Genetic Algorithms in Engineering: Optimizing a Truss with Conflicting Objectives

🧬 How can Genetic Algorithms help solve structural optimization problems involving conflicting objectives?
In this educational video, we demonstrate the implementation of a Genetic Algorithm (GA) to solve a bi-objective nonlinear integer optimization problem in structural engineering.
The case involves a Howe truss, where the design variables include:
🔹 The lengths of vertical bars (discrete values)
🔹 The cross-sectional areas of all bars (selected from fixed discrete options)
🎯 Optimization goals:
1. Minimize the vertical displacement at the bottom central node
2. Minimize the total mass of the steel truss
📐 The vertical displacement was computed using the Virtual Work Method (Virtual Load Method) — a classical and effective approach for analyzing displacements in elastic structures.
💡 The result illustrates the trade-off between objectives and highlights the Pareto front of non-dominated solutions — where no single solution is best in all aspects, but each represents an optimal compromise.
📊 This case study is a great introduction to evolutionary computation, multi-objective optimization, and the practical use of Genetic Algorithms in structural engineering education and design.
👨‍💻 Author: Prof. Rubelmar Neto
🔗 LinkedIn: www.linkedin.com/in/rubelmar-maia-de-azevedo-c-neto-44720578
📺 Channel: @GifData
📌 Developed in Wolfram Mathematica

#geneticalgorithm #structuraloptimization #multiobjective #trussdesign #engineeringdesign #gifdata #civilengineering #mechanicalengineering #optimization

Видео Genetic Algorithms in Engineering: Optimizing a Truss with Conflicting Objectives канала GifData