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

A process control method for secondary processing of sintered NdFeB in a quick-setting furnace belongs to the field of information technology. On the basis of product grades, grades of products processed in the quick-setting furnace are recorded and monitored. When the product grades change from non-common grades into common grades or are not the common grades any more, weight of casting strip heat is re-set according to the number of usage of an inner crucible and a tundish of the quick-setting furnace. Products in the corresponding grades are charged according to the set weight, and the NdFeB casting strips are sintered continuously according to the number of charges. Residual magnetism (Br) and magnetic polarization intensity coercive force (intrinsic coercive force) (Hcj) of the casting strips are increased effectively, oxidation and damp of the casting strips due to long-term placement are reduced, energy waste in quick-setting process is reduced, and results satisfying field staff are achieved.

Process control method for secondary processing of sintered NdFeB in quick-setting furnace

Steelmaking-refining-Continuous Casting (SCC) scheduling is an important industrial scheduling problem, which is also a NP-hard combinatorial optimization problem. Efficient SCC scheduling methods can significantly improve the productivity efficiency. In this paper, an efficient Iterated Local Search (ILS) algorithm is devised for SCC scheduling. The ILS starts from an initial solution with certain quality, then executes a multiswap-based local search, subsequently carries out a cycle of perturbation with probability, neighborhood changing, Simulated Annealing (SA)-based local search, and acceptance criterion until termination condition is reached. The SA-based local search is devised to enhance the local search ability of the ILS, while the perturbation with probability is designed to guide the search to a promising area. Comparison experiments with several scheduling algorithms have shown the strength and effectiveness of the ILS.

Iterated Local Search for Steelmaking-refining-Continuous Casting Scheduling Problem

In this paper, the steelmaking-continuous casting problem with variable processing times is addressed. We present a modified migrating birds optimization (MMBO) to deal with this problem within a reasonable time. For the addressed problem, we use the job permutation to represent the solution and give a detailed decoding process. For the employed algorithm, we introduce a various neighborhood strategy to explore the solution space more widely. And the benefit mechanism is modified to take full advantage of the promising solutions. To evaluate the performance of our proposed algorithm, the other three meta-heuristics are compared and the computational results demonstrate the effectiveness of the MMBO.

A modified migrating birds optimization for solving the steelmaking-continuous casting problem with variable processing times

This paper proposes an effective method for the no-wait flow-shop scheduling optimization based on harmony search (HS). Flow-shop scheduling problem (FSSP) is a typical NP-hard combinational optimization. The purpose of this paper is the total flow time criterion. Firstly, the HS-based optimization mechanism and framework is presented. Secondly, the total flow time is calculated by a novel method. Thirdly, a largest-order-value rule is used to transform harmony in harmony memory from real vectors to job sequence so that the harmony search can be applied for FSSP. Improvising rule of new harmony is expatiated and high effective algorithm parameters are set for optimization object. At last, simulations and comparisons demonstrate the efficiency, effectiveness and robustness of harmony search algorithm for no-wait FSSP.

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

The distributed assembly flowshop scheduling problem has important applications in modern industries. For solving the problem with the objective of minimizing total flowtime, this paper proposes an effective fruit fly optimization (FFO) algorithm with a number of parallel swarms, each of which has its own osphresis foraging and vision foraging stages. The swarms are initialized by a LPT -based heuristic and find food sources using a hybrid operator which is a combination of the product-based and part-based insertion operators. A specific relinking procedure is presented to improve the cooperative search capability. To evaluate the proposed algorithm, we conduct a comprehensive set of computational and statistical experiments based on 910 benchmark instances. The results show that the proposed FFO algorithm performs significantly better than the existing methods in comparison.

Quan-Ke Pan

Papers

Process control method for secondary processing of sintered NdFeB in quick-setting furnace

Iterated Local Search for Steelmaking-refining-Continuous Casting Scheduling Problem

A modified migrating birds optimization for solving the steelmaking-continuous casting problem with variable processing times

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

An Effective Fruit Fly Optimization for the Distributed Assembly Flowshop Scheduling Problem