Description
This book complements our previous book on Optimization Techniques, wherein readers are acquainted with various optimization methods rooted in theoretical concepts, aimed at solving real-world optimization challenges. Unlike the previous book, this one emphasizes computational techniques that do not rely on any particular mathematical optimization theory. Nature-Inspired Optimization Techniques have the capability to effectively tackle both conventional and practical real-world optimization problems without the need of prior knowledge about the problem?s context. These techniques do not demand the fulfilment of continuity and differentiability conditions of the objective function or constraints. Instead of starting with a single initial estimate, these techniques begin with a randomly generated set of feasible solutions, which are then progressively refined using specially crafted operators. They are more inclined to reach the global optimal solution(s) rather than settling for local optimal solution(s). Moreover, they are simple to implement and are integrated into many popular software packages.
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