Browsing by Author "Ho, SY"

Now showing 1 - 11 of 11

A novel approach to production planning of flexible manufacturing systems using an efficient multi-objective genetic algorithm
(2005-06-01)
Chen, JH
;
Ho, SY
;
生物資訊及系統生物研究所
;
Institude of Bioinformatics and Systems Biology
In this paper, a novel approach using an efficient multi-objective genetic algorithm EMOGA is proposed to solve the problems of production planning of flexible manufacturing systems (FMSs) having four objectives: minimizing total flow time, machine workload unbalance. greatest machine workload and total tool cost. EMOGA makes use of Pareto dominance relationship to solve the problems without using relative preferences of multiple objectives. High efficiency of EMOGA arises from that multiple objectives can be optimized simultaneously Without using heuristics and a set of good non-dominated Solutions can be obtained providing additional degrees of freedom for the exploitation of resources of FMSs. Experimental results demonstrate effectiveness of the proposed approach using EMOGA to]production planning of FMSs. (c) 2004 Elsevier Ltd. All rights reserved.
An orthogonal simulated annealing algorithm for large floorplanning problems
(2004-08-01)
Ho, SY
;
Ho, SJ
;
Lin, YK
;
Chu, WCC
;
生物科技學系
The conventional simulated annealing with some random generation mechanism using the sequence-pair topological representation in block placement and floorplanning is effective for a very small number of modules (40-50). This paper proposes an orthogonal simulated annealing algorithm (OSA) with an efficient generation mechanism (EGM) for solving large floorplanning problems. EGM samples a small number of representative floorplans and then efficiently derives a high-performance floorplan by using a systematic reasoning method for the next move of OSA based on orthogonal experimental design. Furthermore, an improved swap operation is proposed which cooperates with EGM to make OSA efficient. Excellent experimental results using the Microelectronics Center of North Carolina and the Gigascale Sysems Research Center benchmarks show that OSA performs better than existing methods for large floorplanning problems.
Design of nearest neighbor classifiers: multi-objective approach
(2005-07-01)
Chen, JH
;
Chen, HM
;
Ho, SY
;
生物資訊及系統生物研究所
;
Institude of Bioinformatics and Systems Biology
The goal of designing optimal nearest neighbor classifiers is to maximize classification accuracy while minimizing the sizes of both reference and feature sets. A usual way is to adaptively weight the three objectives as an objective function and then use a single-objective optimization method for achieving this goal. This paper proposes a multi-objective approach to cope with the weight tuning problem for practitioners. A novel intelligent multi-objective evolutionary algorithm IMOEA is utilized to simultaneously edit compact reference and feature sets for nearest neighbor classification. Three comparison studies are designed to evaluate performance of the proposed approach. It is shown empirically that the IMOEA-designed classifiers have high classification accuracy and small sizes of reference and feature sets. Moreover, IMOEA can provide a set of good solutions for practitioners to choose from in a single run. The simulation results indicate that the IMOEA-based approach is an expedient method to design nearest neighbor classifiers, compared with an existing single-objective approach. (c) 2005 Elsevier Inc. All rights reserved.
Designing structure-specified mixed H-2/H-infinity optimal controllers using an intelligent genetic algorithm IGA
(2005-11-01)
Ho, SJ
;
Ho, SY
;
Hung, MH
;
Shu, LS
;
Huang, HL
This brief proposes an efficient method for designing accurate structure-specified mixed H-2/H-infinity optimal controllers for systems with uncertainties and disturbance using an intelligent genetic algorithm (IGA). The newly-developed IGA with intelligent crossover based on orthogonal experimental design (OED) is efficient for solving intractable engineering problems with lots of design parameters. The IGA-based method without using prior domain knowledge can efficiently solve design problems of multi-input-multi-output (MIMO) optimal control systems, which is very suitable for practical engineering designs. High performance and validity of the proposed method are evaluated by two test problems, a MIMO distillation column model and a MIMO super maneuverable F18/HARV fighter aircraft system. It is shown empirically that the IGA-based method has good tracking performance, robust stability and disturbance attenuation for both controllers, compared with the existing methods.
Efficient gene selection for classification of microarray data
(2005)
Ho, SY
;
Lee, CC
;
Chen, HM
;
Huang, HL
;
生物資訊及系統生物研究所
Microarray is a useful technique for measuring expression data of thousands of genes simultaneously. One of challenges in classification of microarray data is to select a minimal number of relevant genes which can maximize classification accuracy. Many gene selection methods as well as their corresponding classifiers have been proposed. One of existing analysis methods is the hybrid approach based on genetic algorithm and maximum likelihood classification (GA/MLHD). In this paper, an intelligent genetic algorithm (IGA) using control genes and an improved fitness function is proposed to determine the minimal number of relevant genes and identify these genes, while maximizing classification accuracy simultaneously. The experimental results show that our approach is superior to the existing method GA/MLHD in terms of the number of selected genes, classification accuracy, and robustness of selected genes and accuracy, especially for the datasets which have numerous categories and a large number of testing genes inside.
Evolutionary divide-and-conquer approach to inferring S-system models of genetic networks
(2005)
Ho, SY
;
Hsieh, CH
;
Yu, FC
;
Huang, HL
;
生物科技學系
This paper proposes an efficient evolutionary divide-and-conquer approach (EDACA) to inferring S-system models of genetic networks from time-series data of gene expression. Inference of an S-system model has 2N(N+l) parameters to be optimized where N is the number of genes in a genetic network. To cope with higher dimensionality, the proposed approach consists of two stages where each uses a divide-and-conquer strategy. The optimization problem is first decomposed into N subproblems having 2(N+1) parameters each. At the first stage, each subproblem is solved using a novel intelligent genetic algorithm (IGA) with intelligent crossover based on orthogonal experimental design (OED). The intelligent crossover divides two parents into n pairs of parameter groups, economically identifies the potentially better one of two groups of each pair, and systematically obtains a potentially good approximation to the best one of all 2(n) combinations using at most 2n function evaluations. At the second stage, the obtained N solutions to the N subproblems are combined and refined using an OED-based simulated annealing algorithm (OSA) for handling noisy gene expression data. The effectiveness of EDACA is evaluated using simulated expression patterns with/without noise running on a single-CPU PC. It is shown that: 1) IGA is efficient enough to solve subproblems; 2) IGA is significantly superior to. the existing method of using GA with simplex crossover; and 3) EDACA performs well in inferring S-system models of genetic networks from small-noise gene expression data.
Intelligent evolutionary algorithms for large parameter optimization problems
(2004-12-01)
Ho, SY
;
Shu, LS
;
Chen, JH
;
生物資訊及系統生物研究所
;
Institude of Bioinformatics and Systems Biology
This paper proposes two intelligent evolutionary algorithms IEA and IMOEA using a novel intelligent gene collector (IGC) to solve single and multiobjective large parameter optimization problems, respectively. IGC is the main phase in an intelligent recombination operator of IEA and IMOEA. Based on orthogonal experimental design, IGC uses a divide-and-conquer approach, which consists of adaptively dividing two individuals of parents into N pairs of gene segments, economically identifying the potentially better one of two gene segments of each pair, and systematically obtaining a potentially good approximation to the best one of all combinations using at most 2N fitness evaluations. IMOEA utilizes a novel generalized Pareto-based scale-independent fitness function for efficiently finding a set of Pareto-optimal solutions to a multiobjective optimization problem. The advantages of IEA and IMOEA are their simplicity, efficiency, and flexibility. It is shown empirically that IEA and IMOEA have high performance in solving benchmark functions comprising many parameters, as compared with some existing EAs.
Intelligent particle swarm optimization in multi-objective problems
(2006)
Ho, SJ
;
Ku, WY
;
Jou, JW
;
Hung, MH
;
Ho, SY
In this paper, we proposes a novel intelligent multi-objective particle swarm optimization (IMOPSO) to solve multi-objective optimization problems. High performance of IMOPSO mainly arises from two parts: one is using generalized Pareto-based scale-independent fitness function (GPSISF) can efficiently given all candidate solutions a score, and then decided candidate solutions level. The other one is replacing the conventional particle move process of PSO with an intelligent move mechanism (IMM) based on orthogonal experimental design to enhance the search ability. IMM can evenly sample and analyze from the best experience of an individual particle and group particles by using a systematic reasoning method, and then efficiently generate a good candidate solution for the next move of the particle. Some benchmark functions are used to evaluate the performance of IMOPSO, and compared with some existing multi-objective evolution algorithms. According to experimental results and analysis, they show that IMOPSO performs well.
Optimizing fuzzy neural networks, for tuning PID controllers using an orthogonal simulated annealing, algorithm OSA
(2006-06-01)
Ho, SJ
;
Shu, LS
;
Ho, SY
;
生物科技學系
;
生物資訊及系統生物研究所
In this paper, we formulate an optimization problem of establishing a fuzzy neural network model (FNNM) for efficiently tuning proportional-integral-derivative (PID) controllers of various test plants with under-damped responses using a large number P of training plants such that the mean tracking error J of the obtained P control systems is minimized. The FNNM consists of four fuzzy neural networks (FNNs) where each FNN models one of controller parameters (K, T-i, T-d, and b) of PID controllers. An existing indirect, two-stage approach used a dominant pole assignment method with P = 198 to find the corresponding PID controllers. Consequently, an adaptive neuro-fuzzy inference system (ANFIS) is used to independently train the four individual FNNs using input the selected 176 of the 198 PID controllers that 22 controllers with parameters having large variation are abandoned. The innovation of the proposed approach is to directly and simultaneously optimize the four FNNs by using a novel orthogonal simulated annealing algorithm (OSA). High performance of the OSA-based approach arises from that OSA can effectively optimize lots of parameters of the FNNM to minimize J. It is shown that the OSA-based FNNM with P = 176 can improve the ANFIS-based FNNM in averagely decreasing 13.08% error J and 88.07% tracking error of the 22 test plants by refining the solution of the ANFIS-based method. Furthermore, the OSA-based FNNMs using P = 198 and 396 from an extensive timing domain have similar good performance with that using P = 176 in terms of J.
OSA: Orthogonal simulated annealing algorithm and its application to designing mixed H-2/H-infinity optimal controllers
(2004-09-01)
Ho, SJ
;
Ho, SY
;
Shu, LS
;
生物科技學系
;
生物資訊及系統生物研究所
This paper proposes a novel orthogonal simulated annealing (OSA) algorithm for solving intractable large-scale engineering problems and its application to designing mixed H-2/H-infinity optimal structure-specified controllers with robust stability and disturbance attenuation. High performance of OSA arises mainly from an intelligent generation mechanism (IGM), which applies orthogonal experimental design to speed up the search. IGM can efficiently generate a good candidate solution for next move of OSA by using a systematic reasoning method. It is difficult for existing H-infinity- and genetic algorithm (GA)-based methods to economically obtain an accurate solution to the design problem of multiple-input, multiple-output (MIMO) optimal control systems. The high performance and validity of OSA are demonstrated by parametric optimization functions and a MIMO super maneuverable F18/HARV fighter aircraft system with a proportional-integral-derivative (PID)-type controller. It is shown empirically that OSA performs well for parametric optimization functions and the performance of the OSA-based method without prior domain knowledge is superior to those of existing H-infinity- and GA-based methods for designing MIMO optimal controllers.
Scoring method for tumor prediction from microarray data using an evolutionary fuzzy classifier
(2006)
Ho, SY
;
Hsieh, CH
;
Chen, KW
;
Huang, HL
;
Chen, HM
In this paper, we propose a novel scoring method for tumor prediction using an evolutionary fuzzy classifier which can provide accurate and interpretable information. The merits of the proposed method are threefold. 1) The score ranged in [0, 100] can further illustrate the degree of tumor status in contrast to the conventional tumor classifier. 2) The derived score system can be used as a tumor classifier using a system-suggested or human-specified threshold value. 3) The derived classifier with a compact fuzzy rule base can. generate an interpretable and accurate prediction result. The effectiveness of the proposed method is evaluated and compared using two well-known datasets from microarray data and an existing tumor classifier. It is shown by computer simulation that the proposed scoring method is effective using ROC curves of classification.