Multi-objective chaos game optimization for constrained structural design using chaos-inspired search dynamics

Abstract This study introduces Multi-Objective Chaos Game Optimization (MOCGO), a metaheuristic algorithm developed for the purpose of handling multi-objective structural optimization problems with constraints. In doing so, MOCGO utilizes a single-objective algorithm known as Chaos Game Optimization (CGO) in conjunction with chaos exploration dynamics, a diversity-preserving archive, and a grid-selection process to create an effective set of Pareto optimal solutions. This algorithm has been tested on six different trusses (10-bar to 120-bar) based on the problem of minimizing the weight and compliance of a structure while satisfying the stress constraint criteria. As measures of fitness, MOCGO performs significantly better than other algorithms, including NSGA-II, MOGOA, MOALO, and MOAVOA. These results demonstrate MOCGO as an efficient and robust approach for constrained multi-objective structural design.