PPSN 2004: Parallel Problem Solving from Nature - PPSN VIII pp 762-771 | Cite as
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
Keywords
Pareto Front Particle Swarm Optimisation Algorithm Inertia Weight Pareto Solution Modify Particle Swarm OptimisationPreview
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