Improving particle swarm optimization performance with local search for high-dimensional function optimization

DOI10.1080/10556780903034514zbMath1205.90273OpenAlexW1991686551MaRDI QIDQ3161142

Yong-Jun Wang

Publication date: 12 October 2010

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10556780903034514

zbMATH Keywords

global optimizationgradient descent methodparticle swarm optimization (PSO)repulsion technique

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Nonconvex programming, global optimization (90C26) Nonlinear programming (90C30) Numerical methods based on nonlinear programming (49M37) Methods of reduced gradient type (90C52)

Related Items (2)

Gradient surfing: a new deterministic approach for low-dimensional global optimization ⋮ Modeling of biological intelligence for SCM system optimization

Cites Work

• Unnamed Item
• Optimization by Simulated Annealing
• A filled function method for finding a global minimizer of a function of several variables
• Global optimization and simulated annealing
• A hybrid descent method for global optimization
• Objective function stretching to alleviate convergence to local minima.
• Improved particle swarm optimization combined with chaos
• Taboo Search: An Approach to the Multiple Minima Problem
• TRUST: A Deterministic Algorithm for Global Optimization
• The Tunneling Algorithm for the Global Minimization of Functions
• Hybrid simulated annealing and direct search method for nonlinear unconstrained global optimization

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