Deep Statistical Comparison for Meta-heuristic Stochastic Optimization Algorithms

Focusing oncomprehensive comparisonsof the performance of stochastic optimization algorithms, this book provides an overview of the current approachesused to analyzealgorithm performancein a range of commonscenarios, while also addressingissues that are often overlooked.In turn, itshows how these issues can be easily avoided by applyingtheprinciplesthat have producedDeep Statistical Comparison and its variants. The focus is on statistical analyses performed using single-objective and multi-objective optimization data. At the end of the book, examplesfroma recently developed web-service-based e-learning tool(DSCTool) arepresented. The toolprovides users with all the functionalities needed to makerobust statistical comparison analysesinvariousstatistical scenarios.The book isintendedfornewcomers to the field and experienced researchers alike. For newcomers, it coversthe basicsofoptimization and statistical analysis,familiarizing themwith thesubject matterbefore introducingthe Deep Statistical Comparison approach. Experienced researcherscan quickly move on to the content on newstatistical approaches.The book is dividedinto three parts: Part I: Introduction to optimization, benchmarking, and statistical analysis - Chapters 2-4. Part II: Deep Statistical Comparison of meta-heuristic stochastic optimization algorithms - Chapters 5-7. Part III: Implementation and applicationof DeepStatistical Comparison - Chapter 8.