In the (possible or known) presence of many local optima, the goal of global optimization is to identify the model's (potentially nonlinear) globally optimal solution. Formally, global optimization looks for one or more global solutions to an optimization model with constraints. Nonlinear models are frequently used in a wide range of fields, including scientific modelling, advanced engineering design, biotechnology, data analysis, environmental management, financial planning, process control, and risk management. They frequently need a global search strategy to find a solution.
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