Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Fitness Inheritance In Multi-objective Optimization
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Intelligent evolutionary algorithms for large parameter optimization problems
IEEE Transactions on Evolutionary Computation
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This paper describes a multi-objective memetic approach for solving multi-objective flexible process sequencing problems in flexible manufacturing systems (FMSs). FMS can be described as an integrated manufacturing system consisting of machines, computers, robots, tools, and automated guided vehicles (AGVs).FMSs usually pose complex problems on process sequencing of operations among multiple parts. An efficient multi-objective memetic algorithm with fitness inheritance mechanism is proposed to solve flexible process problems (FPSs) with the consideration the machining time of operations and machine workload load balancing. The experimental results demonstrate that our approach can efficiently solve FPSs and fitness inheritance can speed up the convergence speed of the proposed algorithm in solving FPSs.