tikhonov regularization scheme. Although this approach solves the system, the solution may not have a meaning in the physical sense. Comparing to the classical reconstruction, the two-step PSO algorithm improves the accuracy of the solution with respect to the original distribution. Finally, to validate our results, we create a distribution over the heart by using a model of electrical activity (Bidomain model) coupled with a volume conductor model for the torso. Then, using our method, we make the reconstruction of the potential distribution.

关键词： Direct and inverse problems Particle swarm optimization (PSO) bio-inspired algorithm Finite element Electrocardiography Bidomain

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DHGN Network with Mode-Based Receptive Fields for 2-Dimensional Binary Pattern Recognition

DHGN Network with Mode-Based Receptive Fields for 2-Dimensio...

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9th International Conference on Hybrid Artificial Intelligence Systems (HAIS)

作者： Amin, Anang Hudaya Muhamad Khan, Asad I. Nasution, Benny B. Multimedia Univ FIST Thundercloud Res Lab Jalan Ayer Keroh Lama Melaka 75450 Malaysia Monash Univ Fac Informat Technol Clayton Vic 3168 Australia Politeknik Negeri Medan Medan 20155 Indonesia

ISBN: (纸本)9783319076171;9783319076164

We introduce an extension to existing Distributed Hierarchical Graph Neuron (DHGN) network for 2-dimensional binary pattern recognition. The new form of DHGN network, termed as receptive field DHGN network (RF-DHGN) is a hybrid of a receptive field layer for 2D feature extraction, and one or more DHGN subnets for feature recognition. All inputs to the network, in the form of synaptic weights are automatically determined through mode-based activation function within the RF neurons. The proposed scheme minimizes the need for large number of neurons as compared to the normal DHGN scheme. Furthermore, the results of preliminary recognition tests indicate high recognition accuracy, similar to existing DHGN approach for distributed pattern recognition.

关键词： Hybrid system receptive field bio-inspired algorithm parallel pattern recognition

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Fault tolerant network design inspired by Physarum polycephalum

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NATURAL COMPUTING 2013年第2期12卷 277-289页

作者： Houbraken, Maarten Demeyer, Sofie Staessens, Dimitri Audenaert, Pieter Colle, Didier Pickavet, Mario Univ Ghent Dept Informat Technol B-9050 Ghent Belgium

Physarum polycephalum, a true slime mould, is a primitive, unicellular organism that creates networks to transport nutrients while foraging. The design of these natural networks proved to be advanced, e.g. the slime mould was able to find the shortest path through a maze. The underlying principles of this design have been mathematically modelled in literature. As in real life the slime mould can design fault tolerant networks, its principles can be applied to the design of man-made networks. In this paper, an existing model and algorithm are adapted and extended with stimulation and migration mechanisms which encourage formation of alternative paths, optimize edge positioning and allow for automated design. The extended model can then be used to better design fault tolerant networks. The extended algorithm is applied to several national and international network configurations. Results show that the extensions allow the model to capture the fault tolerance requirements more accurately. The resulting extended algorithm overcomes weaknesses in geometric graph design and can be used to design fault tolerant networks such as telecommunication networks with varying fault tolerance requirements.

关键词： bio-inspired algorithm Fault tolerant network design Mathematical modelling Network optimization Physarum polycephalum

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True global optimality of the pressure vessel design problem: a benchmark for bio-inspired optimisation algorithms

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INTERNATIONAL JOURNAL OF bio-inspired COMPUTATION 2013年第6期5卷 329-335页

作者： Yang, Xin-She Huyck, Christian Karamanoglu, Mehmet Khan, Nawaz Middlesex Univ Sch Sci & Technol London NW4 4BT England

The pressure vessel design problem is a well-known design benchmark for validating bio-inspired optimisation algorithms. However, its global optimality is not clear and there has been no mathematical proof put forward. In this paper, a detailed mathematical analysis of this problem is provided that proves that 6,059.714335048436 is the global minimum. The Lagrange multiplier method is also used as an alternative proof and this method is extended to find the global optimum of a cantilever beam design problem.

关键词： algorithm benchmarks pressure vessel global optimality bio-inspired algorithm metaheuristic optimisation

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Solving 0-1 knapsack problems based on amoeboid organism algorithm

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APPLIED MATHEMATICS AND COMPUTATION 2013年第19期219卷 9959-9970页

作者： Zhang, Xiaoge Huang, Shiyan Hu, Yong Zhang, Yajuan Mahadevan, Sankaran Deng, Yong Southwest Univ Sch Comp & Informat Sci Chongqing 400715 Peoples R China Guangdong Univ Foreign Studies Inst Business Intelligence & Knowledge Discovery Guangzhou 510006 Guangdong Peoples R China Vanderbilt Univ Sch Engn Nashville TN 37235 USA

The 0-1 knapsack problem is an open issue in discrete optimization problems, which plays an important role in real applications. In this paper, a new bio-inspired model is proposed to solve this problem. The proposed method has three main steps. First, the 0-1 knapsack problem is converted into a directed graph by the network converting algorithm. Then, for the purpose of using the amoeboid organism model, the longest path problem is transformed into the shortest path problem. Finally, the shortest path problem can be well handled by the amoeboid organism algorithm. Numerical examples are given to illustrate the efficiency of the proposed model. (C) 2013 Elsevier Inc. All rights reserved.

关键词： 0-1 Knapsack problem bio-inspired algorithm Network converting algorithm Optimization Shortest path problem

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bio-inspired Proximity Discovery and Synchronization for D2D Communications

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IEEE COMMUNICATIONS LETTERS 2013年第12期17卷 2300-2303页

作者： Chao, Shih-Lung Lee, Hsin-Ying Chou, Ching-Chun Wei, Hung-Yu Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan

In this paper, a distributed mechanism for application-aware proximity services (ProSe) in device to device (D2D) communications is presented. The method, which is derived from the bio-inspired firefly algorithm, can achieve proximity discovery and synchronization at one time. To be precise, this mechanism not only enables neighbour discovery and service discovery simultaneously, but achieves synchronization in physical communication timing and service interests in the meanwhile. However, the basic firefly algorithm is not perfectly suitable for larger scale systems such as LTE-A D2D. In most network topologies, the property that each node may not be able to hear all its neighbours makes the basic version fail. Thus, we further propose the firefly spanning tree (FST) algorithm which can solve the topology-dependent divergence problems.

关键词： D2D bio-inspired algorithm application-aware services proximity discovery synchronization

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Review on Cat Swarm Optimization algorithms

Review on Cat Swarm Optimization Algorithms

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3rd International Conference on Consumer Electronics, Communications and Networks (CECNet)

作者： Tsai, Pei-Wei Istanda, Vaci Natl Kaohsiung Marine Technol Dept Maritime Informat & Technol Kaohsiung Taiwan Executive Yuan Council Indogenous Peoples Taipei Taiwan

ISBN: (纸本)9781479928606

Cat Swarm Optimization (CSO) is an evolutionary method inspired by the creatures in Mother Nature for solving optimization problems. Less than one decade after CSO is proposed, it has been improved and applied in different fields by many researchers in recent years. In this review, we majorly focus on the original CSO algorithm and some improved branches of CSO family algorithms. Some examples of utilizing CSO to solve problems in engineering are also reviewed. In the first section, the concept of the first proposed CSO algorithm is given in brief, and the improved Parallel CSO (PCSO) is described and summarized in a comprehensive way. Finally, the optimization problems in the engineering fields solved by CSO family algorithms are also reviewed. The objective of the review is to provide the overview of CSO algorithms and a brief introduction for new researchers to the swarm intelligence research field.

关键词： Cat swarm optimization Swarm intelligence bio-inspired algorithm Evolution-ary computing

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Gene Transinfection Directs Towards Gene Functional Enhancement Using Genetic algorithm

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IERI Procedia 2013年 4卷 268-274页

作者： Jayanthi Manicassamy P. Dhavachelvan Department of computer Science Pondicherry University Pondicherry India Department of Computer Science Pondicherry University Pondicherry India

Today, Genetic algorithm plays vital role in solving tough optimization problems with constrains for large search spaces. This works on the principle of survival of the fittest, involving natural evolution process of Darwin theory. GAs don’t opt direct method for resolving tough constrain based optimization problems which has to be carried out by some means, through modifying the existing operations in the phases of classical GA. Numerous operators and its operations subsist for which various design issue confront by most researchers. Among these issues one major issue that is hidden from the researches is, carrying out repairing on genes that are faulty. This paper proposes two bio-inspired gene functionality gene transformation and gene transinfection combined together to solve the problem of obtaining optimal solution. This further enhances the gene functionality through repairing in an effortless means. The proposed work has been tested on 0/1 knapsack problem and its results are compared with classical genetic algorithm which yields to provide better results when compared to that of classical GAs.

关键词： bio-inspired algorithm Evolutionary Computation Gene Repair Genetic algorithm Gene Optimization Transinfection 0/1 knapsack problem

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Gene Transinfection Directs Towards Gene Functional Enhancement Using Genetic algorithm

Gene Transinfection Directs Towards Gene Functional Enhancem...

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2013 International Conference on Electronic Engineering and Computer Science(EECS 2013)

作者： Jayanthi Manicassamy Dhavachelvan.P Department of computer Science Pondicherry University Department of Computer Science Pondicherry University

Today, Genetic algorithm plays vital role in solving tough optimization problems with constrains for large search spaces. This works on the principle of survival of the fittest, involving natural evolution process of Darwin theory. GAs don't opt direct method for resolving tough constrain based optimization problems which has to be carried out by some means, through modifying the existing operations in the phases of classical GA. Numerous operators and its operations subsist for which various design issue confront by most researchers. Among these issues one major issue that is hidden from the researches is, carrying out repairing on genes that are faulty. This paper proposes two bio-inspired gene functionality gene transformation and gene transinfection combined together to solve the problem of obtaining optimal solution. This further enhances the gene functionality through repairing in an effortless means. The proposed work has been tested on 0/1 knapsack problem and its results are compared with classical genetic algorithm which yields to provide better results when compared to that of classical GAs.

关键词： bio-inspired algorithm Evolutionary Computation Gene Repair Genetic algorithm Gene Optimization Transinfection 0/1 knapsack problem

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Two-stage eagle strategy with differential evolution

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INTERNATIONAL JOURNAL OF bio-inspired COMPUTATION 2012年第1期4卷 1-5页

作者： Yang, Xin-She Deb, Suash Natl Phys Lab Teddington TW11 0LW Middx England CV Raman Coll Engn Dept Comp Sci & Engn Bhubaneswar 752054 Orissa India

Efficiency of an optimisation process is largely determined by the search algorithm and its fundamental characteristics. In a given optimisation, a single type of algorithm is used in most applications. In this paper, we will investigate the eagle strategy recently developed for global optimisation, which uses a two-stage strategy by combing two different algorithms to improve the overall search efficiency. We will discuss this strategy with differential evolution and then evaluate their performance by solving real-world optimisation problems such as pressure vessel and speed reducer design. Results suggest that we can reduce the computing effort by a factor of up to ten in many applications.

关键词： bat algorithm Cuckoo Search eagle strategy ES bio-inspired algorithm differential evolution DE optimisation

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