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Hierarchical variable selection for predicting chemical constituents in lamb meats using hyperspectral imaging

JOURNAL OF FOOD ENGINEERING 2014年第Dec.期143卷 44-52页

作者： Pu, Hongbin Sun, Da-Wen Ma, Ji Liu, Dan Kamruzzaman, Mohammed S China Univ Technol Coll Light Ind & Food Sci Guangzhou 510641 Guangdong Peoples R China Natl Univ Ireland Univ Coll Dublin Agr & Food Sci Ctr Sch Biosyst EngnFRCFT Dublin 4 Ireland

Hierarchical variable selection method (UVE-SPA-CSA) based on uninformative variable elimination (UVE), successive projections algorithm (SPA) and clonal selection algorithm (CSA) was proposed and applied for the first time to near infrared (NIR) hyperspectral data of lamb meats for predicting chemical constituents of fat, protein and water contents. Instead of selecting different sets of optimum wavelengths for these chemical constituents, only a set of optimum wavelengths were selected with the proposed technique for fat, protein and water. At first, sensitive wavelengths were identified using combinations of UVE and SPA for fat (910, 961, 988, 1011, 1064, 1084, 1192 and 1212 nm), protein (921, 944, 947, 971, 1021, 1091, 1269 and 1396 nm) and water (910, 961, 994, 1058, 1131, 1195, 1198 and 1312 nm), and merged into instrumental optimal wavelengths (IOW). Then, on the basis of the built optimization formulations and CSA, only seven wavelengths (1021, 1084, 1091, 1192, 1212, 1269 and 1396 nm) were selected as the prediction optimum wavelengths (POW) for predicting fat, protein and water contents in lamb meats. The multiple linear regression (MLR) models were developed to relate absorbance spectra of lamb samples and their chemical constituents (i.e. fat, protein and water contents) using POW. The fat, protein and water contents were predicted with correlation coefficient of calibration (R-c) of 0.95, 0.80 and 0.91, and residual prediction deviation (RPD) of 4.13, 1.31 and 2.53, respectively. Based on the obtained MLR equations, the distribution maps of these chemical constituents within lamb meats were generated to help knowing and understanding the heterogeneity of lamb meats. The results indicated that the proposed UVE-SPA-CSA is useful for variable selection of hyperspectral imaging and prediction of chemical constituents of lamb meats. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Hierarchical selection clonal selection algorithm Hyperspectral imaging Lamb

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clonal selection algorithm for Feature selection and Parameters Optimization of Support Vector Machines

Clonal Selection Algorithm for Feature Selection and Paramet...

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2nd International Symposium on Knowledge Acquisition and Modeling

作者： Ding, Sheng Li, ShunXin Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan 430079 Peoples R China Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan 430065 Peoples R China

ISBN: (纸本)9780769538884

This paper presents the clonal selection algorithm (CSA) to select a proper subset of features and optimal parameters of Support Vector Machines (SVMs) classifier. Like the genetic algorithm, clonal selection algorithm is a tool for optimum solution to select better parameters, in our experiment, to improve classification accuracy, the clonal selection algorithm and genetic algorithm are used to reach the optimization performances with several real-world datasets. The experiments show the effectiveness of the methods. And those results are compared each other. The experiments denote that the proposed clonal selection algorithm is shown to be an evolutionary strategy capable of improving the classification accuracy and has fewer features for support vector machines.

关键词： Artificial Immune System(AIS) Support Vector Machine(SVM) Feature selection clonal selection algorithm

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Optimal Distribution Feeder Reconfiguration for Reliability Improvement Considering Uncertainty

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IEEE TRANSACTIONS ON POWER DELIVERY 2014年第3期29卷 1344-1353页

作者： Kavousi-Fard, Abdollah Niknam, Taher Shiraz Univ Technol SUTech Dept Elect & Elect Engn Shiraz 71555313 Iran

This paper proposes a new method to improve the reliability of the distribution system using the reconfiguration strategy. In this regard, a new cost function is defined to include the cost of active power losses of the network and the customer interruption costs simultaneously. Also, in order to calculate the reliability indices of the load points, the reconfiguration technique is considered as a failure-rate reduction strategy. Regarding the reliability cost, the composite customer damage function is employed to find the customer interruption cost data. Meanwhile, a powerful stochastic framework based on a two- point estimate method is proposed to capture the uncertainty of random parameters. Also, a novel self-adaptive modification method based on the clonal selection algorithm is proposed as the optimization tool. The feasibility and satisfying performance of the proposed method are examined on the 69-bus IEEE test system.

关键词： clonal selection algorithm composite customer damage function (CCDF) reconfiguration reliability

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Quantum Interference Crossover-Based clonal selection algorithm and Its Application to Traveling Salesman Problem

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2009年第1期E92D卷 78-85页

作者： Dai, Hongwei Yang, Yu Li, Cunhua Shi, Jun Gao, Shangce Tang, Zheng Huaihai Inst Technol Sch Comp Engn Lianyungang 222005 Peoples R China Toyama Univ Fac Engn Toyama 9308555 Japan

clonal selection algorithm (CSA), based on the clonal selection theory proposed by Burnet, has gained much attention and wide applications during the last decade. However, the proliferation process in the case of immune cells is asexual. That is, there is no information exchange during different immune cells. As a result the traditional CSA is often not satisfactory and is easy to be trapped in local optima so as to be premature convergence. To solve such a problem, inspired by the quantum interference mechanics, an improved quantum crossover operator is introduced and embedded in the traditional CSA. Simulation results based on the traveling salesman problems (TSP) have demonstrated the effectiveness of the quantum crossover-based clonal selection algorithm.

关键词： clonal selection algorithm quantum interference crossover traveling salesman problem hybrid model

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Fusion of clonal selection algorithm and harmony search method in optimisation of fuzzy classification systems

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2009年第1-2期1卷 80-88页

作者： Wang, Xiaolei Gao, Xiao-Zhi Ovaska, Seppo J. Aalto Univ Dept Elect Engn FI-02150 Espoo Finland

This paper presents a hybrid optimisation method based on the fusion of the clonal selection algorithm (CSA) and harmony search (HS) technique. The CSA is employed to improve the harmony memory members in the HS method. The hybrid optimisation algorithm is further used to optimise Sugeno fuzzy classification systems for the Fisher Iris data and wine data classification. Computer simulations results demonstrate the remarkable effectiveness of our new approach.

关键词： clonal selection algorithm CSA harmony search method HS method hybrid optimisation methods fuzzy classification systems

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A Modified Shuffled Frog Leaping algorithm for PAPR Reduction in OFDM Systems

A Modified Shuffled Frog Leaping Algorithm for PAPR Reductio...

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IEEE Region 10 Conference (TENCON)

作者： Zhou, Jie Dutkiewicz, Eryk Liu, Ren Ping Fang, Gengfa Liu, Yuanan Huang, Xiaojing Macquarie Univ Dept Engn N Ryde NSW 2109 Australia Beijing Univ Post & Telecommun Sch Elect Engn Beijing Peoples R China CSIRO Canberra ACT Australia

ISBN: (纸本)9781479940752

Reducing peak-to-average power ratio (PAPR) is an implementation challenge in orthogonal frequency division multiplexing (OFDM) systems. One way to reduce PAPR is to apply a set of selected partial transmission sequence (PTS) to the transmit signals. However, PTS selection is a highly complex NP-hard problem and the computational complexity is very high when a large number of subcarriers are used in the OFDM system. In this paper, we propose a new heuristic PTS selection method, the modified chaos clonal shuffled frog leaping algorithm (MCCSFLA-PTS). The MCCSFLA-PTS is inspired by natural clonal selection of frog colony and based on chaos theory. Simulation results show that the proposed MCCSFLA-PTS achieves better PAPR reduction than genetic, quantum evolutionary and selective mapping algorithms. Furthermore, the proposed algorithm converges faster than the genetic and quantum evolutionary algorithms.

关键词： OFDM PAPR clonal selection algorithm shuffled frog leaping algorithm

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APPLICATION OF ARTIFICIAL IMMUNE SYSTEM APPROACH TO DEVELOP AN algorithm FOR OPTIMIZING MULTI OBJECTIVE PROBLEMS

APPLICATION OF ARTIFICIAL IMMUNE SYSTEM APPROACH TO DEVELOP ...

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proc 2014 inter conf contemporary comp informatics (ic3i)

作者： Raheja, Lovely Lavania, Siddharth Singh, Sanjeev Kumar Dhanda, Namrata Vishveshwarya Inst Technol & Management Dept Comp Sci Noida India Goel Inst Technol & Management Dept Comp Sci Lucknow Uttar Pradesh India

ISBN: (纸本)9781479966295

This research work proposes application and implementation of artificial immune system approach to develop an algorithm for optimizing multi objective problems. The objective of this research work is to study, analyze and enhance the artificial immune system approach for developing an algorithm to solve various real life engineering multi-objective optimization problems.

关键词： Artificial Immune System clonal selection algorithm Multi-Objectice Optimization

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Stretching Technique-based clonal selection algorithm for Flexible Job-shop Scheduling

Stretching Technique-based Clonal Selection Algorithm for Fl...

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International Conference on Computational Intelligence and Natural Computing

作者： Hong, Lu Huaihai Inst Technol Dept Elect Engn Lianyungang Peoples R China

ISBN: (纸本)9780769536453

Flexible Job-shop Scheduling Problem (FJSP) is expanded from the traditional Job-shop Scheduling Problem (JSP), which possesses wider availability of machines for all the operations. The aim is to find an allocation for each operation and to define the sequence of operations on each machine so that the resulting schedule has a minimal completion time. This paper introduces a hybrid metaheuristic, the stretching technique-based immune algorithm, consisting of a combination of the stretching technique and clonal selection algorithm (CSA). The proposed method is used for solving the multi-objective FJSP. The details of implementation for the multi-objective FJSP and the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for the multi-objective FJSP, especially for large scale problems.

关键词： immune systems clonal selection algorithm stretching technique flexible job shop scheduling problem

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An Improved clonal selection algorithm for Job Shop Scheduling

An Improved Clonal Selection Algorithm for Job Shop Scheduli...

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International Symposium on Intelligent Ubiquitous Computing and Education

作者： Lu, Hong Yang, Jing Huaihai Inst Technol Dept Elect Engn Lianyungang Peoples R China Guizhou Univ Coll Elect Engn Guiyang Peoples R China

ISBN: (纸本)9780769536194

The job shop scheduling problem (JSSP) is a notoriously difficult problem in combinatorial optimization. Extensive investigation has been devoted to developing efficient algorithms to find optimal or near-optimal solutions. This paper proposes an improved immune clonal selection algorithm, called improved clonal selection algorithm for the JSSP. The new algorithm has the advantage of preventing from prematurity and fast convergence speed. Numerous well-studied benchmark examples in job-shop scheduling problems were utilized to evaluate the proposed approach. The computational results show that the proposed algorithm could obtain the high-quality solutions within reasonable computing times, and the results indicate the effectiveness and flexibility of the immune memory clonal selection algorithm.

关键词： artificial immune systems clonal selection algorithm job shop scheduling problem

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A Novel Particle Swarm Optimization Method Using clonal selection algorithm

A Novel Particle Swarm Optimization Method Using Clonal Sele...

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International Conference on Measuring Technology and Mechatronics Automation

作者： Hong, Lu Huaihai Inst Technol Dept Elect Engn Lianyungang Peoples R China

ISBN: (纸本)9780769535838

Particle Swarm Optimization, a nature-inspired evolutionary algorithm, has been successful in solving a wide range of real-value optimization problems. However, little attempts have been made to extend it to discrete problems. In this paper, a new particle swarm optimization method based on the clonal selection algorithm is proposed to avoid premature convergence and guarantee the diversity of the population. The experimental results show that the new algorithm not only has great advantage of convergence property over clonal selection algorithm and PSO, but also can avoid the premature convergence problem effectively.

关键词： particle swarm optimization clonal selection algorithm premature diversity

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