基于进化算法的产品计算设计关键技术研究
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摘要
在全球化经济背景下,具有自主知识产权的产品创新设计能力是国家核心竞争力的一个重要组成部分。本课题通过构造不依赖于问题领域的自动智能化创新设计方法与体系,为产品创新设计提供新的思路,从而缓解依赖于专家设计经验、逻辑推理和少数天才发明家的顿悟而实现产品创新设计的局面,减少产品创新设计对人的依赖性,提高企业的创新设计能力。
本文在研究并实现基于相似性排挤的小生境技术(NTSC)、快速适应值分层算法(FHFS)的基础上,提出了基于相似性排挤与适应值分层计算的可持续Pareto遗传算法(SPGA)。SPGA采用了进化操作种群与外部种群两个种群。外部种群用于存储当前最优解集,利用基于模糊推理机制提出的NTSC维持种群多样度,使外部种群中存储的Pareto非劣解集均匀地逼近问题的理论最优面;采用将个体按其所处层次来精确标识个体适应能力的FHFS辨识个体适应值,避免适应值特别高的个体抑制适应值比它低的个体。仿真优化结果表明,SPGA能够以较少的计算成本搜索到高精度的、分布均匀的Pareto非劣解集。将基因编码的内容分为遗传信息与自适应学习两部分,构造了潜在维持并提高种群多样度的自适应学习算法;提出了为具有潜力的个体模式提供生存空间的相对适应策略,设计了能自然地提高子代质量、加快收敛速度的冗余繁殖算子,形成了基于相对适应策略与冗余繁殖算子的可持续遗传算法。
扼要综述了动态系统自动设计方法与研究进展,研究了基于功率键合图与遗传编程的动态系统自动设计中更加有效的适应值定义方法——基于匈牙利算法的适应值定义方法,从而形成并实现了基于匈牙利算法和遗传编程的动态系统设计框架(HAGP)。多组动态系统中的特征值设置问题的统计结果表明,HAGP能够实现自动设计并获得高质量的设计方案,它的收敛速度与搜索效率均优于代表性的方法。最后介绍了基于遗传编程与键合图的开放式合成机械减震器的自动设计实例。
In the context of a globalized economy, with independent intellectual property rights of the design capabilities in product innovation is the core competitiveness of the country. This research tries to construct an automated intelligent innovative design methods and system for product innovation to provide new ways of thinking, thus easing the design relies on the expert experience, logical reasoning and a few talented inventor of insight to achieve product innovation to reduce dependence on human, enhance the capacity of enterprises.
This research proposes a sustainable Pareto genetic algorithm (SPGA) for multi-objective optimization based on two techniques including the niche technique of similarity based on crowding (NTSC) and the fast hierarchical fitness stratification (FHFS). NTSC with computational complexity of only O(N log N') is used for maintaining the population diversity and uniform distribution of Pareto solutions while FHFS for avoiding or reducing suppression of low-fitness individuals by high-fitness individuals, genetic drift and premature convergence. Two populations are employed in the SPGA algorithm. The main population is used for genetic operation and an external population for storing current optimal solutions. Testing on benchmark multi-objective 0/1 knapsack problems demonstrates that SPGA can obtain high-quality and evenly distributed non-dominated Pareto solutions with fewer computational efforts compared to other representative algorithms.This paper divides the genotype into two parts: genetic information and culture information. The self-adaptive learning algorithm to sustain and enhance the diversity of population is put forward. This paper also proposes a relative adaptation strategy which provides opportunity for individual schemata which is potential and a redundant reproduction operator that improves the quality of descendant and increases the convergence rate, then a kind of sustainable genetic algorithms based on comparative adaptation strategy and self-adaptive learning operator is formed (SGA). Testing on 11 problems with different parameters demonstrates that SGA is superior to standard genetic algorithms on the convergence rate as well as the capable of maintaining the diversity.
Genetic programming is an effective way to generate design candidates in an open-ended, but statistically structured, manner. A critical aspect of the procedure is fitness measure, which guides the evolution of candidate designs toward a suitable result in a reasonable time. This paper briefly summarizes the present research status of automated design method for dynamic systems, investigates the more efficient method of fitness definition for automated design of dynamic systems based on bond graphs and genetic programming. The automated design method based on Hungarian algorithm and genetic programming (HAGP) is proposed, and the statistic results of domain independent - an eigenvalues -placement design problem, which is tested for some sample target sets of eigenvalues, strongly shows the search capability of HAGP is good enough to make feasible automated design for multi-domain systems and obtain high-quality, well evolutionary solutions with less computational efforts, rapid speed in convergence compared to other state-of art algorithms.Finally, an instance of evolving vibration absorbers based on genetic programming and bond graphs is introduced.
本文在研究并实现基于相似性排挤的小生境技术(NTSC)、快速适应值分层算法(FHFS)的基础上,提出了基于相似性排挤与适应值分层计算的可持续Pareto遗传算法(SPGA)。SPGA采用了进化操作种群与外部种群两个种群。外部种群用于存储当前最优解集,利用基于模糊推理机制提出的NTSC维持种群多样度,使外部种群中存储的Pareto非劣解集均匀地逼近问题的理论最优面;采用将个体按其所处层次来精确标识个体适应能力的FHFS辨识个体适应值,避免适应值特别高的个体抑制适应值比它低的个体。仿真优化结果表明,SPGA能够以较少的计算成本搜索到高精度的、分布均匀的Pareto非劣解集。将基因编码的内容分为遗传信息与自适应学习两部分,构造了潜在维持并提高种群多样度的自适应学习算法;提出了为具有潜力的个体模式提供生存空间的相对适应策略,设计了能自然地提高子代质量、加快收敛速度的冗余繁殖算子,形成了基于相对适应策略与冗余繁殖算子的可持续遗传算法。
扼要综述了动态系统自动设计方法与研究进展,研究了基于功率键合图与遗传编程的动态系统自动设计中更加有效的适应值定义方法——基于匈牙利算法的适应值定义方法,从而形成并实现了基于匈牙利算法和遗传编程的动态系统设计框架(HAGP)。多组动态系统中的特征值设置问题的统计结果表明,HAGP能够实现自动设计并获得高质量的设计方案,它的收敛速度与搜索效率均优于代表性的方法。最后介绍了基于遗传编程与键合图的开放式合成机械减震器的自动设计实例。
In the context of a globalized economy, with independent intellectual property rights of the design capabilities in product innovation is the core competitiveness of the country. This research tries to construct an automated intelligent innovative design methods and system for product innovation to provide new ways of thinking, thus easing the design relies on the expert experience, logical reasoning and a few talented inventor of insight to achieve product innovation to reduce dependence on human, enhance the capacity of enterprises.
This research proposes a sustainable Pareto genetic algorithm (SPGA) for multi-objective optimization based on two techniques including the niche technique of similarity based on crowding (NTSC) and the fast hierarchical fitness stratification (FHFS). NTSC with computational complexity of only O(N log N') is used for maintaining the population diversity and uniform distribution of Pareto solutions while FHFS for avoiding or reducing suppression of low-fitness individuals by high-fitness individuals, genetic drift and premature convergence. Two populations are employed in the SPGA algorithm. The main population is used for genetic operation and an external population for storing current optimal solutions. Testing on benchmark multi-objective 0/1 knapsack problems demonstrates that SPGA can obtain high-quality and evenly distributed non-dominated Pareto solutions with fewer computational efforts compared to other representative algorithms.This paper divides the genotype into two parts: genetic information and culture information. The self-adaptive learning algorithm to sustain and enhance the diversity of population is put forward. This paper also proposes a relative adaptation strategy which provides opportunity for individual schemata which is potential and a redundant reproduction operator that improves the quality of descendant and increases the convergence rate, then a kind of sustainable genetic algorithms based on comparative adaptation strategy and self-adaptive learning operator is formed (SGA). Testing on 11 problems with different parameters demonstrates that SGA is superior to standard genetic algorithms on the convergence rate as well as the capable of maintaining the diversity.
Genetic programming is an effective way to generate design candidates in an open-ended, but statistically structured, manner. A critical aspect of the procedure is fitness measure, which guides the evolution of candidate designs toward a suitable result in a reasonable time. This paper briefly summarizes the present research status of automated design method for dynamic systems, investigates the more efficient method of fitness definition for automated design of dynamic systems based on bond graphs and genetic programming. The automated design method based on Hungarian algorithm and genetic programming (HAGP) is proposed, and the statistic results of domain independent - an eigenvalues -placement design problem, which is tested for some sample target sets of eigenvalues, strongly shows the search capability of HAGP is good enough to make feasible automated design for multi-domain systems and obtain high-quality, well evolutionary solutions with less computational efforts, rapid speed in convergence compared to other state-of art algorithms.Finally, an instance of evolving vibration absorbers based on genetic programming and bond graphs is introduced.
引文
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