A Statistical Process Control-Based Heuristic Optimization Algorithm
نویسنده
چکیده
Traditional optimization techniques such as Dynamic Programming(DP), Linear Programming(LP), and MixedInteger Programming(MIP) have been used to solve complex optimization problems in many diverse fields. Shortcomings of these classical techniques and the rapid development of affordable, powerful computing technology have led to the creation of new classes of heuristic-based optimizing techniques. These techniques exist in general purpose and problem-specific forms. This paper presents a new heuristic based upon concepts from Statistical Process Control. Novel features of this heuristic include problem adaptive algorithm tuning and a two-stage solution approach involving genetic algorithms. Computational results and heuristic performance of the new algorithm are illustrated. Comparisons of algorithm performance relative to a Simulated Annealing algorithm are also illustrated.
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