Application of chaotic mutation strategy based single candidate optimizer to engineering design problems

Abstract

Heuristic search algorithms are among the frequently used methods for solving complex optimization problems. These algorithms are usually based on swarm or population-based approaches and play an important role in solving complex problems by providing efficient and flexible approaches. These swarm-based approaches have disadvantages such as high computational cost, some of them have a large number of parameters, and early convergence problems. Unlike swarm-based algorithms, the Single Candidate Optimizer (SCO) algorithm tries to reach the best solution by reducing the computational cost by using only a single candidate. The SCO algorithm is a simple, low-parameter, low-cost and high-performance heuristic algorithm. However, the SCO algorithm also has some disadvantages such as limited exploration ability of a single candidate solution, the risk of being caught in local optima and the possibility of getting stuck in suboptimal regions. In this paper, we present a chaotic mutation strategy based Single Candidate Optimizer (CSCO) algorithm, which is constructed using a new mutation technique based on chaotic functions to overcome these drawbacks and improve the performance of the SCO algorithm. In order to evaluate the performance of the proposed CSCO algorithm, some engineering design problems found in the literature are considered. Some of them are Welded Beam Design (WBD), Compression Spring Design (CSD) and Pressure Vessel Design (PVD). In addition to the original SCO algorithm, popular heuristic algorithms frequently used in the literature were used in the comparisons.