Multiobjective optimization using dynamic neighborhood particle swarm optimization

By Xiaohui Hu, R. Eberhart. Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), 2002

691 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/be25e2d4abccd85a31dac993eb4dca4190a51550

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Computational Theory and Mathematics

Citations in the corpus

Neural Network Modeling and Particle Swarm Optimization (PSO) of Process Parameters in Pulsed Laser Micromachining of Hardened AISI H13 Steel (2009, background): “Hu and Eberhart [23] proposed Dynamic neighborhood-particle swarm optimization (DN-PSO) for this purpose.”
Adaptive Particle Swarm Optimization (2008, methodology): “Further, dynamically changing neighborhood structures have been proposed by Suganthan [37], Hu and Eberhart [47], and Liang and”
Dynamic multi-swarm particle swarm optimizer (2005, background): “Kennedy claimed that PSO with large neighborhood would perform better for simple problems and PSO with small neighborhoods might perform better on complex problems”
Discrete Particle Swarm Optimization, illustrated by the Traveling Salesman Problem (2004, background): “enough, we may simultaneously find several solutions (this property is used in multiple objective optimization [18, 19]).”
A Non-dominated Sorting Particle Swarm Optimizer for Multiobjective Optimization (2003, methodology): “PSO has proved to be an efficient optimization method for single objective optimization, and more recently has also shown promising results for solving multiobjective optimization problems [4][5] [6][7].”
Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems (2013, methodology): “MOPSO [18], the neighborhood of one particle is formed by some nearest particles according to the objective space of one objective, while the best particle in the neighborhood is”
Dynamic multi-swarm particle swarm optimizer with local search (2005, background): “Focusing on improving the local version of PSO, different neighborhood structures are proposed and discussed [3][4][5][6][7].”
A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications (2008, methodology): “Hu and Eberhart (2002b) adopted a trial and error approach to CO using the canonical PSO with two modifications: first, particles were only initialised to feasible positions, and, second, only solutions that satisfied the constraints were used for the local and global best positions.”
On the computation of all global minimizers through particle swarm optimization (2004, background): “The resulting equations for the manipulation of the swarm are [45]–[ 47 ]”
Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients (2007, methodology): “Some of these techniques can be found in [5] [6] [14] [15] [18] [22] [24] [36].”
A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts (2005, methodology): “Previous MOPSO implementations [5,6,7,8,9,10] have all used metrics in objective space (either explicitly or implicitly) in the selection of guides – thus making them susceptible to different scalings in objective space.”
Swarm Intelligence in Optimization (2008, background): “3.1 we first present the original PSO developed by Kennedy and Eberhart.”
Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art (2006, methodology): “Lexicographic ordering Hu and Eberhart [24] ring single-objective no yes no rnd(0.”