Browsing by Author "Dong, Yongquan"

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    Biological survival optimization algorithm with its engineering and neural network applications
    (Springer, 2023-02-13) Wang, Likai; Zhang, Qingyang; He, Xiangyu; Yang, Shengxiang; Jiang, Shouyong; Dong, Yongquan
    This study proposes a novel and lightweight bio-inspired computation technique named biological survival optimizer (BSO), which simulates the escape behavior of prey in the natural environment. This algorithm consists of two important courses, escape phase and adjustment phase. Specifically, in the escape phase, each search agent is required to update its location using the best, the worst and a neighboring individual of the population. The adjustment phase is implemented using the simplex algorithm for search better location of the worst agent within a small region. The effectiveness of the BSO is validated on the CEC2017 benchmark problems, three classical engineering structural problems and neural network training models. Simulation comparison results considering both convergence and accuracy simultaneously show that BSO has competitive performance compared with other state-of-the-art optimization techniques.
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    Multi-strategy grey wolf optimization algorithm for global optimization and engineering applications
    (Springer, 2024-11-06) Wang, Likai; Zhang, Qingyang; Yang, Shengxiang; Dong, Yongquan
    The grey wolf optimizer(GWO), a population-based meta-heuristic algorithm, mimics the predatory behavior of grey wolf packs. Continuously exploring and introducing improvement mechanisms is one of the keys to drive the development and application of GWO algorithms. To overcome the premature and stagnation of GWO, the paper proposes a multiple strategy grey wolf optimization algorithm (MSGWO). Firstly, an variable weights strategy is proposed to improve convergence rate by adjusting the weights dynamically. Secondly, this paper proposes a reverse learning strategy, which randomly reverses some individuals to improve the global search ability. Thirdly, the chain predation strategy is designed to allow the search agent to be guided by both the best individual and the previous individual. Finally, this paper proposes a rotation predation strategy, which regards the position of the current best individual as the pivot and rotate other members for enhacing the exploitation ability. To verify the performance of the proposed technique, MSGWO is compared with seven state-of-the-art meta-heuristics and four variant GWO algorithms on CEC2022 benchmark functions and three engineering optimization problems. The results demonstrate that MSGWO has better performance on most of benchmark functions and shows competitive in solving engineering design problems.
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    Solving dynamic multi-objective problems using polynomial fitting-based prediction algorithm
    (Elsevier, 2022-08-11) Zhang, Qingyang; He, Xiangyu; Yang, Shengxiang; Dong, Yongquan; Song, Hui; Jiang, Shouyong
    Recently, dynamic multi-objective optimization has received growing attention due to its popularity in real-world applications. Inspired by polynomial fitting, this paper proposes a polynomial fitting-based prediction algorithm (PFPA) and incorporates it into the model-based multi-objective estimation of distribution algorithm (RM-MEDA) for solving dynamic multi-objective optimization problems. When an environment change is detected, the main mission of PFPA is to predict high-quality search populations for tracking the moving Pareto-optimal set effectively. Firstly, the non-dominated solutions obtained in past environments are utilized to predict high-quality solutions based on a multi-step movement strategy. Secondly, a polynomial fitting-based strategy is designed to fit the distribution of variables according to the obtained search populations, and capture the relationship between variables in the new search environment. Thirdly, some effective search agents are generated for improving population convergence and diversity based on characteristics of variables. To evaluate the performance of the proposed algorithm, experimental results on a set of benchmark functions, with a variety of different dynamic characteristics and difficulties, and two classical dynamic engineering design problems show that PFPA is competitive with some state-of-the-art algorithms.