Strategies for finding good local guides in multi-objective particle swarm optimization (MOPSO)

By Sanaz Mostaghim, Jürgen Teich. Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706), 2003

734 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/90bf9eb226e366ec5d8da68312fe86fe260bd029

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Citations in the corpus

A review of particle swarm optimization. Part II: hybridisation, combinatorial, multicriteria and constrained optimization, and indicative applications (2008, methodology): “The Sigma method was introduced by Mostaghim and Teich (2003a) to redefine the selection of each particle's local best.”
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 (”
Multiple Populations for Multiple Objectives: A Coevolutionary Technique for Solving Multiobjective Optimization Problems (2013, background): “, when determining the personal historically best position [23] or selecting the globally best position [24].”
Multi-Objective Particle Swarm Optimization with time variant inertia and acceleration coefficients (2007, methodology): “The simplicity and efficiency of PSO motivated researchers to apply it to the MOO problems since 2002.”
Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems (2008, methodology): “In the first case, the leader is determined by some procedure, without any randomness involved, such as the Pareto ranking scheme [128], the sigma method [129] or the”
Handling multiple objectives with particle swarm optimization (2004, methodology): “• Mostaghim and Teich [25] proposed a sigma method in which the best local guides for each particle are adopted to improve the convergence and diversity of a PSO approach used for multiobjective optimization.”
Particle swarm optimization algorithm: an overview (2017, background): “Moreover, Mostaghim and Teich (2003) also experimentally studied the performance of uniﬁed particle swarm algorithm in the dynamic environment.”
Main Barriers and Possible Enablers of ICTs Integration into Pre-service Teacher Education Programs (2009, methodology): “We therefore choose an elite solution from E to serve as gbest using the Sigma strategy ( Mostaghim & Teich, 2003 ).”
Multiobjective Particle Swarm Algorithm With Fuzzy Clustering for Electrical Power Dispatch (2008, background): “[28], Fieldsend and Singh [29], Mostaghim and Teich [30], [31], Coello [26], Zhang [32], Lechuga and Rowe [33], and Knowles [34].”
Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art (2006, background): “h Mostaghimand Teich [44]: They proposea sigma methodin which the leaderfor eachparticle is selectedin orderto improvetheconvergenceanddiversity of a MOPSOapproach.”
Complex Network Clustering by Multiobjective Discrete Particle Swarm Optimization Based on Decomposition (2014, methodology): “…used a turbulence operator [27] 2003 Hu et al. used secondary population [28] 2003 Mostaghim and Teich used sigma method to find best local guide [29] 2003 Zhang et al. selected the gbest as the average of the complete set of the gbest [30] 2004 Baumgartner et al. linear aggregating functions,…”
Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and epsilon-Dominance (2005, methodology): “Table 3. Comparison of results between our approach (denoted by OMOPSO), NSGA-II [13], SPEA2 [16], MOPSO [2], sMOPSO [ 3 ] and cMOPSO [8], for ZDT2, with respect to the binary measures.”
A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts (2005, methodology): “, [9,10]) but is more than compensated for by improved convergence and coverage.”
The mechanics of composite corrugated structures: A review with applications in morphing aircraft (2015, methodology): “[115]) was performed with the objectives of maximising the internal energy and minimizing the peak crushing force.”