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

This paper proposes an effective discrete chemical-reaction optimization (DCRO) algorithm for solving the flexible job-shop scheduling problems with maintenance activity constraints Three minimization objectives-the maximum completion time (makespan), the total workload of machines and the workload of the critical machine are considered simultaneously In the proposed algorithm, each solution is represented by a chemical molecule Four improved elementary reactions, ie, on-wall ineffective collision, inter-molecular ineffective collision, decomposition, and synthesis, are developed A well-designed crossover function is introduced in the inter-molecular collision, synthesis, and decomposition operators Tabu search (TS) based local search is embedded in DCRO to perform exploitation process In addition, the decoding mechanism considering the maintenance activity is presented Several neighboring approaches are developed to improve the local search ability of the DCRO The proposed algorithm is tested on sets of the well-known benchmark instances Through the analysis of experimental results, the highly effective performance of the proposed DCRO algorithm is shown against the best performing algorithms from the literature

Chemical-reaction optimization for flexible job-shop scheduling problems with maintenance activity

Distributed assembly permutation flowshop scheduling problem (DAPFSP) has important applications in modern assembly systems. In this paper, we present three variants of the discrete invasive weed optimization (DIWO) for the DAPFSP with total flowtime criterion. For solving such a problem, we present a two-level representation that consists of a product permutation and a number of job sequences. We introduce neighbourhood operators for both the product permutation and job sequences. We design effective local search procedures respectively for product-permutation-based neighbourhood and job-sequence-based neighbourhood. By combining the problem-specific knowledge and the idea of invasive weed optimization, we present three DIWO-based algorithms: a two-level discrete invasive weed optimization (TDIWO), a discrete invasive weed optimization with hybrid search operators (HDIWO), and a HDIWO with selection probability. The algorithms explore the two neighbourhoods in quite a different way. We calibrate the presented DIWO algorithms by means of the design of experimental method, and carry out a comprehensive computational campaign based on the 810 benchmark instances in the literature. The numerical experiments show that the presented DIWO algorithms perform significantly better than the other competing algorithms in the literature. Among the proposed algorithms, HDIWO is the best one.

Effective invasive weed optimization algorithms for distributed assembly permutation flowshop problem with total flowtime criterion

This article presents a local-best harmony search algorithm with dynamic subpopulations (DLHS) for solving the bound-constrained continuous optimization problems. Unlike existing harmony search algorithms, the DLHS algorithm divides the whole harmony memory (HM) into many small-sized sub-HMs and the evolution is performed in each sub-HM independently. To maintain the diversity of the population and to improve the accuracy of the final solution, information exchange among the sub-HMs is achieved by using a periodic regrouping schedule. Furthermore, a novel harmony improvisation scheme is employed to benefit from good information captured in the local best harmony vector. In addition, an adaptive strategy is developed to adjust the parameters to suit the particular problems or the particular phases of search process. Extensive computational simulations and comparisons are carried out by employing a set of 16 benchmark problems from the literature. The computational results show that, overall, the proposed D...

A local-best harmony search algorithm with dynamic subpopulations

An improved migrating birds optimisation for a hybrid flowshop scheduling with total flowtime minimisation

In this study, we propose a hybrid fruit fly optimization algorithm (HFOA) to solve the hybrid flowshop rescheduling problem with flexible processing time in steelmaking casting systems. First, machine breakdown and processing variation disruptions are considered simultaneously in the rescheduling problem. Second, each solution is represented by a fruit fly with a well-designed solution representation. Third, two novel decoding heuristics considering the problem characteristics, which can significantly improve the solution quality, are developed. Several routing and scheduling neighborhood structures are proposed to balance the exploration and exploitation abilities. Finally, we propose an effective HFOA with well-designed smell and vision search procedures. In addition, an iterated greedy (IG) local search is embedded in the proposed algorithm to further enhance its exploitation ability. The proposed algorithm is tested on sets of instances generated from industrial data. Through comprehensive computational comparisons and statistical analyses, the performance of the proposed HFOA algorithm is favorably compared against several algorithms in terms of both solution quality and efficiency.

Quan-Ke Pan

Papers

Chemical-reaction optimization for flexible job-shop scheduling problems with maintenance activity

Effective invasive weed optimization algorithms for distributed assembly permutation flowshop problem with total flowtime criterion

A local-best harmony search algorithm with dynamic subpopulations

An improved migrating birds optimisation for a hybrid flowshop scheduling with total flowtime minimisation

A Hybrid Fruit Fly Optimization Algorithm for the Realistic Hybrid Flowshop Rescheduling Problem in Steelmaking Systems