Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art

By Margarita Reyes-Sierra, Carlos Artemio Coello Coello. 2006

1,462 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/328618e58dc53640243f52ffa514fba2d0836d3b

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In Computational Theory and Mathematics

Citations in the corpus

Swarm Intelligence in Optimization (2008, background): “In recent years, PSO is also a popular choice of many researchers for handling multiobjective optimization [155] and dynamic optimization problems [102].”
jMetal: A Java framework for multi-objective optimization (2011, methodology): “Nevertheless, other metaheuristic techniques are also gaining momentum, as particle swarm optimization [9].”
A survey: algorithms simulating bee swarm intelligence (2009, background): “So many papers related with their applications have been presented to the literature and several survey papers regarding these studies can be found in the literature (Eberhart et al. 2001; Sierra and Coello 2006; Blum 2005; Dorigo and Blum 2005).”
Multiobjective cuckoo search for design optimization (2013, background): “Many new algorithms are emerging with many important applications [1,20,27,33,39,49].”
A Multiobjective Particle Swarm Optimizer Using Ring Topology for Solving Multimodal Multiobjective Problems (2018, background): “Subsequently, many PSO variants have been proposed to solve multiobjective or multimodal problems [15]–[19].”
Particle Swarm Methods (2018, background): “The interested reader can find comprehensive surveys in [105, 121].”
A survey on optimization metaheuristics (2013, methodology): “Considerable research has been also conducted into further refinement of the original formulation of PSO in both continuous and discrete problem spaces [146], and areas such as dynamic environments [29], parallel implementation [16] and MultiObjective Optimization [223].”
Multi-Objective Particle Swarm Optimization Approach for Cost-Based Feature Selection in Classification (2017, methodology): “In this paper, a domination-based strategy introduced in [20] is used to update the Pbest posi-”
Multi-Objective Flower Algorithm for Optimization (2014, neutral): “Then, decision-makers can rank different options, depending on their preferences or their utilities [1, 2, 3, 5, 6, 7, 23].”
SMPSO: A new PSO-based metaheuristic for multi-objective optimization (2009, background): “Since the first attempt proposed by Moore and Chapman in 1999 to extend it to multi-objective optimization [10], more than thirty different proposals of Multi-Objective Optimization PSOs (MOPSOs) have been reported in the specialized literature [15].”