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Microphone configuration for beamformer design using the Taguchi method

MEASUREMENT 2017年 96卷 58-66页

作者： Chan, Kit Yan Yiu, Cedric Ka Fai Nordholm, Sven Curtin Univ Dept Elect & Comp Engn Perth WA Australia Hong Kong Polytech Univ Dept Appl Math Kowloon Hong Kong Peoples R China

In beamformer design, the microphone configurations which represent microphone number and positions are necessary to be optimized in order to improve the effectiveness of speech enhancement. Determination of microphone configuration, number of elements and positions is a nonlinear and non convex NP-hard optimization problem which was not specified before. However, this is a nonlinear and non-convex NP-hard optimization problem. Gradient-based optimization methods can only converge to suboptimal solutions. Although the recently developed heuristic methods may find better configurations, they require long convergence time. In this paper, we study the effectiveness of using Taguchi method to determine microphone configuration. The Taguchi method is a robust and systematic optimization approach for designing reliable and high-quality models. The method conducts systematic trials based on an orthogonal array which represents a subset of representative configurations. It determines the configurations based on the experimental trials, while the heuristic methods determine the configurations by searching through the configuration domain until no better configuration can be found. A case study based on a common office environment is used as an example to illustrate the effectiveness of the Taguchi method and the commonly used heuristic methods. The numerical results demonstrate that the method is capable to develop the microphone configurations with similar performance compared with the heuristic methods when short computational time is only available. Hence, the method is a strong candidate to design microphone configurations when short development time is only available. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Beamformer design Microphone configuration Speech enhancement Taguchi method Orthogonal array evolutionary algorithms

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A generic framework for multi-criteria decision support in eco-friendly urban logistics systems

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 71卷 288-300页

作者： Gupta, A. Heng, C. K. Ong, Y. S. Tan, P. S. Zhang, A. N. Nanyang Technol Univ Sch Comp Engn Computat Intelligence Lab Singapore 639798 Singapore ASTAR Singapore Inst Mfg Technol Singapore 639798 Singapore

In this paper, we present a multi-objective shortest path evolutionary algorithm for comprehensive solutions to real-world manifestations of the classical vehicle routing problem. The shift from being a purely academic pursuit is highlighted by the introduction of a generic optimization framework which accommodates a variety of attributes that commonly occur in industrial applications. Specifically, the paper's main contribution are as follows: (1) consideration for the following real-world constraints: (a) time windows at customer locations, (b) simultaneous pickup and delivery demands, (c) a heterogeneous fleet of vehicles, and (d) the heterogeneity of traffic congestion levels in urban transportation networks;(2) assimilation of all the above attributes into a multi-objective program which aims to minimize environmental impact, while simultaneously addressing the overall operational costs of the routing solution and service quality concerns;a feat that has not been fully realized by known intelligent systems according to the authors' best knowledge. In order to showcase the efficacy of the proposed algorithm, it is first tested on existing benchmark instances and then applied on a pair of real-world industrial examples from Singapore. These industrial examples serve as a source of new benchmarks which facilitate the study of different routing constraints and their effects on the economic and environmental viability of urban logistics systems. (C) 2016 Published by Elsevier Ltd.

关键词： Multi-objective optimization evolutionary algorithms Vehicle routing problem Eco-friendly urban logistics

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An experimental study on fuzzy distances for skull-face overlay in craniofacial superimposition

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FUZZY SETS AND SYSTEMS 2017年 318卷 100-119页

作者： Campomanes-Alvarez, Carmen Rosario Campomanes-Alvarez, B. Guadarrama, Sergio Ibanez, Oscar Cordon, Oscar Univ Granada Dept Comp Sci & Artificial Intelligence C Daniel Saucedo Aranda S-N E-18071 Granada Spain European Ctr Soft Comp C Gonzalo Gutierrez Quiros S-N Mieres 33600 Asturias Spain Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

Skull-face overlay is the most time-consuming and error-prone stage in craniofacial superimposition, an important skeleton based forensic identification technique. This task focuses on achieving the best possible overlay of an unknown skull found and a single ante-mortem image of a candidate missing person. The process is influenced by some sources of uncertainty since two objects of different nature are involved, i.e. a skull and a face. In previous works we have developed a computer-aided craniofacial superimposition system aimed to assist forensic anthropologists in obtaining the best possible skull and face overlay. The system has successfully allowed us to reduce the processing time, simplify the forensic anthropologists' work, and make the process more objective and reproducible. Our approach is based on automatically overlaying a skull three dimensional model onto a facial photograph by minimizing the distance between two subsets of corresponding cranial and facial landmarks. The proposed method properly deals with the inherent uncertainty sources to the skull face overlay process by considering fuzzy sets to model imprecise landmark location, and imprecise cranial and facial landmarks spatial correspondence (resulting from the presence of soft tissues in the face). Accordingly, our methodology requires computing two kinds of distance metrics: between a point and a fuzzy set, and between two fuzzy sets. This contribution is devoted to study the performance and influence of the most significant and suitable fuzzy distances proposed in the specialized literature, as well as other new ones proposed, on our skull face overlay system. In particular, we have tested the behavior of our automatic method when considering eight different distance measurements. The system performance has been objectively evaluated considering 18 case studies resulting from a ground truth dataset following a rigorous statistical experimental setup. The fact that the choice of a good dist

关键词： Forensic identification Craniofacial superimposition Skull-face overlay Fuzzy landmarks Fuzzy distances evolutionary algorithms Genetic fuzzy systems

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Automated watershed subdivision for simulations using multi-objective optimization

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HYDROLOGICAL SCIENCES JOURNAL-JOURNAL DES SCIENCES HYDROLOGIQUES 2017年第10期62卷 1564-1582页

作者： Momm, Henrique G. Bingner, Ronald L. Emilaire, Randall Garbrecht, Jurgen Wells, Robert R. Kuhnle, Roger A. Middle Tennessee State Univ Dept Geosci Murfreesboro TN 37132 USA ARS Natl Sedimentat Lab USDA Oxford MS USA ARS Great Plains Agroclimate & Nat Resources Res Unit USDA El Reno OK USA

A qualitative trial-and-error approach is commonly used to define watershed subdivisions through varying a single topographic threshold value. A methodology has been developed to quantitatively determine spatially variable threshold values using topography and a user-defined landscape reference layer. Optimization and topographic parameterization algorithms were integrated to create solutions that minimize the number of sub-watersheds and maximize the agreement between the discretized watershed and the reference layer. The system was evaluated using different reference datasets such as soil type, land management, and landscape form. Comparison of simulated results indicated that the scenario using land management as the reference layer yielded results closer to the scenario subdivided using a constant topographic threshold but with approximately 10 times more sub-catchments and therefore indicating customization of the watershed subdivision to the user-defined reference layer. The proposed optimization technology could be used in adequately applying watershed modeling technology in developing conservation practice implementation plans.

关键词： evolutionary algorithms GIS watershed modeling watershed subdivision AnnAGNPS soil erosion spatially variable critical source area

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A two-phase harmony search algorithm for continuous optimization

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COMPUTATIONAL INTELLIGENCE 2017年第4期33卷 1038-1075页

作者： Assad, Assif Deep, Kusum Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttar Pradesh India

Harmony search (HS) algorithm is inspired by the music improvisation process in which a musician searches for the best harmony and continues to polish the harmony to improve its aesthetics. The efficiency of evolutionary algorithms depends on the extent of balance between diversification and intensification during the course of the search. An ideal evolutionary algorithm must have efficient exploration in the beginning and enhanced exploitation toward the end. In this paper, a two-phase harmony search (TPHS) algorithm is proposed that attempts to strike a balance between exploration and exploitation by concentrating on diversification in the first phase using catastrophic mutation and then switches to intensification using local search in the second phase. The performance of TPHS is analyzed and compared with 4 state-of-the-art HS variants on all the 30 IEEE CEC 2014 benchmark functions. The numerical results demonstrate the superiority of the proposed TPHS algorithm in terms of accuracy, particularly on multimodal functions when compared with other state-of-the-art HS variants;further comparison with state-of-the-art evolutionary algorithms reveals excellent performance of TPHS on composition functions. Composition functions are combined, rotated, shifted, and biased version of other unimodal and multimodal test functions and mimic the difficulties of real search spaces by providing a massive number of local optima and different shapes for different regions of the search space. The performance of the TPHS algorithm is also evaluated on a real-life problem fromthe field of computer vision called camera calibration problem, ie, a 12-dimensional highly nonlinear optimization problem with several local optima.

关键词： camera calibration evolutionary algorithms harmony search metaheuristic optimization

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Reduced search space mechanism for solving constrained optimization problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 65卷 147-158页

作者： Sallam, Karam M. Sarker, Ruhul A. Essam, Daryl L. Univ New South Wales Sch Engn & Informat Technol Canberra ACT Australia

Over the last few decades, a considerable number of evolutionary algorithms (EAs) have been proposed for solving constrained optimization problems (COPs). As for most of these problems, the optimal solution exists on the boundary of the feasible space, we aim to focus the search process around the boundary. In this paper a new concept, called reduced search space (R2S), is introduced. In the process, we first identify active constraints, based on the current solutions, and then define R2S around those constraint's boundaries. However, the search may be conducted either in the entire R2S or in some portions of it. To judge the impact of this concept, we have incorporated it with a number of state-of-the-art algorithms, and we have comprehensively tested it on three sets of benchmark test functions, namely, 24 test functions taken from IEEE CEC2006, 18 test functions with 10D and 301) taken from IEEE CEC2010 and 10 test functions taken from IEEE CEC2011. The results show that our proposed mechanism significantly improves the performances of state-of-the-art algorithms. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Constrained optimization problem evolutionary algorithms Differential evolution Boundary search Reduced search space

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Inverse Scattering for Monochromatic Phaseless Measurements

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2017年第1期66卷 45-60页

作者： Alvarez, Yuri Garcia-Fernandez, Maria Poli, Lorenzo Garcia-Gonzalez, Cebrian Rocca, Paolo Massa, Andrea Las-Heras, Fernando Univ Oviedo Dept Ingn Elect Gijon 33203 Spain Univ Trento ELEDIA Res Ctr Dept Informat Engn & Comp Sci I-38123 Trento Italy

An inverse method and measurement setup for profile and constitutive parameters reconstruction from monochromatic phaseless information is presented. The method is based on the minimization of a cost function that relates the measured field with the one scattered by a model of the object under test (OUT), where the position, contour, and constitutive parameters are the unknowns. As a result, phaseless information is directly related to the inverse problem unknowns, thus avoiding the need of an intermediate phase retrieval step. Due to the nonlinear nature of the cost function, global optimization techniques, such as the particle swarm optimization and differential evolution algorithms, have been considered for cost function minimization. An exhaustive analysis of the cost function behavior as a function of the electric size of the OUT is presented, discussing the optimal OUT size where the proposed methodology provides accurate profile and constitutive parameters reconstruction. The proposed methodology is conceived to use it together with a simple, low-cost measurement setup for fast characterization of perfect electric conductor and dielectric objects. Measurement examples are presented aiming to prove the feasibility of the described measurement setup.

关键词： evolutionary algorithms integral equations inverse scattering method of moments optimization phaseless measurements

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A comparative study of fast adaptive preference-guided evolutionary multi-objective optimization 9th

A comparative study of fast adaptive preference-guided evolu...

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9th International Conference on evolutionary Multi-Criterion Optimization, EMO 2017

作者： Siegmund, Florian Ng, Amos H. C. Deb, Kalyanmoy School of Engineering Science University of Skövde Högskolevägen Skövde54128 Sweden Department of Electrical and Computer Engineering Michigan State University 428 S. Shaw Lane East LansingMI48824 United States

ISBN: (纸本)9783319541563

In Simulation-based evolutionary Multi-objective Optimization, the number of simulation runs is very limited, since the complex simulation models require long execution times. With the help of preference information, the optimization result can be improved by guiding the optimization towards relevant areas in the objective space with, for example, the Reference Point-based NSGA-II algorithm (R-NSGA-II) [4]. Since the Pareto-relation is the primary fitness function in R-NSGA-II, the algorithm focuses on exploring the objective space with high diversity. Only after the population has converged close to the Pareto-front does the influence of the reference point distance as secondary fitness criterion increase and the algorithm converges towards the preferred area on the Pareto-front. In this paper, we propose a set of extensions of R-NSGA-II which adaptively control the algorithm behavior, in order to converge faster towards the reference point. The adaption can be based on criteria such as elapsed optimization time or the reference point distance, or a combination thereof. In order to evaluate the performance of the adaptive extensions of R-NSGA-II, a performance metric for reference point-based EMO algorithms is used, which is based on the Hypervolume measure called the Focused Hypervolume metric [12]. It measures convergence and diversity of the population in the preferred area around the reference point. The results are evaluated on two benchmark problems of different complexity and a simplistic production line model. © Springer International Publishing AG 2017.

关键词： evolutionary algorithms

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Multi-factorial evolutionary algorithm based on M2M decomposition 11th

Multi-factorial evolutionary algorithm based on M2M decompos...

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11th International Conference on Simulated Evolution and Learning, SEAL 2017

作者： Mo, Jiajie Fan, Zhun Li, Wenji Fang, Yi You, Yugen Cai, Xinye Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Shantou515063 China Department of Electronic Engineering Shantou University Shantou515063 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Jiangsu210016 China

ISBN: (纸本)9783319687582

This paper proposes a decomposition-based multi-objective multi-factorial evolutionary algorithm (MFEA/D-M2M). The MFEA/D-M2M adopts the M2M approach to decompose multi-objective optimization problems into multiple constrained sub-problems for enhancing the diversity of population and convergence of sub-regions. An machine learning model augmented version is also been implemented, which utilized discriminative models for pre-selecting solutions. Experimental studies on nine multi-factorial optimization (MFO) problem sets are conducted. The experimental results demonstrated that MFEA/D-M2M outperforms the vanilla MFEA on six MFO benchmark problem sets and achieved comparable results on the other three problem sets with partial intersection of global optimal. © Springer International Publishing AG 2017.

关键词： evolutionary algorithms

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An evolutionary Many-Objective Optimization Algorithm Based on Coverage and Cache Strategy 3

An Evolutionary Many-Objective Optimization Algorithm Based ...

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3rd International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration, ICIICII 2017

作者： Sun, Haoran Cai, Xinye Sulaman, Muhammad Fan, Zhun College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing Jiangsu210016 China Department of Electronic Engineering School of Engineering Shantou University Guangdong China

ISBN: (纸本)9781538624340

How to balance the diversity and convergence plays an important role on the performance of a multiobjective evolutionary optimizer. Due to the loss of selection pressure and the exponential expansion in the high-dimensional objective space, it is even more difficult for an optimizer to balance between convergence and diversity for a many-objective optimization problem. To address this issue, in this paper, we propose a cache mechanism to improve the convergence and a coverage-based method for maintaining better diversity. Based on these two mechanisms, a many-objective evolutionary algorithm is further proposed. The experimental studies are conducted to verify the effectiveness of the proposed approach. © 2017 IEEE.

关键词： evolutionary algorithms

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