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An improved multi-objective optimization method based on adaptive mutation particle swarm optimization and fuzzy statistics algorithm

机译:基于自适应变异粒子群优化和模糊统计算法的改进多目标优化方法

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摘要

This paper proposes an adaptive mutation particle swarm optimization (AMPSO) to realize multi-objective optimization design method through scale-based product platform theory model. The Pareto-optimal solution was obtained via AMPSO, then the fuzzy statistics algorithm is presented to extract the optimal solution of multi-objective optimization problem. The Multi-objective Optimization Method was carried out in two stages. In the first stage, each product is optimized independently via AMPSO and the product platform constant parameter and its value is obtained according to the change ratio of design variables; In the second stage, the scaling variables of each product are solved via AMPSO based on the optimization objectives improving the performance in constraint of restrictions and the best compromise solution is extracted based on fuzzy statistics algorithm.
机译:提出了一种自适应的变异粒子群算法(AMPSO),通过基于规模的产品平台理论模型实现多目标优化设计方法。通过AMPSO求出Pareto最优解,然后提出模糊统计算法,提取多目标优化问题的最优解。多目标优化方法分两个阶段进行。在第一阶段,通过AMPSO和产品平台常数参数分别对每个产品进行优化,并根据设计变量的变化率获得其值。在第二阶段,基于优化目标,通过改进约束条件下的性能,通过AMPSO求解每个产品的比例变量,并基于模糊统计算法提取最佳折衷方案。

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