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8th International Multi-Conference on Engineering and Technology Innovation (IMETI)

作者： Hsieh, Yi-Chih You, Peng-Sheng Natl Formosa Univ Dept Ind Management Huwei Yunlin Taiwan Natl ChiaYi Univ Dept Business Adm Chiayi Taiwan

In this study, we investigate a new multi-maintenance with sequential operation (MMSO) problem, in which a variety of tasks must be processed on multiple machines. In the MMSO problem, each task has multiple sequential operations that must be processed for each machine. In addition to maintenance, the MMSO problem has many other practical applications, such as physical examination scheduling. The proposed MMSO, which is an NP-hard problem, generalizes typical job shop scheduling problems. Thus, a novel encoding scheme, which is embedded into an immune-based algorithm (IBA), is proposed in this study to convert any sequence of random numbers into a feasible solution of the MMSO problem to solve the MMSO problem. Numerical results of applications in aircraft maintenance and physical examination scheduling are reported and compared with those of particle swarm optimization and genetic algorithm. Experimental results show that IBA outperforms the two other algorithms.

关键词： Scheduling maintenance sequential operations immune-based algorithm optimization

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Artificial immune-based algorithm for Academic Leadership Assessment

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2019年第8期10卷 364-370页

作者： Jantan, Hamidah Azemi, Nur Hamizah Syafiqah Che Othman, Zulaiha Ali Univ Teknol MARA UiTM Terengganu Fac Comp & Math Sci Kuala Terengganu Terengganu Malaysia Univ Kebangsaan Malaysia Fac Informat Sci & Technol Ctr Artificial Intelligence Technol Bangi Malaysia

Artificial immune-based algorithm is inspired by the biological immune system as computational intelligence approach in data analysis. Negative selection algorithm is derived from immune-based algorithm's family that used to recognize the pattern's changes perform by the gene detectors in complementary state. Due to the self-recognition ability, this algorithm is widely used to recognize the abnormal data or non-self especially for fault diagnosis, pattern recognition, network security etc. In this study, the self-recognition performance proposed by the negative selection algorithm been considered as a potential technique in classifying employee's competency. Assessing the employee's performance in organization is an important task for human resource management people to identify the right candidate in job promotion assessment. Thus, this study attempts to propose an immune-based model in assessing academic leadership performance. There are three phases involved in experimental phase i.e. data acquisition and preparation;model development;and analysis and evaluation. The data consists of academic leadership proficiency was prepared as data-set for learning and detection processes. Several experiments were conducted using cross validation process on different model to identify the most accurate model. Therefore, the accuracy of NS classifier is considered acceptable enough for this academic leadership assessment case study. For enhancement, other immune-based algorithm or bio-inspired algorithms, such as genetic algorithm, particle swam optimization, ant colony optimization would also be considered as a potential algorithm for performance assessment.

关键词： immune-based algorithm negative selection algorithm academic leadership performance assessment

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Solving nonlinear constrained optimization problems: An immune evolutionary based two-phase approach

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APPLIED MATHEMATICAL MODELLING 2015年第19期39卷 5759-5768页

作者： Hsieh, Yi-Chih Lee, Yung-Cheng You, Peng-Sheng Natl Formosa Univ Dept Ind Management Huwei 632 Yunlin Taiwan WuFeng Univ Dept Secur Technol & Management Chiayi 621 Taiwan Natl Chiayi Univ Dept Business Adm Chiayi 600 Taiwan

In this paper, an artificial evolutionary two-phase method that is based upon the immune evolutionary method is proposed to solve nonlinear constrained optimization problems that consist of real variables, integer variables and discrete variables. In the first phase, an immune based algorithm is used to solve the nonlinear constrained optimization problem approximately for all variables. In the second phase, the integer variables and discrete variables are fixed and then a search procedure is proposed to improve the real variable solutions obtained in the first phase. The numerical results for four benchmark problems, including the tube and pressure vessel problem, are reported and compared. As shown, the solutions using the proposed method are all superior to the best solutions for traditional methods detailed in the literature. (C) 2015 Elsevier Inc. All rights reserved.

关键词： Nonlinear Optimization immune-based algorithm

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