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Abstract It is difficult to find optimal solutions to multi-objective optimization problems, because they involve balancing conflicting objectives under complicated constraints. Metaheuristics are widely applied for solving problems, since they are easy to apply and may produce promising near-optimum solutions. However, achieving an optimal balance between exploration and exploitation still remains a key challenge, especially in high-dimensional and constrained design spaces. This research work presents MOGWO2Arc, a new multi-objective optimizer utilizing a dual-archive strategy to promote solution diversity and convergence. Eight benchmark truss structure optimization problems were used to test the suggested algorithm with the objectives of minimizing weight and compliance subject to safe stress levels. Other constraints, such as dimensional constraints, standard safety codes, and unique cross-sectional domains, were also considered. The hypervolume, generational distance, inverted generational distance, and spacing were used as the key parameters to compare performance with eight state-of-the-art optimization algorithms. Based on the outcome of the Friedman rank test, MOGWO2Arc outperformed the competing approaches, especially when utilized to solve large-scale structural optimization problems, producing higher-quality solutions at significantly lower computational costs. MOGWO2Arc also provides a powerful and efficient way to solve complex multi-objective structure optimization problems by promoting exploration and diversifying Pareto-optimal solutions across decision and objective spaces.
Tejani et al. (Mon,) studied this question.