A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization

By Xiaodong Li. Annual Conference on Genetic and Evolutionary Computation, 2003

533 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/8acc9cc9ff413351c66f45eeda9556e45a12ab8f

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Citations in the corpus

Evolutionary Multi-Objective Workflow Scheduling in Cloud (2016, unknown): “For NSPSO, the corresponding parameters are w 1⁄4 1:0 ! 0:4, c1 1⁄4 2 and c2 1⁄4 2, as used in [40].”
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems (2008, background): “a well-distributed solution set along the Pareto front), Li [135] proposed sorting the entire population into various nondomination levels such that the individuals from better fronts can be selected.”
Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach (2013, unknown): “Li [48] introduces the idea of NSGAII into PSO to develop a multi-objective PSO algorithm and achieves promising results on benchmark function optimization.”
Handling multiple objectives with particle swarm optimization (2004, methodology): “• Li [23] proposed an approach in which the main mechanisms of the NSGA-II [11] are adopted in a PSO algorithm.”
An effective use of crowding distance in multiobjective particle swarm optimization (2005, methodology): “The second test function was introduced by Kursawe [11] which has three disconnected Pareto curves.”
Differential Evolution With Neighborhood Mutation for Multimodal Optimization (2012, background): “In [63] and [64], crowding concept is used to maintain diversity.”
Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and epsilon-Dominance (2005, methodology): “The task of the subswarms is to cover the gaps between the non-dominated solutions obtained from phase 1. Li [ 12 ]: This approach incorporates the main mechanisms of the NSGA-II [13] into PSO.”
Swarm Intelligence in Optimization (2008, methodology): “With the aim of increasing the efficiency of extracting non-dominated solutions from a swarm, Li proposed NSPSO (Non-dominated Sorting PSO) [100], which follows the principal idea of the well-known NSGA II algorithm [39].”
Differential evolution for filter feature selection based on information theory and feature ranking (2018, methodology): “…based information gain measure), and four multi-objective PSO methods ( NSfsMI and NSfsE based on non-dominated sorting based multi-objective PSO [65] with MIFS and the entropy measures, respectively, and CMDfsMI and CMDfsE based on multi-objective PSO in [66] with MIFS and the entropy measures,…”
On the computation of all global minimizers through particle swarm optimization (2004, background): “Among other applications, it has been applied to tackle multiobjective problems [14]–[19], minimax problems [20], [21], integer programming problems [22], noisy and continuously changing environments [23]–[26], errors-in-variables problems [27], existence of function zeros [28], and numerous enginee”
Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art (2006, unknown): “[5] fully connected densityof solutions; yes no no success Li [37] fully connected nichecount; yes yes yes densityestimator ̈ ©oa «E¬H« a s®”
Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems (2013, background): “Moreover, many MOPSOs adopt the Pareto domination concept when assigning the fitness value of the individuals [21], [22], e.”