Dynamic analysis for the selection of parameters and initial population, in particle swarm optimization

DOI10.1007/s10898-009-9493-0zbMath1206.90121OpenAlexW2080647549MaRDI QIDQ609562

Giovanni Fasano, Antonio Pinto, Emilio Fortunato Campana

Publication date: 1 December 2010

Published in: Journal of Global Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10898-009-9493-0

zbMATH Keywords

convergence analysisevolutionary optimizationdynamic linear system

Mathematics Subject Classification ID

Nonconvex programming, global optimization (90C26) Approximation methods and heuristics in mathematical programming (90C59)

Related Items (8)

Uniform initialization in response space for PSO and its applications ⋮ Dynamic analysis for the selection of parameters and initial population, in particle swarm optimization ⋮ Incremental learning optimization on knowledge discovery in dynamic business intelligent systems ⋮ A novel hybrid PSO-based metaheuristic for costly portfolio selection problems ⋮ Dynamic guiding particle swarm optimization with embedded chaotic search for solving multidimensional problems ⋮ Global versus local search: the impact of population sizes on evolutionary algorithm performance ⋮ Modelling and parameter identification of a nonlinear enzyme-catalytic time-delayed switched system and its parallel optimization ⋮ MURAME parameter setting for creditworthiness evaluation: data-driven optimization

Uses Software

• Global Optimization Toolbox For Maple
• LGO
• Genocop
• CEC 05

Cites Work

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• New global optimization methods for ship design problems
• Dynamic analysis for the selection of parameters and initial population, in particle swarm optimization
• On initial populations of a genetic algorithm for continuous optimization problems
• The particle swarm optimization algorithm: Convergence analysis and parameter selection
• Evolutionary optimization.
• A study of particle swarm optimization particle trajectories
• Evolutionary Computation for Modeling and Optimization
• Introduction to global optimization.

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