A modified PSO with a dynamically varying population and its application to the multi-objective optimal design of alloy steels

In this paper, a new mechanism for dynamically varying the population size is proposed based on a previously modified PSO algorithm (nPSO). This new algorithm is extended to the multi-objective optimisation case by applying the Random Weighted Aggregation (RWA) technique and by maintaining an archive for preserving the suitable Pareto-optimal solutions. Both the single objective and multi-objective optimisation algorithms were tested using well-known benchmark problems. The results show that the proposed algorithms outperform some of the other salient Evolutionary Algorithms (EAs). The proposed algorithms were further applied successfully to the optimal design problem of alloy steels, which aims at determining the optimal heat treatment regime and the required weight percentages for chemical composites to obtain the desired mechanical properties of steel hence minimising production costs and achieving the overarching aim of ‘right-first-time production’ of metals.

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