Weighted-leader search: A new choice in metaheuristic and its application in real-world large-scale optimization. (February 2023)

• Record Type: Journal Article
• Authors: Wang, Xiaofei; Wu, Bi; Xuan, Yongbo; Liang, Yajun; Yang, Hang
• Is Part Of: Advances in engineering software. Volume 176(2023)
• Publication Date: 2023-02
• Subjects: Metaheuristic algorithm -- Numerical optimization -- Weighted-leader search -- CEC 2017 -- CEC 2008 -- Large-scale optimization -- UAV -- Path planning; Computer-aided engineering -- Periodicals; Engineering -- Computer programs -- Periodicals; Engineering -- Software -- Periodicals; Periodicals; 620.0028553
• DOI: 10.1016/j.advengsoft.2022.103405
• Languages: English
• ISSNs: 0965-9978

Abstract

Highlights: Weighted-leader Search (WLS) utilizes multiple top solutions to evolve population. WLS develops a simple framework containing four search mechanisms to balance exploration and exploitation. In CEC tests and large-scale tests, WLS exhibits an outstanding ability among the multiple state-of-the-art methods in terms of accuracy and efficiency. WLS is successfully applied in addressing the real-world optimization application of coordinated 4D path planning of UAV swarm. Abstract: Aiming at the robustness and real-time requirements of engineering applications, this paper develops a novel metaheuristic algorithm with a simple mathematical framework for numerical optimization, namely the weighted-leader search (WLS). With the WLS, a weighted mean point of the top solutions is developed to evolve the population. Additionally, several simple local search mechanisms are introduced to prevent the algorithm from premature convergence. Moreover, the WLS possesses fewer constant coefficients to reduce the parameter sensitivity. The performance of the WLS is evaluated with the CEC 2017 test suite with 10D, 30D, 50D and 100D problems, the CEC 2008 test suite with 100D, 500D and 1000D problems, large-scale benchmarks with 100D, 500D and 1000D (or beyond) problems and constrained engineering design problems. The statistical results of the experiments indicate that the WLS exhibits an outstanding ability among the state-of-the art algorithms in different competitions, which verifies the accuracy and efficiency of our algorithm in solving complex problems, as well as its robustness in large-scale evaluation. Moreover, the WLS is employed to address the cooperative path planning problem of an unmanned aircraft vehicle (UAV) swarm with 100 members. Through problem model construction, the path planning issue is transformed into a constrained optimization problem and solved by the WLS. The experimental results show that the proposed WLS is able to solve the path planning problem in real time within the constraints and significantly outperforms the other competitors, demonstrating the accuracy and time savings of the algorithm in solving real-world optimization problems.