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The book provides suggestions on how to start using bionic optimization methods, including pseudo-code examples of each of the important approaches and outlines of how to improve them. The most efficient methods for accelerating the studies are discussed. These include the selection of size and generations of a study’s parameters, modification of these driving parameters, switching to gradient methods when approaching local maxima, and the use of parallel working hardware.
Bionic Optimization means finding the best solution to a problem using methods found in nature. As Evolutionary Strategies and Particle Swarm Optimization seem to be the most important methods for structural optimization, we primarily focus on them. Other methods such as neural nets or ant colonies are more suited to control or process studies, so their basic ideas are outlined in order to motivate readers to start using them.
A set of sample applications shows how Bionic Optimization works in practice. From academic studies on simple frames made of rods to earthquake-resistant buildings, readers follow the lessons learned, difficulties encountered and effective strategies for overcoming them. For the problem of tuned mass dampers, which play an important role in dynamic control, changing the goal and restrictions paves the way for Multi-Objective-Optimization. As most structural designers today use commercial software such as FE-Codes or CAE systems with integrated simulation modules, ways of integrating Bionic Optimization into these software packages are outlined and examples of typical systems and typical optimization approaches are presented.
The closing section focuses on an overview and outlook on reliable and robust as well as on Multi-Objective-Optimization, including
discussions of current and upcoming research topics in the field concerning a unified theory for handling stochastic design processes.商品描述(中文翻譯)
本書提供了如何開始使用仿生優化方法的建議，包括每種重要方法的偽代碼範例以及如何改進它們的概述。討論了加速研究的最有效方法，包括選擇研究參數的大小和世代、修改這些驅動參數、在接近局部極大值時切換到梯度方法，以及使用並行工作的硬體。
仿生優化是指利用自然界中發現的方法來尋找問題的最佳解決方案。由於進化策略（Evolutionary Strategies）和粒子群優化（Particle Swarm Optimization）似乎是結構優化中最重要的方法，因此我們主要集中於這些方法。其他方法如神經網絡或蟻群算法更適合控制或處理研究，因此簡要概述了它們的基本思想，以激勵讀者開始使用這些方法。
一組範例應用展示了仿生優化在實踐中的運作。從對簡單桿件框架的學術研究到抗震建築，讀者可以跟隨所學的教訓、遇到的困難以及克服這些困難的有效策略。對於調諧質量阻尼器的問題，這在動態控制中扮演著重要角色，改變目標和限制為多目標優化（Multi-Objective-Optimization）鋪平了道路。由於當今大多數結構設計師使用商業軟體，如 FE-Codes 或具有集成模擬模組的 CAE 系統，因此概述了如何將仿生優化整合到這些軟體包中的方法，並展示了典型系統和典型優化方法的範例。
最後一部分集中於對可靠性和穩健性以及多目標優化的概述和展望，包括對於處理隨機設計過程的統一理論的當前和即將到來的研究主題的討論。
