Optimizing a Simply Supported I-Section Beam with Genetic Algorithm
The journey to weight optimization for a simply supported I-section beam, using the Genetic Algorithm, is a step-by-step process that mirrors the principles of natural evolution. Here’s a brief summary of the steps involved:
Initialization: We begin by generating a diverse population of I-section beam designs, each with different flange and web dimensions.
Fitness Evaluation: Each design is assessed for weight and structural performance (stress and deflection limits). The lighter and more structurally sound designs receive higher fitness scores.
Selection: The best designs are selected to form a mating pool, prioritizing lightweight solutions that meet all constraints.
Crossover: Parent beam designs are combined, exchanging dimensions to produce new offspring with potentially better performance.
Mutation: Small random changes are introduced to the beam designs, ensuring diversity and preventing premature convergence on suboptimal solutions.
Survivor Selection: The fittest designs, from both the parent and offspring generations, are chosen to continue evolving.
Iteration: This process is repeated over many generations, with each iteration refining the designs and bringing the algorithm closer to the optimal solution.
Convergence: Finally, the algorithm reaches convergence, where further iterations no longer yield significant improvements, producing the lightest, structurally sound I-section beam.
