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

Recent developments in fitness landscape analysis include the study of Local Optima Networks (LON) and applications of the Elementary Landscapes theory. This paper represents a first step at combining these two tools to explore their ability to forecast the performance of search algorithms. We base our analysis on the Quadratic Assignment Problem (QAP) and conduct a large statistical study over 600 generated instances of different types. Our results reveal interesting links between the network measures, the autocorrelation measures and the performance of heuristic search algorithms.

Local Optima Networks, Landscape Autocorrelation and Heuristic Search Performance

A smart meter enables electric utilities to get detailed insights into their customer needs, allowing them to offer tailored products and services, and to succeed in an increasingly competitive market. While in an ideal world companies would treat every customer as an individual, in practice this is rather difficult. For this reason, companies usually have to target smaller groups of customers that are similar. There are several ways of tackling this matter and finding the right approach is a key to success. Therefore, in this study we introduce the electricity demand signature, a novel approach to characterize and cluster electricity customers based on their demand habits. We test our proposal using the electricity demand of 64 buildings in Andalusia, Spain, and compare our results to the state-of-the-art. The results show that our proposal is useful for clustering customers in a meaningful way, and that it is an easy and friendly representation of the behavior of a customer that can be used for further analysis.

Customer Segmentation Based on the Electricity Demand Signature: The Andalusian Case

Classical cooperative parallel models for metaheuristics have one major issue when the underlying search method is based on the exploration of the neighborhood of one single solution, i.e., a trajectory-based metaheuristic. Whenever a cooperation step takes place by exchanging solutions, either the incoming or the local solution has to be discarded because the subalgorithm does only work with one single solutions. Therefore, important information may be lost. A recent new parallel model for trajectory-based metaheuristics has faced this issue by adding a crossover operator that is aimed at combining valuable information from both the incoming and the local solution. This work is targeted to further evaluate this parallel model by addressing two well-known, hard optimization problems (MAXSAT and RND) using Simulated Annealing as the search method in each subalgorithm. The results have shown that the new model is able to outperform the classical cooperative method under the experimental conditions used.

Exploring the accuracy of a parallel cooperative model for trajectory-based metaheuristics

Planning a cellular phone network makes engineers to face a number of challenging optimization problems This paper addresses the solution of one of these problems, Automatic Cell Planning (ACP), which lies in positioning the antennae of the network and configuring them properly in order to meet several objectives and constraints This paper approaches, for the first time, the ACP problem with a Scatter Search technique The algorithm used is called AbYSS Three large-scale real-world instances have been used for evaluating the search capabilities of AbYSS on this optimization problem The results shows that AbYSS not only reaches very accurate solutions for the three instances, but also it scales well with increasingly sized instances.

A scatter search approach for solving the automatic cell planning problem

The aim of the work presented here is to reduce gas emissions in modern cities by creating a light infrastructure of WiFi intelligent spots informing drivers of customized, real-time routes to their destinations. The reduction of gas emissions is an important aspect of smart cities, since it directly affects the health of citizens as well as the environmental impact of road traffic. We have built a real scenario of the city of Malaga (Spain) by using OpenStreetMap (OSM) and the SUMO road traffic microsimulator, and solved it by using an efficient new Evolutionary Algorithm (EA). Thus, we are dealing with a real city (not just a roundabout, as found in the literature) and we can therefore measure the emissions of cars in movement according to traffic regulations (real human scenarios). Our results suggest an important reduction in gas emissions (10%) and travel times (9%) is possible when vehicles are rerouted by using the Red Swarm architecture. Our approach is even competitive with human expert’s solutions to the same problem.

Enrique Alba

Papers

Local Optima Networks, Landscape Autocorrelation and Heuristic Search Performance

Customer Segmentation Based on the Electricity Demand Signature: The Andalusian Case

Exploring the accuracy of a parallel cooperative model for trajectory-based metaheuristics

A scatter search approach for solving the automatic cell planning problem

Reducing Gas Emissions in Smart Cities by Using the Red Swarm Architecture