An improved PSO algorithm for solving non-convex NLP/MINLP problems with equality constraints

Application of a multi objective multi-leader particle swarm optimization algorithm on NLP and MINLP problems

“…As demonstrated by several researchers such as Elbeltagi, Hegazy, and Grierson (2005) and Yiqing et al (2007), PSO is more time efficient than other stochastic optimization methods and is also cheaper to implement. In the past decade PSO has been applied for a variety of applications with single and/or multiple objectives and it has been proven to be a powerful optimization algorithm suitable for both linear and nonlinear search spaces (Elhossini, Areibi, & Dony, 2010;Ho, Yang, Ni, Lo, & Wong, 2005;Mostaghim & Schmeck, 2008;Wang & Yang, 2009;Xu & Liu, 2009).…”

“…However, there are two major concerns when applying stochastic strategies. First, handling constraints particularly those which are active at the optimum to obtain feasible solutions and second the efficiency in terms of computational burden and time (Yiqing, Xigang, & Yongjian, 2007). Among stochastic algorithms, particle swarm optimization (PSO) has been the focus of researchers due to its high efficiency compared to the other methods in the same category.…”

Performance Evaluation of Nature-Inspired Algorithms in constrained Optimization

“…Here, we note that the number of problems possibly put in this set is more than we included in this work (Yiqing, Xigang and Yongjian, 2007;Lin, Hwang and Wang, 2004;Costa and Oliveira, 2001). However, we have selected the most common problems.…”

MIDACO Parallelization Scalability on 200 MINLP Benchmarks

“…Babu and Angira [1], Cardoso [2], Costa and Oliveira [3], Deep et al [4], Glover [6], Liang et al [11], Mohammed [12], Munawar [13], Wasanapradit et al [28], Young et al [30], Yiqing et al [29] or Yue et al [32]. One advantage of evolutionary algorithms is their robustness towards the analytical properties of the objective and constraint functions.…”