Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
Computers and Operations Research
Multiobjective Evolutionary Algorithms: Analyzing the State-of-the-Art
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Intelligent evolutionary algorithms for large parameter optimization problems
IEEE Transactions on Evolutionary Computation
Research of multi-population agent genetic algorithm for feature selection
Expert Systems with Applications: An International Journal
Model-free adaptive control design using evolutionary-neural compensator
Expert Systems with Applications: An International Journal
Two coding based adaptive parallel co-genetic algorithm with double agents structure
Engineering Applications of Artificial Intelligence
Expert Systems with Applications: An International Journal
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Nano-particle materials have been widely applied in many industries and the wet-type mechanical milling process is a popular powder technology to produce the nano-particles. Since the milling process involves a number of process parameters and the multi-objective quality criteria, it is very important to set the optimal milling process parameters in order to achieve the desired multiple quality criteria. In this study, a new multi-objective evolutionary algorithm (MOEA), called the multi-population intelligent genetic algorithm (MPIGA), is proposed to find the optimal process parameters for the nano-particle milling process. In the new method, the orthogonal array (OA) experiment is first applied to obtain the analytic data of the milling process. Then the response surface method (RSM) is applied to model the nano-particle milling process and to determine the objective (fitness) value. The generalized Pareto-based scale-independent fitness function (GPSIFF) is then used to evaluate the Pareto solutions. Finally, the MPIGA is proposed to find the Pareto-optimal solutions. The results show that the integrated MPIGA approach can generate the Pareto-optimal solutions for the decision maker to determine the optimal parameters and to achieve the desired product qualities for a nano-particle milling process.