GrS Algorithm for Solving Gas Transmission Compressor Design Problem

Abstract

This paper is a continuous study of our recently proposed gradient-free deterministic method, named granular sieving (GrS), for its application exploration. GrS is developed to solve global optimization problems for Lipschitz continuous functions defined in arbitrary path-wise connected compact sets in Euclidean spaces. It can be regarded as granular sieving with synchronous analysis in both the domain and range of the objective function. The algorithm is easy to implement with moderate computational cost. Although the effectiveness of the algorithm has been verified on the benchmark databases, its feasibility in real optimization problems remains to be explored. This paper applies GrS in a well-known real-world engineering optimization problem, gas transmission compressor design (GTCD), which requires to determine the minimum cost for a gas pipeline transmission system per day. The experimental results are promising compared with some classic algorithms.