A Multistrategy-Integrated Learning Sparrow Search Algorithm and Optimization of Engineering Problems

Abstract

The swarm intelligence algorithm is a new technology proposed by researchers inspired by the biological behavior of nature, which has been practically applied in various fields. As a kind of swarm intelligence algorithm, the newly proposed sparrow search algorithm has attracted extensive attention due to its strong optimization ability. Aiming at the problem that it is easy to fall into local optimum, this paper proposes an improved sparrow search algorithm (IHSSA) that combines infinitely folded iterative chaotic mapping (ICMIC) and hybrid reverse learning strategy. In the population initialization stage, the improved ICMIC strategy is combined to increase the distribution breadth of the population and improve the quality of the initial solution. In the finder update stage, a reverse learning strategy based on the lens imaging principle is utilized to update the group of discoverers with high fitness, while the generalized reverse learning strategy is used to update the current global worst solution in the joiner update stage. To balance exploration and exploitation capabilities, crossover strategy is joined to update scout positions. 14 common test functions are selected for experiments, and the Wilcoxon rank sum test method is achieved to verify the effect of the algorithm, which proves that IHSSA has higher accuracy and better convergence performance to obtain solutions than 9 algorithms such as WOA, GWO, PSO, TLBO, and SSA variants. Finally, the IHSSA algorithm is applied to three constrained engineering optimization problems, and satisfactory results are held, which proves the effectiveness and feasibility of the improved algorithm.

Optimal Deep Learning Based Intruder Identification in Industrial Internet of Things Environment

“…Finally, the SSO technique is exploited to fine-tune the hyper-parameters related to the GCN method. SSO is inspired by the vigilant and predatory behaviours of the sparrow population [25]. Discoverer, entrant, and vigilant are the roles played by every sparrow in its population.…”

Research and Application of a New Lion Swarm Algorithm

“…The above formula (7) and formula (8) are the lens reverse learning strategy in the literature [31] and the general reverse learning strategy in the literature [32,33]. The new individuals generated by the general reverse learning strategy are fixed, and this learning algorithm has the risk of falling into a local optimum in a high-dimensional search space.…”

“…At the same time, in order to verify the effect of this algorithm, eight algorithms are selected and compared with this algorithm. They are the improved lion swarm optimization algorithm (ILSO) proposed by Ji et al [20] ， lens learning sparrow search algorithm (LLSSA) proposed by Ouyang et al [30] ， Multi Strategy sparrow search algorithm (IHSSA) proposed by Wang et al [31] ，Gray Wolf algorithm (GWO), lion swarm optimization algorithm (LSO), particle swarm optimization (PSO), whale optimization algorithm (WOA). Meanwhile, in order to ensure the fairness among algorithms, the population of all algorithms is 100, the maximum number of iterations is 200, and the parameters of the algorithm are original parameters, as shown in Table 2.…”

Compound fault diagnosis of rolling bearings based on AVMD and IMOMEDA

“…Zhang and Zhu [25] applies chaotic mapping, a nonlinear decreasing factor and a dimensional learning strategy to balancing global and local search performance of DBO. Hence, inspired by above works, an improved dung beetle optimizer (IDBO) is introduced in this study, employing cubic chaotic mapping [26] and reverse learning strategies to enhance initial population quality [27]. In alignment with the learning strategy of particle swarm optimization [28], adaptive variable inertia weight factors are integrated to improve the algorithm's global and local exploration.…”