A fast constrained state transition algorithm

When solving constrained optimization problems in real industrial processes, both optimality and computational efficiency need to be considered. However, most existing meta-heuristic algorithms are slow to find the global optimum. The first reason is that the way to generate and select candidate solutions is time-consuming. The low probability to generate and select potential solutions in assisting the computational efficiency is another reason. In this paper, a simplified state transition algorithm (STA) and a novel constraint-handling technique are proposed to address the above issues for small size constrained optimization problems. Firstly, three out of four operators in basic STA to produce candidate solutions are selected and two operators are modified with adaptive parameter tuning, which have a large probability to generate potential solutions, but consumes less time. Secondly, the constraint-handling technique considers not only the objective function value and the constraint violation but also the difference among candidate solutions. Thirdly, the sequential quadratic programming embedded into the simplified STA can further speed up the convergence. Experiments are conducted on 22 well-known test functions from IEEE CEC2006 and 4 engineering constrained optimization problems, in comparison with state-of-the-art algorithms. The experimental results show that the proposed method is competitive in finding the optimum faster. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposed a simplified state transition algorithm and constraint-handling technique to address the slow convergence of meta-heuristic algorithms for small-sized constrained optimization problems. Experimental results demonstrate that the proposed method is competitive in finding the optimum faster.