An improved particle swarm optimizer for mechanical design optimization problems

By Shang He, Emmanuel Prempain, Q. Henry Wu. 2004

417 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/908bc29d1a5c9718dd29edef2ca84d6c93699a90

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In Artificial Intelligence

In Computational Theory and Mathematics

Citations in the corpus

Mathematical and Metaheuristic Applications in Design Optimization of Steel Frame Structures: An Extensive Review (2013, methodology): “In [180] particle swarm optimizer is improved by introducing fly-back mechanism in order to maintain a feasible population.”
Flower pollination algorithm: A novel approach for multiobjective optimization (2014, methodology): “Other algorithms such as shuﬄed frog-leaping algorithm and particle swarm optimizers have been applied to various optimization problems (Eusuﬀ et al. 2006; He et al. 2004; Huang 1996).”
Differential evolution with dynamic stochastic selection for constrained optimization (2008, background): “The details of best solutions by different approaches are presented in Table 9. From the comparisons, it is clear that the results obtained by our approach are better than those by FSA [25], Ray and Liew [22], Deb [5,27], and are competitive with that by He et al. [ 26 ].”
Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems (2011, neutral): “The problem of the hydrostatic bearing was attempted by He et al. [29] using improved PSO, by Coello [26] using new constraint handling techniques, and by Deb and Goyal [30] using GeneAS.”
Solving Engineering Optimization Problems with the Simple Constrained Particle Swarm Optimizer (2008, methodology): “The other PSObased approach compared [14] was tested with the same set of problems and the best known values were reached for E02 and E04 after 30,000 objective function evaluations.”
Bat algorithm: a novel approach for global engineering optimization (2012, methodology): “Moreover, PSO and harmony search are the special cases of BA under appropriate simplifications.”
Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems (2011, result): “123 [ 16 ], Shi and Eberhart [17]). Table 2 illustrates the results obtained by CS, as well as those published in the literature.”
Multiple trial vectors in differential evolution for engineering design (2007, methodology): “He et al. (2004) proposed an approach based on particle swarm optimization (PSO) to solve engineering design problems.”
Artificial bee colony algorithm for large-scale problems and engineering design optimization (2010, methodology): “In terms of the complexity of the algorithms, while UPSO, ABC and ( μ + λ )-ES have similar complexities, PSO of He et al. (2004) has more complexity since it requires a feasible initial population and SCA adds extra computational cost derived of clustering routines (Mezura-Montes and Coello Coello…”