Enhanced Philoponella Prominens Optimization (EESPPO) Algorithm Integrated with Experience Exchange Strategy for Global Optimization and Engineering Design Problems

To address the challenges of high-dimensional nonlinearity, multimodal landscapes, and stringent constraints prevalent in modern engineering design, traditional meta-heuristic algorithms often suffer from a loss of population diversity and premature convergence. Inspired by the social collaborative predation and collective information interaction behaviors of P. prominens (jumping spiders), this study proposes a novel bio-inspired meta-heuristic optimization algorithm, termed the Experience Exchange Strategy-Enhanced Philoponella Prominens Optimization (EESPPO). The proposed EESPPO integrates an Experience Exchange Strategy framework to reshape the search dynamics of the population through three progressive evolutionary stages: (1) In the Experience Scarcity (ESC) stage, the algorithm focuses on the construction and dynamic maintenance of an experience library to ensure the effective preservation of high-quality historical information; (2) In the Experience Crossover (ECR) stage, a random guidance vector generation mechanism is introduced to significantly enhance population behavioral diversity and the capability to escape local optima; (3) In the Experience Sharing (ESH) stage, an adaptive fusion update strategy is employed to achieve efficient information interaction and co-evolution among individuals. These three stages operate synergistically within the optimization cycle to establish a dynamic balance between global exploration and local exploitation, effectively overcoming the inherent defects of premature convergence in traditional meta-heuristics. Extensive empirical analysis based on the CEC2017 benchmark functions confirms that EESPPO comprehensively outperforms 12 existing advanced algorithms (including PPO, HSO, SGA, PSO, FLO, DE, HO, WOA, KEO, GWO, FDB-AGSK, and IVYPSO) in terms of convergence accuracy and robustness. Furthermore, the application of EESPPO to four challenging engineering design problems confirms its superiority. The experimental results validate the high precision and feasibility of EESPPO in solving complex constrained engineering problems.