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"This book presents a comprehensive review on latest research and development trends for design optimization of mechanical elements and devices. Using examples of various mechanical elements and devices, the possibilities for design optimization with advanced optimization techniques are demonstrated. Basic and advanced concepts of traditional and advanced optimization techniques are presented, along with real case studies, results of applications of the proposed techniques, and the best optimization strategies to achieve best performance are highlighted. Furthermore, a novel advanced optimization method named teaching-learning-based optimization (TLBO) is presented in this book and this method shows better performance with less computational effort for the large scale problems."

"This book is the first contemporary comprehensive treatment of optimization without derivatives, and it covers most of the relevant classes of algorithms from direct-search to model-based approaches. Readily accessible to readers with a modest background in computational mathematics, Introduction to Derivative-Free Optimization contains:1. a comprehensive description of the sampling and modeling tools needed for derivative-free optimization that allow the reader to better understand the convergent properties of the algorithms and identify their differences and similarities;2.analysis of convergence for modified Nelderead and implicit-filtering methods as well as for model-based methods such as wedge methods and methods based on minimumorm Frobenius models."

"Summary"Discusses problem formulation and problem solving with the help of algorithms such as secant method, quasi-Newton method, linear programming and dynamic programming"

"Industri pertambangan merupakan salah satu sektor pekerjaan paling berbahaya di dunia karena memiliki tingkat kecelakaan kerja yang tinggi, terutama kecelakaan fatal. Dalam 23 tahun terakhir, industri pertambangan mencatat 30.327 kecelakaan, dengan 357 berakhir dengan kematian pekerja. Machine learning dapat digunakan untuk memecahkan permasalahan dunia nyata yang kompleks, termasuk klasifikasi derajat cedera akibat kecelakaan kerja di industri pertambangan bawah tanah. Penelitian ini menggunakan metode machine learning Whale Optimization Algorithm Support Vector Machine (WOA-SVM), dengan Whale Optimization Algorithm (WOA) berperan sebagai optimizer untuk parameter model Support Vector Machine (SVM). Derajat cedera dibagi menjadi tiga kelas berdasarkan pengaruhnya terhadap produktivitas pekerja, yaitu no days away from work (NDAFW), days away from work (DAFW), dan disability or fatality (DF). Data yang digunakan berasal dari Mine Safety and Health Organization milik pemerintah Amerika Serikat sebanyak 28.520 kejadian kecelakaan dalam rentang 1 Januari 2000 hingga 31 Desember 2023. Sebelum mengimplementasikan model machine learning, dilakukan data preprocessing yang meliputi pembersihan data, transformasi data, sampling data, encoding data, penyeimbangan data, dan seleksi fitur. Kinerja model WOA-SVM dievaluasi menggunakan metrik akurasi, presisi, recall, dan F1-score dengan berbagai proporsi splitting data train dari 50% hingga 90%, serta mempertimbangkan waktu komputasi. Setelah itu dilakukan komparasi model WOA-SVM dengan model SVM tanpa optimisasi. Hasil komparasi menunjukkan bahwa model WOA-SVM lebih unggul dibandingkan model SVM, dengan keunggulan pada metrik konfusi, akurasi, presisi, recall, F1-score, serta memiliki waktu komputasi yang lebih cepat. Model WOA- ix Universitas Indonesia SVM memiliki nilai akurasi, presisi, recall, dan F1-score tertinggi pada proporsi 70:30, masing-masing sebesar 82,4153 %, 82,1255%, 82,4153%, dan 82,0812%.

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The mining industry is one of the most dangerous employment sectors in the world due to its high rate of workplace accidents, particularly fatal ones. Over the past 23 years, the mining industry has recorded 30,327 accidents, with 357 resulting in worker fatalities. Machine learning can be employed to address complex real-world problems, including the classification of injury severity resulting from workplace accidents in the underground mining industry. This study utilizes the Whale Optimization Algorithm Support Vector Machine (WOA-SVM) method, with the Whale Optimization Algorithm (WOA) acting as an optimizer for the parameters of the Support Vector Machine (SVM) model. The severity of injuries is divided into three classes based on their impact on worker productivity: no days away from work (NDAFW), days away from work (DAFW), and disability or fatality (DF). The data used comes from the Mine Safety and Health Organization's, managed by the U.S. government, encompassing 28,520 accident incidents from January 1, 2000, to December 31, 2023. Before implementing the machine learning model, data preprocessing was conducted, including data cleaning, data transformation, data sampling, data encoding, data balancing, and feature selection. The performance of the WOA-SVM model was evaluated using accuracy, precision, recall, and F1-score metrics with various train data splitting proportions ranging from 50% to 90%, while also considering computational time. A comparison was then made between the WOA-SVM model and the non-optimized SVM model. The comparison results indicated that the WOA-SVM model outperformed the SVM model, with superiority in confusion metrics, accuracy, precision, recall, F1-score, and having the fastest computational time. The WOA-SVM model has the highest accuracy, precision, recall, xi Universitas Indonesia and F1-score values at a 70:30 ratio, which are 82.4153%, 82.1255%, 82.4153%, and 82.0812%, respectively."

"This book presents these fields in interdependence as a conversation between theoretical aspects of mathematics and computer science and the mathematical field of optimization theory at a practical level. The 19 case studies that were conducted by the author in real enterprises in cooperation and co-authorship with some of the leading industrial enterprises, including RWE, Vattenfall, EDF, PetroChina, Vestolit, Sasol, and Hella, illustrate the results that may be reasonably expected from an optimization project in a commercial enterprise. "