A Hybrid STA Based on Nelder–Mead Simplex Search and Quadratic Interpolation

Abstract

State transition algorithm (STA) is a metaheuristic method for global optimization. However, due to the insufficient utilization of historical information, it still suffers from slow convergence speed and low solution accuracy on specific problems in the later stages. This paper proposes a hybrid STA based on Nelder–Mead (NM) simplex search and quadratic interpolation (QI). In the exploration stage, NM simplex search utilizes the historical information of STA to generate promising solutions. In the exploitation stage, QI utilizes the historical information to enhance the local search capacity. The proposed method uses an eagle strategy to maximize the efficiency and stability. The proposed method successfully combines the merits of the three distinct approaches: the powerful exploration capacity of STA, the fast convergence speed of NM simplex search and the strong exploitation capacity of QI. The hybrid STA is evaluated using 15 benchmark functions with dimensions of 20, 30, 50 and 100. Moreover, the results are statistically analyzed using the Wilcoxon signed-rank sum test. In addition, the applicability of the hybrid STA to solve real-world problems is assessed using the wireless sensor network localization problem. Compared with six state-of-the-art metaheuristic methods, the experimental results demonstrate the superiority and effectiveness of the proposed method.

Optimal Path Planning Method for Unmanned Surface Vehicles Based on Improved Shark-Inspired Algorithm

“…The sub-optimal points are obtained by internal compression, external compression, reflection and expansion of the worst point of the simplex. Then, the worst point of the simplex is replaced by the sub-optimal point, and the simplex is reconstructed to approach the global optimum continuously [27]. Since the simplex method is not affected by the continuity and derivability of the objective function, it has an excellent optimization ability, thus improving its capacity to break out the regional optimum.…”

“…structed to approach the global optimum continuously [27]. Since the simplex method is not affected by the continuity and derivability of the objective function, it has an excellent optimization ability, thus improving its capacity to break out the regional optimum.…”

“…λ is the number of path points in the set L. Then, the continuous steering angle between the path point L i and the subsequent path point L i+1 is denoted by θ i . To better assess the smoothness of the path planned by the mentioned algorithms, a path smoothness cost metric denominated mot has been established, which is defined as follows: (27) where i = 1,2, . .…”

Speed Analysis of Binary Search and Interpolation Search for Searching Identification Numbers on National Identity Cards

“…There are several algorithms that can be used to perform searches [7,8], one example of a search algorithm is the binary search algorithm and also the interpolation search algorithm. The speed of processing time and the complexity of the search algorithm were tested in this study to search for identification numbers on Indonesian national identity cards, with the goal of providing a comparison of the processing time and complexity between the two algorithms [9].…”