A new Reduced Space Searching Algorithm (RSSA) and its application in optimal design of alloy steels

In this paper, a new search and optimisation algorithm based on a reduced space searching strategy, named RSSA, is presented. This algorithm originates from an idea which relates to a simple experience when humans search for an optimal solution to a ´real-life´ problem, i.e. when humans search for a candidate solution given a certain objective, a large area tends to be scanned first; should one succeed in finding clues in relation to the predefined objective, then the search space is greatly reduced for a more detailed search. The proposed algorithm is validated via well-known benchmark functions and is found to be efficient. Furthermore, the algorithm is extended to include the multiobjective case. Simulation results of optimising some challenging benchmark multiobjective problems, including the ZDT and DTLZ series problems, suggest that the new algorithm can locate the Pareto-optimal front and performs better than some other salient optimisation algorithms. Then, this proposed algorithm is successfully applied to the 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.