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

Improved non-maximum suppression for object detection using harmony search algorithm

In this article, scheduling and rescheduling problems with increasing processing time and new job insertion are studied for reprocessing problems in the remanufacturing process. To handle the unpre...

Discrete harmony search algorithm for scheduling and rescheduling the reprocessing problems in remanufacturing: a case study

This paper presents a harmony search algorithm with ensemble of parameter sets, named EHS algorithm, for solving continuous optimization problems. In the proposed algorithm, an ensemble of parameter sets is adopted to self-adaptively choose the best control parameters during the evolution process. This method not only eliminates the need to perform the trail-and-error search for the best single parameter set, but enables us to benefit from the match between the parameter sets, the different search phases, and the specific problems as well. Extensive computational simulations and comparisons are carried out by employing a set of 10 benchmark problems from the literature. The computational results show that the proposed EHS algorithm is more effective in finding better solutions than the state-of-the-art harmony search (HS) variants [1,2,3].

A Harmony Search Algorithm with Ensemble of Parameter Sets

In this study, an Improved Inver-over operator is proposed to solve the Euclidean traveling salesman problem (TSP) problem. The Improved Inver-over operator is tested on 14 different TSP examples selected from TSPLIB. The application of the Improved Inver-over operator gives much more effective results regarding to the best and average error values than the Basic Inver-over operator. Then an effective Memetic Algorithm based on Improved Inver-over operator and Lin-Kernighan local search is implemented. To speed up the convergence capability of the presented algorithm, a restart technique is employed. We evaluate the proposed algorithm based on standard TSP test problems and show that the proposed algorithm performs better than other Memetic Algorithm in terms of solution quality and computational effort.

Memetic Algorithm based on Improved Inver-over operator and Lin-Kernighan local search for the Euclidean traveling salesman problem

This paper presents a hybrid harmony search (HHS) algorithm for solving no-wait flow shop scheduling problems with total flowtime criterion. First, an initial harmony memory (HM) is formed by taking advantage of the NEH heuristic. Second, the harmony memory is divided into several small groups and each group executes its evolution process independently. At the same time, groups share information reciprocally by dynamic re-grouping mechanism. Third, to stress the balance between the global exploration and local exploration, a variable neighborhood search algorithm is developed and embedded in the HHS algorithm. In addition, a speed-up method is applied to reduce the running time requirement. Computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is shown that the proposed HHS algorithm is superior to the recently published hybrid DE-based (HDE) algorithm and hybrid particle swarm optimization (HPSO) algorithm in terms of effectiveness and efficiency.

Quan-Ke Pan

Papers

Improved non-maximum suppression for object detection using harmony search algorithm

Discrete harmony search algorithm for scheduling and rescheduling the reprocessing problems in remanufacturing: a case study

A Harmony Search Algorithm with Ensemble of Parameter Sets

Memetic Algorithm based on Improved Inver-over operator and Lin-Kernighan local search for the Euclidean traveling salesman problem

A hybrid harmony search algorithm for the no-wait flow-shop scheduling problems