A TSA–HS–GWO Triple Co-Evolutionary Strategy for Morphology–Control Integration in Soft Climbing Robots

Soft wall-climbing robots operating on complex vertical surfaces often suffer from limited locomotion efficiency and poor environmental adaptability due to the decoupled optimization of morphology and control. To address this issue, this paper proposes an integrated morphology–control design framework based on the cooperative evolution of TSA-HS-GWO, which combines the Tree Seed Algorithm, Harmony Search, and Grey Wolf Optimizer. Structural parameters and control variables, such as actuation timing, are jointly encoded into a hybrid real-valued vector, and a multi-objective fitness function is formulated to account for energy consumption per unit displacement, climbing stability, and adhesion success rate.