Solving Engineering Optimization Problems with the Simple Constrained Particle Swarm Optimizer
By Leticia Cecilia Cagnina, Susana Cecilia Esquivel, Carlos Artemio Coello Coello. Informatica, 2008
324 citations recorded by the source corpus.
View Paper: https://www.semanticscholar.org/paper/37619038a6f2d0971927149dc39a5e8a9d9442eb
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Xin-She Yang
- Xingshi He
- Suash Deb
- Mehmet Karamanoglu
- Arpan Kumar Kar
- Iztok Fister
- Tansel Dökeroğlu
- Seyed Mohammad Mirjalili
Citations in the corpus
- Multi-Objective Flower Algorithm for Optimization (2014, unknown): “Then, decision-makers can rank different options, depending on their preferences or their utilities [1, 2, 3, 5, 6, 7, 23].”
- Multiobjective firefly algorithm for continuous optimization (2012, background): “Different objectives often conflict each other, and sometimes, truly optimal solutions do not exist, and some compromises and approximations are often needed [1, 2, 3].”
- Firefly algorithm, stochastic test functions and design optimisation (2010, background): “714 obtained by Cagnina et al (2008).”
- Flower Pollination Algorithm for Global Optimization (2012, methodology): “Typically, the design variables are the thickness d1 of the head, the thickness d2 of the body, the inner radius r, and the length L of the cylindrical section [2].”
- Stochastic Fractal Search: A powerful metaheuristic algorithm (2015, unknown): “12) design optimization [46,47].”
- Engineering optimisation by cuckoo search (2010, methodology): “Table 1: Comparison of CS with genetic algorithms and particle swarm optimisation Functions GA PSO CS (1) 89325 ± 7914(95%) 6922 ± 537(98%) 3221 ± 519(100%) (2) 55723 ± 8901(90%) 32756 ± 5325(98%) 5923 ± 1937(100%) (3) 15232 ± 1270(100%) 10079 ± 970(100%) 3015 ± 540(100%) (4) 23790 ± 6523(95%) 92411”
- Multiobjective cuckoo search for design optimization (2013, background): “In reality, different objectives often conflict each other, and sometimes, truly optimal solutions may not exist at all, and some compromise and approximations are often needed [8,11,23].”
- Flower pollination algorithm: A novel approach for multiobjective optimization (2014, background): “Common approaches are to provide good approximations to the true Pareto fronts of the problem of interest so that decision-makers can rank different options, depending on their preferences or their utilities (Abbass and Sarker 2002; Babu and Gujarathi 2007; Cagnina et al. 2008; Deb, 1999, 2000, 2001”