New Perspectives on Optimization Algorithms Research
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Optimization processes are present in nearly all human activities, aiming to reduce time and expenses, conserve resources, and maximize efficiency. Classical numerical optimization methods were initially employed in more complex or elaborate problems. This class of methods was convenient to use when equations could correctly and completely formulate all the attributes of the problem to be optimized and whose search space for the solution had convexity characteristics and was not multimodal. Expressing a problem completely through equations is often a difficult (if not impossible) task because ...
Optimization processes are present in nearly all human activities, aiming to reduce time and expenses, conserve resources, and maximize efficiency. Classical numerical optimization methods were initially employed in more complex or elaborate problems. This class of methods was convenient to use when equations could correctly and completely formulate all the attributes of the problem to be optimized and whose search space for the solution had convexity characteristics and was not multimodal. Expressing a problem completely through equations is often a difficult (if not impossible) task because linguistic values or employing quantities that are not easily dealt with by classical optimization methods can only describe certain limitations and barriers. Irregular and non-convex search spaces also present problems for classical methods, especially those with multiple local minima and maxima, as they can lead to suboptimal solutions. In this context, researchers in optimization have presented new techniques to overcome these barriers, introducing new concepts and methods that enable working with problems that are not well-defined and in multimodal search spaces. Many of these techniques utilize artificial intelligence methods, and it is possible to optimize problems where their limitations are not clearly defined or where their search spaces contain different local maxima and minima. This book is a contribution in this sense. In it, the reader will find two sections. The first, titled "Methodologies", presents the frontier of state-of-the-art optimization techniques and their applications to various problems. The second section, titled "Applications," presents examples of practical applications of modern techniques in optimizing issues, which can be easily adapted to others with similar characteristics. Thus, the reader can use this book to optimize their problems by applying the solutions presented in the chapters and adapting them to their specific issues.
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