THE DESIGN AND ANALYSIS OF EXPERIMENTS. By Oscar Kempthorne. New York, John Wiley and Sons, Inc., 1952. 631 pp. $8.50. This book by a teacher of statistics (as well as a consultant for \"experimenters\") is a comprehensive study of the philosophical background for the statistical design of experiment. It is necessary to have some facility with algebraic notation and manipulation to be able to use the volume intelligently. The problems are presented from the theoretical point of view, without such practical examples as would be helpful for those not acquainted with mathematics. The mathematical justification for the techniques is given. As a somewhat advanced treatment of the design and analysis of experiments, this volume will be interesting and helpful for many who approach statistics theoretically as well as practically. With emphasis on the \"why,\" and with description given broadly, the author relates the subject matter to the general theory of statistics and to the general problem of experimental inference. MARGARET J. ROBERTSON

The Design and Analysis of Experiments

A multi-population harmony search algorithm with external archive for dynamic optimization problems

Harmony search algorithm (HSA) is one of the relatively new metaheuristic algorithms that classified under population-based search algorithms. Based on literature, hybridizing local-based searching algorithms with population-based algorithms can improve the performance of hybridized algorithms. This research is an extension to our previous work that focus on solving Nurse Rostering Problems (NRP) using hybrid metaheuristic algorithms. One of the improved version of HSA is enhanced harmony search algorithm (EHSA) where it overcomes some of the weaknesses of basic HSA. Slow convergence is noticed in EHSA which encourage us to hybridize it with other metaheuristic algorithms to improve its performance. In this research, EHSA is hybridized with great deluge algorithm (GD) and called Deluged harmony search algorithm (DHSA). DHSA then compared to CHSA (the hybridization of EHSA with Hill climbing (HC)) which developed earlier. To strike the balance between exploration and exploitation, the exploration stage run using EHSA and the exploitation stage used GD. DHSA is tested to solve a real world NRP problem at National University Malaysia Medical Center (UKMMC). The results show that, DHSA performed much better than CHSA in all instances in terms of solution quality with slightly higher execution time.

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Deluge Harmony Search Algorithm For Nurse Rostering Problems

Display OmittedThe improvisation process of LHS algorithm. Opposition-based learning (OBL) technique is employed in improvisation process. The purpose is to increase the diversity of solution.The current best harmony and worst harmony are used to adjust the parameter BW. An adaptive global pitch adjustment is designed to enhance the exploitation ability of solution space.In the proposed algorithm, a new harmony and its opposite harmony are generated in iteration. Then a competition selection mechanism is established to improve solution precision.The effects that varying the parameter HMS and HMCR have on the performance of the LHS algorithm is also analyzed in detail. In this paper, we propose an improved harmony search algorithm named LHS with three key features: (i) adaptive global pitch adjustment is designed to enhance the exploitation ability of solution space; (ii) opposition-based learning technique is blended to increase the diversity of solution; (iii) competition selection mechanism is established to improve solution precision and enhance the ability of escaping local optima. The performance of the LHS algorithm with respect to harmony memory size (HMS) and harmony memory considering rate (HMCR) are also analyzed in detail. To further evaluate the performance of the proposed LHS algorithm, comparison with ten state-of-the-art harmony search variants over a large number of benchmark functions with different characteristics is carried out. The numerical results confirm the superiority of the proposed LHS algorithm in terms of accuracy, convergence speed and robustness.

Improved Harmony Search Algorithm

In this paper, an improved global-best harmony search algorithm, named IGHS, is proposed. In the IGHS algorithm, initialization based on opposition-based learning for improving the solution quality of the initial harmony memory, a new improvisation scheme based on differential evolution for enhancing the local search ability, a modified random consideration based on artificial bee colony algorithm for reducing randomness of the global-best harmony search (GHS) algorithm, as well as two perturbation schemes for avoiding premature convergence, are integrated. In addition, two parameters of IGHS, harmony memory consideration rate and pitch adjusting rate, are dynamically updated based on a composite function composed of a linear time-varying function, a periodic function and a sign function in view of approximate periodicity of evolution in nature. Experimental results tested on twenty-eight benchmark functions indicate that IGHS is far better than basic harmony search (HS) algorithm and GHS. In further study, IGHS has also been compared with other eight well known metaheuristics. The results show that IGHS is better than or at least similar to those approaches on most of test functions.

Le Liu

Papers

Hybridization of harmony search with variable neighborhood search for restrictive single-machine earliness/tardiness problem