SMPSO: A new PSO-based metaheuristic for multi-objective optimization

By Antonio J. Nebro, Juan José Durillo, José García-Nieto, Carlos C. Coello, Francisco Luna, Enrique Alba. International Conference on Multiple Criteria Decision Making, 2009

653 citations recorded by the source corpus.

In this work, we present a new multi-objective particle swarm optimization algorithm (PSO) characterized by the use of a strategy to limit the velocity of the particles. The proposed approach, called Speed-constrained Multi-objective PSO (SMPSO) allows to produce new effective particle positions in those cases in which the velocity becomes too high. Other features of SMPSO include the use of polynomial mutation as a turbulence factor and an external archive to store the non-dominated solutions found during the search. Our proposed approach is compared with respect to five multi-objective metaheuristics representative of the state-of-the-art in the area. For the comparison, two different criteria are adopted: the quality of the resulting approximation sets and the convergence speed to the Pareto front. The experiments carried out indicate that SMPSO obtains remarkable results in terms of both, accuracy and speed.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/317a1d1cbc8d3fc8789aa30c834e9225a23b4f09

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Citations in the corpus

Bio-inspired computation: Where we stand and what's next (2019, background): “NSGA-II [14], MOEA/D [15], SMPSO [16] and others proposed along the years [17,18]).”
A Comprehensive Survey on the Multiple Travelling Salesman Problem: Applications, Approaches and Taxonomy (2021, result): “The authors compared the proposed approach with well known existing multi-objective approaches such as OMOPSO[52], SPEA2[53], NSGA-II[54], and SMPSO[55] and proved its superiority.”
jMetal: A Java framework for multi-objective optimization (2011, methodology): “We have used jMetal in a number of works: we have proposed new multi-objective techniques (AbYSS [16], a scatter search algorithm; SMPSO, a particle swarm optimization technique [23]; or CellDE [19], a cellular genetic algorithm hybridized with”
PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum] (2017, methodology): “rential evolution 3 MOEA/D-DE [18] 2009 MOEA/D based on differential evolution Multi-objective Particle Swarm Optimization Algorithms MOPSO [61] 2002 Multi-objective particle swarm optimization SMPSO [62] 2009 Speed-constrained multi-objective particle swarm optimization dMOPSO [63] 2011 Decompositi”
A Framework for Large-Scale Multiobjective Optimization Based on Problem Transformation (2018, methodology): “In our experiments in Section VI, we use the WOF framework on three well-known algorithms (NSGA-II [8], SMPSO [9], and GDE3 [10]) and compare their performances with each other and their original algorithm versions on a variety of well-known benchmark functions.”
A competitive mechanism based multi-objective particle swarm optimizer with fast convergence (2018, methodology): “Moreover, as reported in some recent studies [5, 6, 25], PSO also has good potential in solving MOPs.”
Evolutionary Large-Scale Multi-Objective Optimization: A Survey (2021, methodology): “…experiments, three MOEAs (i.e., the differential evolution based GDE3 [85], the ge-netic algorithm based NSGA-II [34], and the particle swarm optimization based SMPSO [122]) were adopted to optimize the weight vectors, which converge much faster than conventional MOEAs on many challenging LSMOPs.”
Efficient Large-Scale Multiobjective Optimization Based on a Competitive Swarm Optimizer (2020, methodology): “In CCSMPSO [42], the SMPSO [33] is enhanced and parallelized by a cooper-”
The jMetal framework for multi-objective optimization: Design and architecture (2010, methodology): “Besides NSGA-II, jMetal includes the implementation of a number of both classic and modern multi-objective optimizers; some examples are: SPEA2 [27], PAES [26], OMOPSO [29], MOCell [30], AbYSS [31], MOEA/D [32], GDE3 [33], IBEA [34], and SMPSO [ 35 ].”