Journal
ADVANCES IN MECHANICAL ENGINEERING
Volume 11, Issue 3, Pages -Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/1687814018824930
Keywords
Constrained engineering design optimization problems; continuous and discrete design variables; meta-heuristic; dynamic adaptive; particle swarm optimization; genetic algorithm
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Funding
- National Natural Science Foundation of China [51805339]
- Fundamental Research Funds for Central Universities
- State Key Development Program for Basic Research of China [2014CB049401]
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A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm-related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.
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