Constrained particle swarm optimization using a bi-objective formulation

DOI10.1007/S00158-009-0380-6zbMath1274.90514OpenAlexW2133755953MaRDI QIDQ381359

Gerhard Venter, Raphael T. Haftka

Publication date: 15 November 2013

Published in: Structural and Multidisciplinary Optimization (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10019.1/14645

zbMATH Keywords

multi-objective optimizationcomposite design problemconstrained particle swarm optimization

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Approximation methods and heuristics in mathematical programming (90C59) Optimization of other properties in solid mechanics (74P10) Random materials and composite materials (74A40)

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Using multi-objective evolutionary algorithms for single-objective constrained and unconstrained optimization ⋮ An adaptive penalty scheme to solve constrained structural optimization problems by a craziness based particle swarm optimization ⋮ On improving normal boundary intersection method for generation of Pareto frontier ⋮ A mixed-discrete particle swarm optimization algorithm with explicit diversity-preservation ⋮ Using multi-objective evolutionary algorithms for single-objective optimization ⋮ Opposition-based barebones particle swarm for constrained nonlinear optimization problems ⋮ Filter-based adaptive Kriging method for black-box optimization problems with expensive objective and constraints ⋮ RANDOM NEURAL NETWORK LEARNING HEURISTICS

Cites Work

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• Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: A survey of the state-of-the-art
• Evolutionary Multi-Criterion Optimization
• Nonlinear programming without a penalty function.

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