Solving Constrained Nonlinear Optimization Problems with Particle Swarm Optimization

By Xiaohui Hu, R. Eberhart. 2002

490 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/18a228e29a7e8ee08c1cf50a7f27c96271bea708

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Citations in the corpus

Well placement optimization subject to realistic field development constraints (2016, methodology): “Superiority of feasible points In this method, the feasibility of a solution is preserved by removing the infeasible solutions [30, 31].”
A derivative-free methodology with local and global search for the constrained joint optimization of well locations and controls (2014, background): “For treating general (nonlinear) constraints, global stochastic search methods typically employ techniques that either discard infeasible solutions (and thus only consider feasible solutions), as in [26], or they apply penalty function approaches, as in [14] and [37].”
A ranking selection-based particle swarm optimizer for engineering design optimization problems (2008, methodology): “Methods based on preserving feasibility of solutions were also employed by Hu and Eberhart (2002) , He et al. (2004), in which each particle keeps tracking only feasible solutions.”
An improved particle swarm optimizer for mechanical design optimization problems (2004, methodology): “The best solution reported by Hu and Eberhart [21] was −30665.”
A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications (2008, methodology): “At a similar time, Hu and Eberhart (2002a) developed a Dynamic Neighbourhood PSO (DNPSO) in which each particle calculates its fitness and uses the proximity of neighbouring particles against each objective function to deduce its own local best solution.”
Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective (2017, methodology): “The basic PSO algorithm can be adapted to accept problems with constraints, as detailed in [74].”
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review (2017, background): “The theoretical analysis in that paper proved that the convergence boundaries found by Jiang et al. (2007b) and Poli (2009) are valid under this assumption for the global best particle. Also, Liu (2014) proved that the found regions serve as a necessary and sufficient condition for second-order stab”