Analyzing the Run � time Behaviour of Iterated Local Search for the TSP
نویسنده
چکیده
Metaheuristics strongly involve random decisions during the search process Such random deci sions are for example due to random initial solutions randomized tie breaking criteria randomized sampling of neighborhoods probabilistic acceptance criteria and many more Because of their nonde terministic nature the run time required by such an algorithm to achieve a speci c goal like nding an optimal solution to a given problem instance is a random variable Obviously knowledge on the distribution of this random variable can give valuable information for the analysis and the character ization of an algorithm s behavior provide a basis for the comparison of algorithms and give hints on possible improvements of an algorithm s performance To obtain empirical knowledge on the run time distribution RTD of an algorithm when applied to a speci c problem instance one may estimate the RTD from data collected over several runs of the algorithm and possibly approximate the empirically observed RTD by a distribution function known from probability theory If similar behav ior is observed on all tested instances from a particular problem class for example on Euclidean TSP instances the observed type of RTDs characterizes the run time behavior on this problem class The RTDs may also give an indication under which conditions an algorithm may be improved As we will later explain the exponential distribution plays a crucial role for judging an algorithm s e ectiveness
منابع مشابه
MIC ' 99 - III Metaheuristics International Conference 1 Analyzing the Run - time Behaviour of Iterated Local Search for the TSPThomas St
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تاریخ انتشار 2003