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 addresses to the lot-streaming flow shop scheduling problem with makespan criterion. An effective shuffled frog-leaping algorithm (SFLA) with job permutation based representation is proposed. Extensive computational experiments and comparisons demonstrate the effectiveness of the proposed SFLA.

Scheduling the lot-streaming flow shop problem using a shuffled frog-leaping algorithm

An effective tabu search algorithm is proposed for solving No-idle permutation Flow Shop Scheduling Problem in this paper. The objective is to minimize the maximum completion time (MakeSpan) of the problem. The Tabu Search Algorithm starts to search from an initial solution generated by the famous NEH heuristic. The dynamic feature of the tabu list length is applied to improve the robustness of the algorithm. In order to further improve the capability of the algorithm, a detailed local search method is introduced. Simulation results show the effectiveness and superiority of the algorithm.

Tabu search algorithm for solving No-idle permutation Flow Shop Scheduling Problem

In this paper, we present a variable iterated greedy (vIGP_DE) algorithm where its parameters (basically destruction size and cooling parameter for the simulated annealing type of acceptance criterion) are optimized by the differential evolution algorithm. A unique multi-chromosome solution representation is presented such that first chromosome represents the destruction size and cooling parameter of the iterated greedy algorithm while second chromosome is simply a permutation assigned to each individual in the population randomly. As an application area, we choose to solve the no-idle permutation flowshop scheduling problem with the total flowtime criterion. To the best of our knowledge, the no-idle permutation flowshop problem hasn't yet been studied thought it's a variant of the well-known permutation flowshop scheduling problem. The performance of the vIGP_DE algorithm is tested on the Taillard's benchmark suite and compared to a very recent variable iterated greedy algorithm from the existing literature. The computational results show its highly competitive performance and ultimately, we provide the best known solutions for the total flowtime criterion for the Taillard's benchmark suit.

A DE based variable iterated greedy algorithm for the no-idle permutation flowshop scheduling problem with total flowtime criterion

In this paper an improved genetic algorithm (GA) was present for magnetic material two-stage, multi-product, production scheduling problem (TMPS) with parallel machines. TMPS was changed into molding-stage's multi-product production scheduling problem (MMPS) and the scheduling model was set up for the first time. A set of random solutions were explored first, better feasible solutions were obtained by GA. To shorten the solving time and improve solution accuracy, an improved GA was proposed. We improved GA's crossover operator, adopted heuristic greedy 3PM crossover operator (HG3PMCO) to reduce GA's computational time. Through contrast of computational results of MILP, general GA and improved GA, the improved GA has demonstrated its effectiveness and reliability in solving the molding sintering production scheduling problems and the MILP model set up for the first time is reasonable. At last, the improved genetic algorithm was used in molding stage and sintering stage TMPS of magnetic material.

Improved genetic algorithm for magnetic material two-stage multi-product production scheduling: A case study

In this paper, a novel optimized trust-based search approach was proposed for the peer-to-peer (P2P) platform. In traditional trust-based or recommendation P2P system, the trust or recommendation value of a peer is considered when selecting it for forwarding messages or downloading resources, while the other factors are ignored, such as good download rate, good response rate, and on-line time similarity. In the proposed optimized approach, we consider all possible factors which affect the search quality in the P2P system. The proposed algorithm is developed on the well-known QueryCycle platform. Experiment results show the efficiency of our approach.

Quan-Ke Pan

Papers

Scheduling the lot-streaming flow shop problem using a shuffled frog-leaping algorithm

Tabu search algorithm for solving No-idle permutation Flow Shop Scheduling Problem

A DE based variable iterated greedy algorithm for the no-idle permutation flowshop scheduling problem with total flowtime criterion

Improved genetic algorithm for magnetic material two-stage multi-product production scheduling: A case study

An Effective Trust-Based Search Approach in Peer-to-Peer Network