In order to solve the problem that the butterfly optimization algorithm (BOA) is prone to low accuracy and slow convergence, the trend of study is to hybridize two or more algorithms to obtain a superior solution in the field of optimization problems. A novel hybrid algorithm is proposed, namely HPSOBOA, and three methods are introduced to improve the basic BOA. Therefore, the initialization of BOA using a cubic one-dimensional map is introduced, and a nonlinear parameter control strategy is also performed. In addition, the particle swarm optimization (PSO) algorithm is hybridized with BOA in order to improve the basic BOA for global optimization. There are two experiments (including 26 well-known benchmark functions) that were conducted to verify the effectiveness of the proposed algorithm. The comparison results of experiments show that the hybrid HPSOBOA converges quickly and has better stability in numerical optimization problems with a high dimension compared with the PSO, BOA, and other kinds of well-known swarm optimization algorithms.
To address the problems of uneven distribution and low coverage of wireless sensor network (WSN) nodes in random deployment, a node coverage optimization strategy with an improved COOT bird algorithm (COOTCLCO) is proposed. Firstly, the chaotic tent map is used to initialize the population, increase the diversity of the population, and lay the foundation for the global search for the optimal solutions. Secondly, the Lévy flight strategy is used to perturb the individual positions to improve the search range of the population. Thirdly, Cauchy mutation and an opposition-based learning strategy are fused to perturb the optimal solutions to generate new solutions and enhance the ability of the algorithm to jump out of the local optimum. Finally, the COOTCLCO algorithm is applied to WSN coverage optimization problems. Simulation results show that COOTCLCO has a faster convergence speed and better search accuracy than several other typical algorithms on 23 benchmark test functions; meanwhile, the coverage rate of the COOTCLCO algorithm is increased by 9.654%, 13.888%, 6.188%, 5.39%, 1.31%, and 2.012% compared to particle swarm optimization (PSO), butterfly optimization algorithm (BOA), seagull optimization algorithm (SOA), whale optimization algorithm (WOA), Harris hawks optimization (HHO), and bald eagle search (BES), respectively. This means that in terms of coverage optimization effect, COOTCLCO can obtain a higher coverage rate compared to these algorithms. The experimental results demonstrate that COOTCLCO can effectively improve the coverage rate of sensor nodes and improve the distribution of nodes in WSN coverage optimization problems.
MODFLOW is one of the most popular groundwater simulation tools available; however, the development of lake modules that can be coupled with MODFLOW is lacking apart from the LAK3 package. This study proposes a new approach for simulating lake
‐
groundwater interaction under steady‐state flow, referred to as the sloping lakebed method (SLM). In this new approach, discretization of the lakebed in the vertical direction is independent of the spatial discretization of the aquifer system, which can potentially solve the problem that the lake and groundwater are usually simulated at different scales. The lakebed is generalized by a slant at the bottom of each lake grid cell, which can be classified as fully submerged, dry, and partly submerged. The SLM method accounts for all lake sources and sinks, establishing a governing equation that can be solved using Newton's method. A benchmarking case study was conducted using a modified model setup in the LAK3 user manual. It was found that when there is a sufficient number of layers at the top of the groundwater model, SLM simulates an almost identical groundwater head as the LAK3‐based model; when the number of layers decreases, SLM is unaffected while LAK3 may be at a risk of giving unrealistic results. Additionally, the SLM can reflect the relationship between the simulated lake surface area and lake water depth more accurately. Therefore, the SLM method is a promising alternative to the LAK3 package when simulating lake
‐
groundwater interaction.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.