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

In a flowshop, the current stage has to remain work-in-process and cannot release any task to the downstream stage once the buffer is stuffed. This scheduling phenomenon is called blocking, and it usually occurs in an industrial setting because of the buffer capacity or the product volume. This study addresses several blocking flowshop scheduling problems to individually optimize the following criteria, i.e. makespan, maximum lateness, or maximum delivery-completion time, in which the tasks are available at different release dates. Given that these scheduling problems are strongly NP-complete, exact and metaheuristic algorithms are designed for different scale instances. An effective branch and bound method is proposed to search exact solutions for small-scale instances within a given period, where well-designed branching rules and lower bounds prune considerable invalid nodes. Further, to seek high-quality feasible schedules in a short time, a discrete artificial bee colony algorithm is provided for medium-scale instances, where a dispatching-rule-based initialization, a series of efficient non-delay variable neighborhood search mechanisms, and a well-designed disturbing scheme enhance the optimization capability. Comprehensive computational tests are conducted to evaluate the effectiveness of the proposed algorithms. 

Blocking flowshop scheduling problems with release dates

In various flow shop scheduling problems, it is very common that a large-scale production is done. Under this situation, more factories are of more practical interest than a factory. Thus, the distributed permutation flow shop scheduling problems (DPFSPs) have been attracted attentions by researchers. However, the DPFSP is more complicated than the traditional flow shop scheduling problems. It considers not only the processing order of the jobs, but also how to distribute the jobs to multiple factories for parallel processing. In addition, the sequence- dependent setup time (SDST) constraint of machines is taken into account to well study the above DPFSP with SDST. This paper presents a simple and effective iterated greedy algorithm. It is proposed to replace the traditional insertion-based local search with exchange-based local search, which greatly improves the search efficiency. The proposed new iterated greedy (NIG) algorithm is applied to test instances, and compares with the state- of-the-art algorithms. Our empirical results demonstrate that the proposed algorithm outperforms the compared algorithms and can obtain the best solution of DPFSP.

An improved iterated greedy algorithm for the distributed flow shop scheduling problem with sequence-dependent setup times

This paper researches on knowledge retrieval, reuse and innovation in product conceptual design First, user’s design requirement is translated and decomposed using function-method tree Second, design case and constraint are used to describe a two-layer process of knowledge retrieval One is to get the similarity of case function through matching function units’ attribute values that is weighted Another, a similarity constraint retrieval algorithm is proposed for similarity case retrieval And then, Patch case is defined Patch case for user’s design requirement is attained through a patch algorithm Knowledge reuse and innovation is based on patch case At last, system framework and application are constructed to explain this process

Conceptual Design Knowledge Retrieval, Reuse and Innovation Based on Case and Constraint

In this paper, we study the path planning problem of the detection robot after the coal mine disaster, and arrange a robot to detect multiple target points. The objective is to minimize the path length and the degree of danger simultaneously. First, the A-Star algorithm is used to obtain the path matrix of any two target points. Then an improved multi-objective optimization algorithm is proposed to find paths with Pareto optimal or near-optimal solutions. Finally, the multi-objective optimization algorithm is compared with other multi-objective optimization algorithms. The experimental results demonstrate that the algorithm can effectively obtain the detection path and minimize the length of the path and the degree of danger.

An Improved Multi-objective Evolutionary Algorithm for A Robot Detect Path Planning Problem

In this paper, we study a distributed blocking flowshop scheduling problem (DBFSP) that is an extension of the traditional blocking flowshop scheduling problem (BFSP), in which an additional decision of which factory to process each job. We propose an iterated greedy (IG) algorithm to minimize makespan among all the factories. First of all, an effective initialization method based on the PW algorithm is used in order to make better use the problem-specific characteristics. Then, an enhanced construction method is developed to further improve the solution obtained at each iteration. At last, after calibration of algorithm parameters, comparison of algorithms is carried out using the well-known 720 instances from the literature. The results demonstrate the effectiveness of the proposed IG algorithm for solving the DBFSP with makespan criterion.

Quan-Ke Pan

Papers

Blocking flowshop scheduling problems with release dates

An improved iterated greedy algorithm for the distributed flow shop scheduling problem with sequence-dependent setup times

Conceptual Design Knowledge Retrieval, Reuse and Innovation Based on Case and Constraint

An Improved Multi-objective Evolutionary Algorithm for A Robot Detect Path Planning Problem

An Iterated Greedy Algorithm for Distributed Blocking Flowshop Problems with Makespan Minimization