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10th International Conference on Electronics, Computer and Computation (ICECCO)

作者： Dilmen, Erdem Yilmaz, Selim Beyhan, Selami Pamukkale Univ Dept Mechatron Engn TR-20070 Denizli Turkey Pamukkale Univ Dept Elect & Elect Engn TR-20070 Denizli Turkey

ISBN: (纸本)9781479933433

In this paper, the well-known heuristic Artificial Bee Colony algorithm (ABC) and deterministic Levenberg-Marquardt (LM) optimization method are unified to get better performance of nonlinear optimization. In the proposed cascaded ABC-LM algorithm, the power of the ABC and LM algorithms are synergized to reduce computational-time and get rid of the problem "stucking at local minima" of some nonlinear functions. Then, the proved power of the cascaded optimization is also tested on the training of Artificial Neural Network (ANN) for classification of XOR data and nonlinear system identification of real-time inverted pendulum set-up. The comparisons in function optimization and system identification using ABC, LM and ABC-LM showed that ABC-LM optimized nonlinear functions and ABC-LM trained ANN has resulted smaller cost functions and mean-squared-error (MSE) values, respectively.

关键词： ABC algorithm LM method nonlinear function optimization nonlinear system identification

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A hybrid optimization method of harmony search and opposition-based learning

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ENGINEERING optimization 2012年第8期44卷 895-914页

作者： Gao, X. Z. Wang, X. Ovaska, S. J. Zenger, K. Aalto Univ Sch Elect Engn Dept Automat & Syst Technol FI-00076 Aalto Finland Aalto Univ Dept Elect Engn Sch Elect Engn FI-00076 Aalto Finland

The harmony search (HS) method is an emerging meta-heuristic optimization algorithm. However, like most of the evolutionary computation techniques, it sometimes suffers from a rather slow search speed, and fails to find the global optimum in an efficient way. In this article, a hybrid optimization approach is proposed and studied, in which the HS is merged together with the opposition-based learning (OBL). The modified HS, namely HS-OBL, has an improved convergence property. optimization of 24 typical benchmark functions and an optimal wind generator design case study demonstrate that the HS-OBL can indeed yield a superior optimization performance over the regular HS method.

关键词： harmony search (HS) opposition-based learning (OBL) hybrid optimization methods nonlinear function optimization optimal wind generator design

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A hybrid PBIL-based harmony search method

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NEURAL COMPUTING & APPLICATIONS 2012年第5期21卷 1071-1083页

作者： Gao, X. Z. Wang, X. Jokinen, T. Ovaska, S. J. Arkkio, A. Zenger, K. Aalto Univ Sch Elect Engn Dept Automat & Syst Technol Aalto 00076 Finland Aalto Univ Sch Elect Engn Dept Elect Engn Aalto 00076 Finland

The harmony search (HS) method is a popular meta-heuristic optimization algorithm, which has been extensively employed to handle various engineering problems. However, it sometimes fails to offer a satisfactory convergence performance under certain circumstances. In this paper, we propose and study a hybrid HS approach, HS-PBIL, by merging the HS together with the population-based incremental learning (PBIL). Numerical simulations demonstrate that our HS-PBIL is well capable of outperforming the regular HS method in dealing with nonlinear function optimization and a practical wind generator optimization problem.

关键词： Harmony search (HS) Population-based incremental learning (PBIL) Hybrid optimization methods nonlinear function optimization Wind generator optimization

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A Novel Harmony Search Method with Dual Memory

A Novel Harmony Search Method with Dual Memory

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Gao, X. Z. Wang, X. Zenger, K. Wang, Xiaofeng Shanghai Maritime Univ Coll Informat Engn 1550 Pu Dong Da Dao Shanghai 200135 Peoples R China Aalto Univ Sch Elect Dept Automat & Syst Technol FI-00076 Aalto Finland

ISBN: (纸本)9781467317146

The Harmony Search (HS) method is an emerging meta-heuristic optimization algorithm, which has been widely employed to deal with various optimization problems during the past decade. However, like most of the evolutionary computation techniques, it sometimes suffers from a rather slow search speed, and even fails to find the global optima in an efficient way. In this paper, a new HS method with dual memory, namely DUAL-HS, is proposed and studied. The secondary memory in the DUAL-HS takes advantage of the Opposition-Based Learning (OBL) to evolve so that the quality of all the harmony memory members can be significantly improved. optimization of 25 typical benchmark functions demonstrate that compared with the regular HS method, our DUAL-HS has an enhanced convergence property.

关键词： Harmony Search (HS) Opposition-Based Learning (OBL) dual harmony memory hybrid optimization methods nonlinear function optimization

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A Bee Foraging-based Memetic Harmony Search Method

A Bee Foraging-based Memetic Harmony Search Method

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Gao, X. Z. Wang, X. Zenger, K. Wang, Xiaofeng Shanghai Maritime Univ Coll Informat Engn 1550 Pu Dong Da Dao Shanghai 200135 Peoples R China Aalto Univ Sch Elect Engn Dept Automat & Syst Technol FIN-00076 Aalto Finland

ISBN: (纸本)9781467317146

The Harmony Search (HS) method is an emerging meta-heuristic optimization algorithm, which has been extensively applied to handle numerous optimization problems during the past decade. However, it usually lacks of an efficient local search capability, and may sometimes suffer from weak convergence. In this paper, a memetic HS method, m-HS, with local search function is proposed and studied. The local search in the m-HS is inspired by the principle of bee foraging, and performs only at selected harmony memory members, which can significantly improve the efficiency of the overall search procedure. Compared with the original HS method, our m-HS has been demonstrated in numerical simulations of 16 typical benchmark functions to yield a superior optimization performance.

关键词： Harmony Search (HS) memetic computing local search hybrid optimization methods bee foraging nonlinear function optimization

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Sensor calibration based on external stimulus actuation

Sensor calibration based on external stimulus actuation

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Smart Structures and Materials 2005 Conference

作者： Feng, J Potkonjak, M Univ Calif Los Angeles Dept Comp Sci Los Angeles CA 90095 USA

ISBN: (纸本)0819457388

We have developed an on-line sensor calibration scheme that employes a additional single source as the external stimulus that creats differential sensor readings used for calibration. The key idea of our approach is to use an actuator to produce differential simultaneous excitement of all sensors over a number of time frames while the environment the sensors are deployed in is relatively inactive. The sensor calibration functions are derived in such a way that all sensors (or a group of sensors) agree on the effect of the actuator in the most consistent way. More specifically, we utilizes the maximal likelihood principle and a nonlinear system optimization solver to derive the calibration functions of arbitrary complexity and accuracy. The approach has the following noble properties: i) it is maximally localized in that each sensor only needs to communicate with one other sensor in order to be calibrated;ii) the number of time steps that are required for calibration is very low. Therefore, the approach is both communication and time efficitent. We present two variants of the approach: i) one where only two neighboring sensors have to communicate in order to conduct calibration;ii) one that utilizes an integer linear programming (ILP) formulation to provably minimize the required number of packets that must be sent for calibration. We evaluate the techniques using traces from light sensors recorded by in-field deployed sensors, and statistical evaluations are conducted in order to obtain the interval of confidence to support all the results.

关键词： calibration external actuator nonlinear function optimization

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Knowledge-based function optimization using fuzzy cultural algorithms with evolutionary programming

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 2001年第1期31卷 1-18页

作者： Reynolds, RG Zhu, SN Wayne State Univ Dept Comp Sci Detroit MI 48202 USA DaimlerChrysler Dept Informat Technol Auburn Hills MI 48326 USA

In this paper, the advantages of a fuzzy representation in problem solving and search is investigated using the framework of Cultural algorithms (CAs), Since all natural languages contain a fuzzy component, the natural question is "Does this fuzzy representation facilitate the problem-solving process within these systems?" In order to investigate this question we use the CA framework of Reynolds [1], CAs are a computational model of cultural evolution derived from and used to express basic anthropological models of culture and its development. A mathematical model of a full fuzzy CA is developed here, In it, the problem solving knowledge is represented using a fuzzy framework. Several theoretical results concerning its properties are presented. The model is then applied to the solution of a set of 12 difficult, benchmark problems in nonlinear real-valued function optimization. The performance of the full fuzzy model is compared with 8 other fuzzy and crisp architectures. The results suggest that a fuzzy approach can produce a statistically significant improvement in search efficiency over nonfuzzy versions for the entire set of functions, We then investigate the class of performance functions for which the full fuzzy system exhibits the greatest improvements over nonfuzzy systems. In general, these are functions which require some preliminary investigation in order to embark on an effective search.

关键词： cultural algorithms evolutionary programming fuzzy set theory nonlinear function optimization

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