Enhancing Robustness in Precast Modular Frame Optimization: Integrating NSGA-II, NSGA-III, and RVEA for Sustainable Infrastructure

Abstract

The advancement toward sustainable infrastructure presents complex multi-objective optimization (MOO) challenges. This paper expands the current understanding of design frameworks that balance cost, environmental impacts, social factors, and structural integrity. Integrating MOO with multi-criteria decision-making (MCDM), the study targets enhancements in life cycle sustainability for complex engineering projects using precast modular road frames. Three advanced evolutionary algorithms—NSGA-II, NSGA-III, and RVEA—are optimized and deployed to address sustainability objectives under performance constraints. The efficacy of these algorithms is gauged through a comparative analysis, and a robust MCDM approach is applied to nine non-dominated solutions, employing SAW, FUCA, TOPSIS, PROMETHEE, and VIKOR decision-making techniques. An entropy theory-based method ensures systematic, unbiased criteria weighting, augmenting the framework’s capacity to pinpoint designs balancing life cycle sustainability. The results reveal that NSGA-III is the algorithm converging towards the most cost-effective solutions, surpassing NSGA-II and RVEA by 21.11% and 10.07%, respectively, while maintaining balanced environmental and social impacts. The RVEA achieves up to 15.94% greater environmental efficiency than its counterparts. The analysis of non-dominated solutions identifies the A4 design, utilizing 35 MPa concrete and B500S steel, as the most sustainable alternative across 80% of decision-making algorithms. The ranking correlation coefficients above 0.94 demonstrate consistency among decision-making techniques, underscoring the robustness of the integrated MOO and MCDM framework. The results in this paper expand the understanding of the applicability of novel techniques for enhancing engineering practices and advocate for a comprehensive strategy that employs advanced MOO algorithms and MCDM to enhance sustainable infrastructure development.

Cloud Manufacturing Service Composition Optimization Based on Improved Chaos Sparrow Search Algorithm with Time-Varying Reliability and Credibility Evaluation

“…The computer configuration for the experiment is Intel Core i3-3110M CPU, 2.4 GHz main frequency, and 4G memory; the BSSA [53], PSO [3], SGA [46] and NSGA-III [54] are used as comparative algorithms. BSSA is the basic sparrow search algorithm; PSO is the particle swarm optimization algorithm; SGA is the standard genetic algorithm; and NSGA-III is the third-generation non-dominated sorting genetic algorithm.…”

“…It can help enterprises build a better and more reliable manufacturing industry chain in today's complex and volatile international environment. In the case of the same maximum iteration number and total number of sparrows, five algorithms such as the ICSSA, BSSA [53], PSO [3], SGA [46] and NSGA-III [54] are employed to address the identical SvcComp optimization problem. According to Figure 8, the ICSSA attains the optimal solution in the 87th iteration, BSSA in the 92nd, PSO in the 95th, SGA in the 126th, and NSGA-III in the 97th iteration.…”