Handling multiple objectives with particle swarm optimization

By Carlos Artemio Coello Coello, Gregorio Toscano Pulido, Maximino Salazar Lechuga. IEEE Transactions on Evolutionary Computation, 2004

4,119 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/73ee298302fab0d92ec445266b46ad3b57dde858

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Citations in the corpus

Complex Network Clustering by Multiobjective Discrete Particle Swarm Optimization Based on Decomposition (2014, methodology): “Ever since its emergence, it has found nationwide applications in many domains [20]–[25], among which is the MOEA.”
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.”
Differential evolution for filter feature selection based on information theory and feature ranking (2018, methodology): “Furthermore, MODE [57] uses no complex and time consuming components to sort or renew individuals based on objective values like nondominated sorting genetic algorithm (NS-GAII) [68] or multi objective particle swarm optimization (MOPSO) [69] .”
Multiobjective optimization for empty and foam-filled square columns under oblique impact loading (2013, methodology): “The MOPSO algorithm features relatively low computational cost and fast convergence, as well as the ability to generate the best set of Pareto solutions close to the true Pareto front [52].”
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition (2007, background): “There is no decomposition involved in the majority of the current state-of-the-art multiobjective evolutionary algorithms (MOEAs) [2]–[4], [12]–[19].”
Preference-based Pareto optimization in certain and noisy environments (2009, neutral): “In addition to the NSGA-II algorithm, also the Strength Pareto Evolutionary Algorithm 2 (SPEA2) (Zitzler et al. 2001) and Multiobjective Particle Swarm Optimization (MOPSO) (Coello Coello et al. 2004) have been used.”
A Survey on Evolutionary Constrained Multiobjective Optimization (2023, background): “…car side impact problem [139], vibration platform design [150], robot gripper optimization [1], software engineering [151], four-bar truss design [152], spring design [153], servo turntable system [154], antenna design optimization [155], turning process parameters optimization [157], and…”
Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art (2006, methodology): “Although the approach is independent of the search engine adopted, they incorporate it into the MOPSO proposed in [ 12 ].”
A bare-bones multi-objective particle swarm optimization algorithm for environmental/economic dispatch (2012, methodology): “Three well-known MOPSO algorithms are selected for the performance comparison, which are the multi-objective particle swarm optimization algorithm (CMOPSO) [13], the multi-objective particle swarm with the sigma method (SMOPSO) [21], and the time variant multi-objective particle swarm optimization (”
Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems (2015, methodology): “[47]: Pi 1⁄4 c Ni ð3:21Þ”
ParEGO: a hybrid algorithm with on-line landscape approximation for expensive multiobjective optimization problems (2006, methodology): “of some early methods has also been overcome by a gradual progression with the introduction of more efficient algorithms such as niched Pareto genetic algorithm (NPGA) [30] and Pareto archived evolution strategy (PAES) [40], which first attempted to use reduce the cost of niching, through to the mic”