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Given a transformation between input and output values, described by a mathematical function, optimization deals with generating and selecting the best solution from some set of available alternatives, by systematically choosing input values from within an allowed set, computing the output of the function and recording the best output values found during the process.
Given a system transforming a set of inputs to output values, described by a mathematical function f, optimization refers to the generation and selection of the best solution from some set of available alternatives, [1] by systematically choosing input values from within an allowed set, computing the value of the function, and recording the best value found during the process.
S. Search-based software engineering; Second-order cone programming; Semidefinite embedding; Sequential linear-quadratic programming; Sequential minimal optimization
Heap-Based Optimizer HBO Nature-inspired Human-based 2020 [85] Color Harmony Algorithm CHA Art-inspired Color-based 2020 [86] Stochastic Paint Optimizer SPO Art-inspired Color-based 2020 [87] Mayfly Optimization Algorithm MOA Nature-inspired Bio-inspired 2020 [88] Giza Pyramids Construction GPC Ancient-inspired - 2020 [89] Fire Hawk Optimizer FHO
Optimization problems are often multi-modal; that is, they possess multiple good solutions. They could all be globally good (same cost function value) or there could be a mix of globally good and locally good solutions. Obtaining all (or at least some of) the multiple solutions is the goal of a multi-modal optimizer.
An automatic optimizer (or optimizing compiler, a program that performs code optimization) may itself have to be optimized, either to further improve the efficiency of its target programs or else speed up its own operation. A compilation performed with optimization "turned on" usually takes longer, although this is usually only a problem when ...