Accordingly, modern software packages that perform signal timing “optimization” for arterial or network coordination employ a search algorithm that attempts to find the best solution amongst a greatly reduced number of variable-value combinations. This allows a solution to be found within a reasonable amount of time, using commonly available computing hardware. However, because these algorithms do not perform an exhaustive search of all of the variable-value combinations (as is generally required to find a truly optimal solution for this type of problem), the identified solution is considered to be only a “local” optimum rather than a “global” optimum. So while the local optimum solution may provide reasonable results, it is unlikely that this particular set of variable settings will provide the best results. Nonetheless, with the tremendous increase in desktop computing power over recent decades, the differences between a local optimum and global optimum solution have decreased, as the search space (the set of variable values considered by the solution algorithm) has accordingly been expanded.
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