A dynamic adaptive particle swarm optimization and genetic algorithm for different constrained engineering design optimization problems
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2018-12-12
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Hao Zhu , Yumei Hu and Weidong Zhu, A dynamic adaptive particle swarm optimization and genetic algorithm for different constrained engineering design optimization problems, Advances in Mechanical Engineering 2019, Vol. 11(3), 1–27, 2019, DOI: 10.1177/1687814018824930
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Attribution 4.0 International (CC BY 4.0)
Attribution 4.0 International (CC BY 4.0)
Abstract
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.