Multi-Objective Teaching-Learning-Based Optimization for Structure Optimization

Teaching–learning-based optimization is a specific parameter-free and powerful algorithm. However, in large and diverse spaces it often gets trapped in local optima and faces criticism of premature convergence particularly while solving multi-objective problems. The present work proposed a novel multi-objective teaching–learning-based optimization (MOTLBO) based on the framework of non-dominated sorting and solution storage in an external archive. These techniques improve the algorithm’s speed of search and convergence rate. Moreover, this mechanism also assists in obtaining a Pareto optimal set near to the true Pareto solutions while simultaneously maintaining the diversity among non-dominated solutions within one run. The present work proposed a novel MOTLBO. To determine feasibility for practical applications, perceived structure design problems are exposed to multiple and diverse weight minimization and maximization of nodal deformation objectives. The suggested algorithm is employed to five challenging optimization issues of the structure having discrete design variables and subject to multiple constraints. For a performance check, the suggested algorithm is contrasted with two prominent multi-objective algorithms. The performance gauge for all considered test examples is the Pareto front hypervolume and front spacing-to-extent test. MOTLBO shows its promise with coherence and diversification of solutions for producing the desired Pareto fronts.