Journal
INTERNATIONAL JOURNAL OF APPLIED MECHANICS
Volume 10, Issue 1, Pages -Publisher
WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S1758825118500096
Keywords
Truss structure; heuristic particle swarm; multi-objective optimization; constraint improvement; robustness
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Funding
- National Natural Science Foundation of China [11472084]
- Science and Technology Planning Project of Guangzhou City [201704030057]
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In this study, a novel multi-objective hybrid algorithm (MHGH, multi-objective HPSO-GA hybrid algorithm) is developed by crossing the heuristic particle swarm optimization (HPSO) algorithm with a genetic algorithm (GA) based on the concept of Pareto optimality. To demonstrate the effectiveness of the MHGH, the optimizations of four unconstrained mathematical functions and four constrained truss structural problems are tested and compared to the results using several other classic algorithms. The results show that the MHGH improves the convergence rate and precision of the particle swarm optimization (PSO) and increases its robustness.
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