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

Grey Wolf Optimizer

Data Mining Practical Machine Learning Tools and Techniques

The Whale Optimization Algorithm

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

Real-life problems are usually time-consuming since they require solving large instances of NP-hard problems. Exact search methods in most of the cases cannot afford practical solutions for such problems. Metaheuristics arise as promising solvers for these problems, by obtaining acceptable solutions in terms of quality and computational cost in a reasonable time bound. Nowadays, energy efficiency is also taken into consideration during the design of new algorithms because of the million times that algorithms run on labs and computation centers. This work presents two novel experiments for investigating the numerical performance and energy efficiency of the sequential and parallel metaheuristics. The main aim of this study is to analyze the energy consumption of three well-known and commonly used metaheuristics (Genetic Algorithm, Variable Neighborhood Search, and Simulated Annealing) and their parallel versions. The discussions reveal the differences/similarities between the different sequential/parallel algorithms, which include trajectory-based and population-based metaheuristics so that this study is useful for the future design of energy-aware algorithms.

Analyzing the Energy Consumption of Sequential and Parallel Metaheuristics

Many distributed systems (task scheduling,moving priorities,mobile environments, ...) can be linked as Dynamic Optimization Problems (DOPs), since they require to pursue an optimal value that changes over time. We have focused on the utilization of Distributed Genetic Algorithms (dGAs), one of the domains still to be investigated for DOPs. A dGA essentially decentralizes the population in islands which cooperate through migrations of individuals. In this article, we analyze the effect of the migrants selection and replacement on the performance of dGAs for DOPs. Quality and distance based criteria are tested using a comprehensive set of benchmarks. Results show the benefits and drawbacks of each setting for DOPs.

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Migrants Selection and Replacement in Distributed Evolutionary Algorithms for Dynamic Optimization

Yellow Swarm: LED panels to advise optimal alternative tours to drivers in the city of Malaga

In the last few years, metaheuristic approaches have shown an important development in many application areas. This situation has turned them into one of the more appropriate candidates when dealing with difficult real-world problems for which timely, good-quality solutions are necessary. Furthermore, the class of metaheuristic approaches includes a large number of variants and designs which mainly depend on the concepts from which they are inspired. This chapter aims at giving an overview of Evolutionary Algorithms and Ant Colony Optimization when applied to the two-dimensional strip packing problem. The respective performance of these two metaheuristics are analyzed and compared from different perspectives by implementing a Genetic Algorithm and an Ant Colony System.

Enrique Alba

Papers

Analyzing the Energy Consumption of Sequential and Parallel Metaheuristics

Migrants Selection and Replacement in Distributed Evolutionary Algorithms for Dynamic Optimization

Yellow Swarm: LED panels to advise optimal alternative tours to drivers in the city of Malaga

Evolutionary and Ant Colony Optimization Based Approaches for a Two-Dimensional Strip Packing Problem

Canonical Metaheuristics for Dynamic Optimization Problems