On the run-time behaviour of stochastic local search algorithms for SAT
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Stochastic Hillclimbing as a Baseline Method for
Stochastic Hillclimbing as a Baseline Method for
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
When a genetic algorithm outperforms hill-climbing
Theoretical Computer Science
Optimization and selection of cutters for 3D pocket machining
International Journal of Computer Integrated Manufacturing
Tool sequence optimisation using preferential multi-objective search
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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In this paper we discuss our approach to solving the tool selection problem, specifically applied to rough machining. A simulation is used to evaluate tool sequences, which provides accurate values for tool paths and a 3D model of the final machined part. This allows for a largely unrestricted search using different tool types, making this approach more useful for real world applications than previous attempts at solving the problem. An exhaustive search of every valid tool sequence is executed and shows that assumptions present in related research can prevent the optimal solution from being discovered. Metaheuristic algorithms are used to traverse the search space because of its complex combinatorial properties. Four algorithms are tested - Genetic Algorithm, Stochastic Hill Climbing, Hybrid Genetic Algorithm and Random Restart Stochastic Hill Climbing. Evaluating their performance at coping with two competing demands, finding optimal solutions and keeping the number of potentially expensive evaluations low, it is shown that RRSHC performs best in terms of solution accuracy but at the greatest computational cost. SHC finds the optimum sequence less frequently but needs far fewer evaluations and the HGA lies somewhere in between, making it a good choice if the problem domain is not well-specified.