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

This paper proposes a novel population-based optimization algorithm called Sine Cosine Algorithm (SCA) for solving optimization problems. The SCA creates multiple initial random candidate solutions and requires them to fluctuate outwards or towards the best solution using a mathematical model based on sine and cosine functions. Several random and adaptive variables also are integrated to this algorithm to emphasize exploration and exploitation of the search space in different milestones of optimization. The performance of SCA is benchmarked in three test phases. Firstly, a set of well-known test cases including unimodal, multi-modal, and composite functions are employed to test exploration, exploitation, local optima avoidance, and convergence of SCA. Secondly, several performance metrics (search history, trajectory, average fitness of solutions, and the best solution during optimization) are used to qualitatively observe and confirm the performance of SCA on shifted two-dimensional test functions. Finally, the cross-section of an aircraft's wing is optimized by SCA as a real challenging case study to verify and demonstrate the performance of this algorithm in practice. The results of test functions and performance metrics prove that the algorithm proposed is able to explore different regions of a search space, avoid local optima, converge towards the global optimum, and exploit promising regions of a search space during optimization effectively. The SCA algorithm obtains a smooth shape for the airfoil with a very low drag, which demonstrates that this algorithm can highly be effective in solving real problems with constrained and unknown search spaces. Note that the source codes of the SCA algorithm are publicly available at http://www.alimirjalili.com/SCA.html .

SCA: A Sine Cosine Algorithm for solving optimization problems

Salp Swarm Algorithm

In this paper a novel nature-inspired optimization paradigm is proposed called Moth-Flame Optimization (MFO) algorithm. The main inspiration of this optimizer is the navigation method of moths in nature called transverse orientation. Moths fly in night by maintaining a fixed angle with respect to the moon, a very effective mechanism for travelling in a straight line for long distances. However, these fancy insects are trapped in a useless/deadly spiral path around artificial lights. This paper mathematically models this behaviour to perform optimization. The MFO algorithm is compared with other well-known nature-inspired algorithms on 29 benchmark and 7 real engineering problems. The statistical results on the benchmark functions show that this algorithm is able to provide very promising and competitive results. Additionally, the results of the real problems demonstrate the merits of this algorithm in solving challenging problems with constrained and unknown search spaces. The paper also considers the application of the proposed algorithm in the field of marine propeller design to further investigate its effectiveness in practice. Note that the source codes of the MFO algorithm are publicly available at http://www.alimirjalili.com/MFO.html.

Moth-flame optimization algorithm

This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

In nature, the eastern North American monarch population is known for its southward migration during the late summer/autumn from the northern USA and southern Canada to Mexico, covering thousands of miles. By simplifying and idealizing the migration of monarch butterflies, a new kind of nature-inspired metaheuristic algorithm, called monarch butterfly optimization (MBO), a first of its kind, is proposed in this paper. In MBO, all the monarch butterfly individuals are located in two distinct lands, viz. southern Canada and the northern USA (Land 1) and Mexico (Land 2). Accordingly, the positions of the monarch butterflies are updated in two ways. Firstly, the offsprings are generated (position updating) by migration operator, which can be adjusted by the migration ratio. It is followed by tuning the positions for other butterflies by means of butterfly adjusting operator. In order to keep the population unchanged and minimize fitness evaluations, the sum of the newly generated butterflies in these two ways remains equal to the original population. In order to demonstrate the superior performance of the MBO algorithm, a comparative study with five other metaheuristic algorithms through thirty-eight benchmark problems is carried out. The results clearly exhibit the capability of the MBO method toward finding the enhanced function values on most of the benchmark problems with respect to the other five algorithms. Note that the source codes of the proposed MBO algorithm are publicly available at GitHub (
 https://github.com/ggw0122/Monarch-Butterfly-Optimization
 , C++/MATLAB) and MATLAB Central (
 http://www.mathworks.com/matlabcentral/fileexchange/50828-monarch-butterfly-optimization
 , MATLAB).

Monarch butterfly optimization

Phototaxis, signifying movement of an organism towards or away from a source of light, is one of the most representative features for moths. It has recently been shown that one of the characteristics of moths has been the propensity to follow Levy flights. Inspired by the phototaxis and Levy flights of the moths, a new kind of metaheuristic algorithm, called moth search (MS) algorithm, is developed in the present work. In nature, moths are a family insects associated with butterflies belonging to the order Lepidoptera. In MS method, the best moth individual is viewed as the light source. Some moths that are close to the fittest one always display an inclination to fly around their own positions in the form of Levy flights. On the contrary, due to phototaxis, the moths that are comparatively far from the fittest one will tend to fly towards the best one directly in a big step. These two features correspond to the processes of exploitation and exploration of any metaheuristic optimization method. The phototaxis and Levy flights of the moths can be used to build up a general-purpose optimization method. In order to demonstrate the superiority of its performance, the MS method is further compared with five other state-of-the-art metaheuristic optimization algorithms through an array of experiments on fourteen basic benchmarks, eleven IEEE CEC 2005 complicated benchmarks and seven IEEE CEC 2011 real world problems. The results clearly demonstrate that MS significantly outperforms five other methods on most test functions and engineering cases.

Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems

In this paper, a new kind of swarm-based metaheuristic search method, called Elephant Herding Optimization (EHO), is proposed for solving optimization tasks The EHO method is inspired by the herding behavior of elephant group In nature, the elephants belonging to different clans live together under the leadership of a matriarch, and the male elephants will leave their family group when they grow up These two behaviors can be modelled into two following operators: clan updating operator and separating operator In EHO, the elephants in each clan are updated by its current position and matriarch through clan updating operator It is followed by the implementation of the separating operator which can enhance the population diversity at the later search phase To demonstrate its effectiveness, EHO is benchmarked by fifteen test cases comparing with BBO, DE and GA The results show that EHO can find the better values on most benchmark problems than those three metaheuristic algorithms

Elephant Herding Optimization

Chaotic Krill Herd algorithm

With the development of the Internet, malicious code attacks have increased exponentially, with malicious code variants ranking as a key threat to Internet security. The ability to detect variants of malicious code is critical for protection against security breaches, data theft, and other dangers. Current methods for recognizing malicious code have demonstrated poor detection accuracy and low detection speeds. This paper proposed a novel method that used deep learning to improve the detection of malware variants. In prior research, deep learning demonstrated excellent performance in image recognition. To implement our proposed detection method, we converted the malicious code into grayscale images. Then, the images were identified and classified using a convolutional neural network (CNN) that could extract the features of the malware images automatically. In addition, we utilized a bat algorithm to address the data imbalance among different malware families. To test our approach, we conducted a series of experiments on malware image data from Vision Research Lab. The experimental results demonstrated that our model achieved good accuracy and speed as compared with other malware detection models.

Gai-Ge Wang

Papers

Monarch butterfly optimization

Moth search algorithm: a bio-inspired metaheuristic algorithm for global optimization problems

Elephant Herding Optimization

Chaotic Krill Herd algorithm

Detection of Malicious Code Variants Based on Deep Learning