Multi-Objective Genetic Algorithms for Vehicle Routing Problem with Time Windows
Applied Intelligence
Maintaining diversity through adaptive selection, crossover and mutation
Proceedings of the 10th annual conference on Genetic and evolutionary computation
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Expert Systems with Applications: An International Journal
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EvoWorkshops '09 Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing: EvoCOMNET, EvoENVIRONMENT, EvoFIN, EvoGAMES, EvoHOT, EvoIASP, EvoINTERACTION, EvoMUSART, EvoNUM, EvoSTOC, EvoTRANSLOG
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows
Computers and Operations Research
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IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
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This paper presents an adaptive genetic algorithm (GA) to solve the Vehicle Routing Problem with Time Windows (VRPTW) to near optimal solutions. The algorithm employs a unique decoding scheme with the integer strings. It also automatically adapts the crossover probability and the mutation rate to the changing population dynamics. The adaptive control maintains population diversity at user-defined levels, and therefore prevents premature convergence insearch. Comparison between this algorithm and a normal fixed parameter GA clearly demonstrates the advantage of population diversity control. Our experiments with the 56 Solomon benchmark problems indicate that this algorithm is competitive and it paves way for future research on population-based adaptive genetic algorithm.