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

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. DE operates through similar computational steps as employed by a standard evolutionary algorithm (EA). However, unlike traditional EAs, the DE-variants perturb the current-generation population members with the scaled differences of randomly selected and distinct population members. Therefore, no separate probability distribution has to be used for generating the offspring. Since its inception in 1995, DE has drawn the attention of many researchers all over the world resulting in a lot of variants of the basic algorithm with improved performance. This paper presents a detailed review of the basic concepts of DE and a survey of its major variants, its application to multiobjective, constrained, large scale, and uncertain optimization problems, and the theoretical studies conducted on DE so far. Also, it provides an overview of the significant engineering applications that have benefited from the powerful nature of DE.

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Differential Evolution: A Survey of the State-of-the-Art

Swarm intelligence (SI) is briefly defined as the collective behaviour of decentralized and self-organized swarms. The well known examples for these swarms are bird flocks, fish schools and the colony of social insects such as termites, ants and bees. In 1990s, especially two approaches based on ant colony and on fish schooling/bird flocking introduced have highly attracted the interest of researchers. Although the self-organization features are required by SI are strongly and clearly seen in honey bee colonies, unfortunately the researchers have recently started to be interested in the behaviour of these swarm systems to describe new intelligent approaches, especially from the beginning of 2000s. During a decade, several algorithms have been developed depending on different intelligent behaviours of honey bee swarms. Among those, artificial bee colony (ABC) is the one which has been most widely studied on and applied to solve the real world problems, so far. Day by day the number of researchers being interested in ABC algorithm increases rapidly. This work presents a comprehensive survey of the advances with ABC and its applications. It is hoped that this survey would be very beneficial for the researchers studying on SI, particularly ABC algorithm.

A comprehensive survey: artificial bee colony (ABC) algorithm and applications

Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.

Recent advances in differential evolution – An updated survey

The Arithmetic Optimization Algorithm

Medical image segmentation is the basis of medical image three-dimension reconstruction. The accuracy of image segmentation directly affects the results of image 3D reconstruction. Medical image is a kind of grayscale image. In order to adequately utilize gray information and spatial information of image, the traditional 2D gray histogram is improved and forms the 2D D?value attribute gray histogram. Computation method of average gray and 2D entropy is improved. Use spatial information as a substitute for gray probability to compute entropy. Computation of entropy is based on D-value attribute gray histogram and created spatial different attribute information entropy(SDAIVE). In experiment, a series of head CT images are segmented. Experimental results show that improved threshold method can better segment noise image. This method has strong anti-noise capability and clear segmentation results.

Medical Image Segmentation Based on an Improved 2D Entropy

Fruit Fly algorithm (FFO) is a recently reported metaheuristics originally designed for function optimization problems. This paper proposes an effective FFO to solve the hybrid flowshop scheduling (HFS) problem with the aim of makespan optimization (minimization). In presented FFO, we utilize the permutation-based representation and operators. The presented FFO can directly explore the discrete solution space of the HFS problem. Extensive comparisons are carried out with well-known meta-heuristics. The results show that the developed FFO is very effective for the considered HFS problem.

An effective fruit fly optimization algorithm for the hybrid flowshop scheduling problem

Range image registration is a popular problem in pattern recognition and computer vision, and it has a wide range of applications in real life. The objective of registration is to match two models as close as possible. In this area, the best known iterative closest point (ICP) method is sensitive to the initial position of two models and it is easy stuck in local minima. In recent years, heuristic algorithms have been used for registration with good ability for global searching. However, the features of models are ignored generally in the iterative evolution process, so the tailored methods are lack of versatility for different models registration. In this paper, normal angle histogram is added into the fruit fly optimization algorithm for registration. The searching step of each individual is relative to the initial position of two models. The versatility and effectiveness of proposed algorithm are illustrated by a series of experiments.

Normal histogram-based fruit fly optimization algorithm for range image registration

It is essential to effective control plant inventory, improve production efficiency, reduce production cost and raise product delivery time satisfactory rate that the fast and effective optimization software's application of magnetic multi-process coordination production planning. This paper researches the integration of production planning problem to solve multi-process, multi-device, multi-stage's planning, which based on theory of constraints (TOC).The planning problem takes enterprise benefit as goal and takes bottleneck resources in the production process as system constraints. Then we change the research result into optimization software of magnetic multi-process coordination production planning. The practical application of the software demonstrates the design and development of this software's feasibility and necessity.

Design and development of optimization software of magnetic multi-process coordination production planning based on benefit

An Improved Harmony search algorithm (IHS) is proposed for solving multi-dimensional and multi-extremal function optimization problems. The IHS algorithm uses a common cross-operation to generate new solutions instead of the traditional improvisation mechanism presented in the basic Harmony search. The number of the new solutions varies from 1 to HMS. In each iteration, IGHS uses a one-on-one selection scheme to compare the old solutions with the new ones, and the winner will enter the next generation. Simulation results show that the proposed algorithm is more quickly and accurately to solve function optimization problems than the compared algorithms.

Quan-Ke Pan

Papers

Medical Image Segmentation Based on an Improved 2D Entropy

An effective fruit fly optimization algorithm for the hybrid flowshop scheduling problem

Normal histogram-based fruit fly optimization algorithm for range image registration

Design and development of optimization software of magnetic multi-process coordination production planning based on benefit

An improved harmony search algorithm for multi-dimensional function optimization problem