Improved Tuna Swarm Optimization Algorithm for Engineering Design Problems

Abstract

Tuna Swarm Optimization (TSO) which is developed by being inspired by the hunting strategies of the tuna fish is a metaheuristic optimization algorithm (MHA). TSO is able to solve some optimization problems successfully. However, TSO has the handicap of having premature convergence and being caught by local minimum trap. This study proposes a mathematical model aiming to eliminate these disadvantages and to increase the performance of TSO. The basic philosophy of the proposed method is not to focus on the best solution but on the best ones. The Proposed algorithm has been compared to six current and popular MHAs in the literature. Using classical test functions to have a preliminary evaluation is a frequently preferred method in the field of optimization. Therefore, first, all the algorithms were applied to ten classical test functions and the results were interpreted through the Wilcoxon statistical test. The results indicate that the proposed algorithm is successful. Following that, all the algorithms were applied to three engineering design problems, which is the main purpose of this article. The original TSO has a weak performance on design problems. With optimal costs like 1.74 in welded beam design problem, 1581.47 in speed reducer design problem, and 38.455 in I-beam design problem, the proposed algorithm has been the most successful one. Such a case leads us to the idea that the proposed method of this article is successful for improving the performance of TSO.