Paper Info
Title
Adaptive Weighted Particle Swarm Optimisation for Multi-objective Optimal Design of Alloy Steels
Abstract
In this paper, a modified Particle Swarm Optimisation (PSO) algorithm is presented to improve the performance of multi-objective optimisation. The PSO algorithm search capabilities are enhanced via the inclusion of the adaptive inertia weight and acceleration factor. In addition, a weighted aggregation function has been introduced within the algorithm to guide the selection of the personal and global bests, together with a non-dominated sorting algorithm to select the particles from one iteration to another. The proposed algorithm has been successfully applied to a series of well-known benchmark functions as well as to the multi-objective optimal design of alloy steels, which aims at determining the optimal heat treatment regimes and the required weight percentages for the chemical composites in order to obtain the pre-defined mechanical properties of the material. The results have shown that the algorithm can locate the constrained optimal design with a very good accuracy.
Year
DOI
Venue
2004
10.1007/978-3-540-30217-9_77
Lecture Notes in Computer Science
Keywords
Field
DocType
constrained optimization,sorting algorithm,chemical composition,heat treatment,multi objective optimization
Particle swarm optimization,Mathematical optimization,Search algorithm,Weight function,Evolutionary algorithm,Computer science,Algorithm,Optimal design,Sorting,Multi-objective optimization,Sorting algorithm
Conference
Volume
ISSN
Citations 
3242
0302-9743
17
PageRank 
References 
Authors
0.99
9
3
Name
Order
Citations
PageRank
Mahdi Mahfouf123533.17
Min-you Chen227422.18
Derek A. Linkens321525.36