An adaptive resistance and stamina strategy-based dragonfly algorithm for solving engineering optimization problems. Issue 5 (20th October 2020)

• Record Type: Journal Article
• Authors: Yuan, Yongliang; Wang, Shuo; Lv, Liye; Song, Xueguan
• Is Part Of: Engineering computations. Volume 38:Issue 5(2021)
• Page Start: 2228
• Page End: 2251
• Publication Date: 2020-10-20
• Subjects: Adaptive resistance and stamina strategy -- Bucket wheel reclaimer -- Dragonfly algorithm -- Optimization algorithm; Computer-aided engineering -- Periodicals; Computer graphics -- Periodicals; 620.00285
• DOI: 10.1108/EC-08-2019-0362
• Languages: English
• ISSNs: 0264-4401

Abstract

Abstract : Purpose: Highly non-linear optimization problems exist in many practical engineering applications. To deal with these problems, this study aims to propose an improved optimization algorithm, named, adaptive resistance and stamina strategy-based dragonfly algorithm (ARSSDA). Design/methodology/approach: To speed up the convergence, ARSSDA applies an adaptive resistance and stamina strategy (ARSS) to conventional dragonfly algorithm so that the search step can be adjusted appropriately in each iteration. In ARSS, it includes the air resistance and physical stamina of dragonfly during a flight. These parameters can be updated in real time as the flight status of the dragonflies. Findings: The performance of ARSSDA is verified by 30 benchmark functions of Congress on Evolutionary Computation 2014's special session and 3 well-known constrained engineering problems. Results reveal that ARSSDA is a competitive algorithm for solving the optimization problems. Further, ARSSDA is used to search the optimal parameters for a bucket wheel reclaimer (BWR). The aim of the numerical experiment is to achieve the global optimal structure of the BWR by minimizing the energy consumption. Results indicate that ARSSDA generates an optimal structure of BWR and decreases the energy consumption by 22.428% compared with the initial design. Originality/value: A novel search strategy is proposed to enhance the global exploratory capability and convergence speed. This paper provides an effective optimization algorithm for solving constrained optimization problems.