MOPSO: a proposal for multiple objective particle swarm optimization

By Carlos Artemio Coello Coello, Maximino Salazar Lechuga. Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), 2002

2,335 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/bf8e7d30aa129663315b4fee89c345b3fb6d7417

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Citations in the corpus

Optimization design for aerodynamic elements of high speed trains (2014, methodology): “In order to verify the efficiency of the approach proposed in this paper, the following test function [19] (minimization problem) is chosen for analysis:”
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”
Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer (2020, methodology): “Moreover, some algorithms adapt the parameters in updating velocity, such as MO-TRIBES [34] and pcc-sAMOPSO [36].”
Handling multiple objectives with particle swarm optimization (2004, methodology): “Our current proposal is an improved version of the algorithm reported in [5], in which we have added a constraint-handling mechanism and a mutation operator that considerably improves the exploratory capabilities of our original algorithm.”
A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications (2008, methodology): “Coello Coello and Lechuga (2002) also produced an alternative method of determining the local best solution by dividing the search space into hypercubes and applying a form of elitism, made possible by a repository of best non-dominated vectors.”
Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems (2015, neutral): “In order to find a well-spread Pareto optimal front, a food source is chosen from the least populated region of the obtained Pareto optimal front, similarly to the multi-objective particle swarm optimization (MOPSO) algorithm [46].”
Modelling and Pareto optimization of mechanical properties of friction stir welded AA7075/AA5083 butt joints using neural network and particle swarm algorithm (2013, neutral): “The algorithm of MOPSO is as following [29]:”
Particle swarm optimization versus genetic algorithms for phased array synthesis (2004, background): “Particle swarm optimizers have also been proposed for integer programming [50], multiobjective optimization [51], [52], and binary optimization [53], [54].”
Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients (2007, background): “The simplicity and efficiency of PSO motivated researchers to apply it to the MOO problems since 2002.”