A SELF-ADAPTIVE ENHANCED VIBRATING PARTICLE SYSTEM ALGORITHM FOR STRUCTURAL OPTIMIZATION: APPLICATION TO ISCSO BENCHMARK PROBLEMS

Structural optimization plays a crucial role in engineering design, aiming to minimize weight and cost while satisfying performance constraints. This research presents a novel Self-Adaptive Enhanced Vibrating Particle System (SA-EVPS) algorithm that automatically adjusts algorithm parameters to improve optimization performance. The algorithm is applied to two challenging examples from the International Student Competition in Structural Optimization (ISCSO) benchmark suite: the ۳۱۴-member truss structure (ISCSO_۲۰۱۸) and the ۳۴۵-member truss structure (ISCSO_۲۰۲۱). Results demonstrate that SA-EVPS achieves significantly better solutions compared to previous studies using the Exponential Big Bang-Big Crunch (EBB-BC) algorithm. For ISCSO_۲۰۱۸, SA-EVPS achieved a minimum weight of ۱۶۵۴۳.۵۷ kg compared to ۱۷۹۳۴.۳ kg for the best EBB-BC variant—a ۷.۷۵% improvement. Similarly, for ISCSO_۲۰۲۱, SA-EVPS achieved ۴۲۹۲.۷۱ kg versus ۴۳۹۹.۰ kg for the best EBB-BC variant—a ۲.۴۲% improvement. The proposed algorithm also demonstrates superior convergence behavior and solution consistency, with coefficients of variation of ۳.۱۳% and ۱.۲۱% for the two benchmark problems, compared to ۱۲.۵% and ۲.۴% for the best EBB-BC variant. These results highlight the effectiveness of the SA-EVPS algorithm for solving complex structural optimization problems and demonstrate its potential for engineering applications.

کلیدواژه ها:

Structural optimization ، Metaheuristic algorithms ، Self-adaptive parameters ، Vibrating particle system ، Truss structures ، ISCSO benchmarks ، Size optimization ، Parameter tuning