Global Optimization provides methods that search for global solutions to problems that contain multiple maxima or minima. The objective of global optimization is to find the globally best solution of models, in the presence of multiple local optima. It includes global search, multistart, pattern search, genetic algorithm, and simulated annealing solvers Genetic algorithm and pattern search solvers support algorithmic customization. Formally, global optimization seeks global solution(s) of a constrained optimization model. Nonlinear models are ubiquitous in many applications, e.g., in advanced engineering design, biotechnology, data analysis, environmental management, financial planning, process control, risk management, scientific modeling, and others.
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Following is the list of topics under Global Optimization which is prepared after detailed analysis of courses taught in multiple universities across the globe: