Achieving sustainable design requires balancing environmental performance, resource efficiency, functional feasibility, and aesthetic acceptance throughout the product life cycle. However, traditional design approaches often struggle to quantitatively integrate subjective aesthetic evaluation with objective sustainability indicators such as energy consumption, carbon emissions, and material recyclability. To address this challenge, this study proposes a semantic-guided multi-objective optimization framework for sustainable design that integrates cross-modal aesthetic evaluation with life cycle environmental performance assessment. The proposed framework employs a Contrastive Language–Image Pre-training (CLIP)-based semantic evaluation mechanism to translate abstract sustainability and aesthetic concepts into quantifiable design features, enabling consistent assessment across diverse design solutions. These semantic features are further optimized using a multi-objective evolutionary optimization strategy to simultaneously minimize energy consumption and carbon emissions while maximizing material recovery and design quality. Life cycle environmental indicators derived from OpenLCA datasets are incorporated into the optimization process to ensure practical sustainability relevance. The experimental results demonstrate that the proposed framework achieves a superior performance compared with benchmark optimization methods. Specifically, carbon emission equivalents are reduced to as low as 12.3 kg CO₂e, material recovery rates exceed 92%, and total computational energy consumption is reduced by more than 40% relative to comparative models. In addition, the framework shows strong stability and convergence efficiency while maintaining a high aesthetic evaluation accuracy in high-quality design ranges. The findings indicate that the proposed approach provides an effective pathway for integrating aesthetic value with environmental responsibility in sustainable design practice. This framework supports low-carbon and resource-efficient product development and offers practical insights for sustainable manufacturing, circular design, and environmentally conscious innovation.

This study proposes a health and performance optimization framework based on Particle Swarm Optimization (PSO) to improve the structural performance and energy utilization efficiency of coastal wind–wave energy harvesting systems. A semi-submersible floating wind turbine–wave energy integrated system is selected as the case study. The control variable method and numerical simulations are employed to determine the optimal structural parameters. Furthermore, a multi-scenario coordinated optimization model for wind, wave, energy storage, and load is established using the Velocity Pause Particle Swarm Optimization (VPPSO) algorithm. The optimal structural parameters are identified as an outer diameter of 16 m, an inner diameter of 8 m, a height of 8 m, and a draft of 3.5 m. The results show that VPPSO achieves faster convergence and better optimization performance compared to conventional algorithms. In the optimal scenario with wind–wave curtailment and energy storage participation, the minimum economic cost of 1780 CNY is achieved after 200 iterations. The proposed method provides theoretical guidance for the optimal design and efficient operation of coastal wind–wave energy harvesting systems.