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Title
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A SELF-ADAPTIVE ENHANCED VIBRATING PARTICLE SYSTEM ALGORITHM FOR STRUCTURAL OPTIMIZATION: APPLICATION TO ISCSO BENCHMARK PROBLEMS
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Type
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JournalPaper
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Keywords
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Structural optimization; Metaheuristic algorithms; Self-adaptive parameters; Vibrating particle system; Truss structures; ISCSO benchmarks; Size optimization; Parameter tuning.
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Abstract
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Structural optimization plays a crucial role in engineering design, aiming to minimize weight and cost while satisfying performance constraints. This research presents a novel SelfAdaptive 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 314-member truss structure (ISCSO_2018) and the 345-member truss structure (ISCSO_2021). Results demonstrate that SA-EVPS achieves significantly better solutions compared to previous studies using the Exponential Big BangBig Crunch (EBB-BC) algorithm. For ISCSO_2018, SA-EVPS achieved a minimum weight of 16543.57 kg compared to 17934.3 kg for the best EBB-BC variant—a 7.75% improvement. Similarly, for ISCSO_2021, SA-EVPS achieved 4292.71 kg versus 4399.0 kg for the best EBB-BC variant—a 2.42% improvement. The proposed algorithm also demonstrates superior convergence behavior and solution consistency, with coefficients of variation of 3.13% and 1.21% for the two benchmark problems, compared to 12.5% and 2.4% 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.
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Researchers
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Seyed Jamalaldin Seyed Hakim (Fourth Researcher), Ali Kaveh (Third Researcher), Pedram Hosseini (Second Researcher), Masoud Paknahad (First Researcher)
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