A dynamic adaptive particle swarm optimization and genetic algorithm for different constrained engineering design optimization problems

A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search abilit...

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Vydané v:Advances in mechanical engineering Ročník 11; číslo 3
Hlavní autori: Zhu, Hao, Hu, Yumei, Zhu, Weidong
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London, England SAGE Publications 01.03.2019
Sage Publications Ltd
SAGE Publishing
Predmet:
Adaptive algorithms
Basic converters
Constraints
Convergence
Crossovers
Design engineering
Design optimization
Genetic algorithms
Heuristic methods
Mathematical analysis
Mechanical engineering
Mechanical engineering design
Operators
Optimization algorithms
Particle swarm optimization
Robustness (mathematics)
Search process
Constrained engineering design optimization problems
meta-heuristic
genetic algorithm
dynamic adaptive
particle swarm optimization
continuous and discrete design variables
ISSN:1687-8132, 1687-8140
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Shrnutí:A dynamic adaptive particle swarm optimization and genetic algorithm is presented to solve constrained engineering optimization problems. A dynamic adaptive inertia factor is introduced in the basic particle swarm optimization algorithm to balance the convergence rate and global optima search ability by adaptively adjusting searching velocity during search process. Genetic algorithm–related operators including a selection operator with time-varying selection probability, crossover operator, and n-point random mutation operator are incorporated in the particle swarm optimization algorithm to further exploit optimal solutions generated by the particle swarm optimization algorithm. These operators are used to diversify the swarm and prevent premature convergence. Tests on nine constrained mechanical engineering design optimization problems with different kinds of objective functions, constraints, and design variables in nature demonstrate the superiority of the dynamic adaptive particle swarm optimization and genetic algorithm against several other meta-heuristic algorithms in terms of solution quality, robustness, and convergence rate in most cases.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1687-8132
1687-8140
DOI:10.1177/1687814018824930