Metaheuristic algorithms are widely used in engineering problems due to their high efficiency and simplicity. However, engineering challenges often involve multiple control variables, which present significant obstacles for metaheuristic algorithms. The Crested Porcupine Optimizer (CPO) is a metaheuristic algorithm designed to address engineering problems, but it faces issues such as falling into a local optimum. To address these limitations, this article proposes three new strategies: composite Cauchy mutation strategy, adaptive dynamic adjustment strategy, and population mutation strategy. The three proposed strategies are then introduced into CPO to enhance its optimization capabilities. On three well-known test suites, the improved CPO (CAPCPO) outperforms 11 metaheuristic algorithms. Finally, comparative experiments on seven real-world engineering optimization problems demonstrate the advantages and potential of CAPCPO in solving complex problems. The multifaceted experimental results indicate that CAPCPO consistently achieves superior solutions in most cases.

Northern Goshawk Optimization (NGO) is an efficient optimization algorithm, but it has the drawbacks of easily falling into local optima and slow convergence. Aiming at these drawbacks, an improved NGO algorithm named the Multi-Strategy Improved Northern Goshawk Optimization (MSINGO) algorithm was proposed by adding the cubic mapping strategy, a novel weighted stochastic difference mutation strategy, and weighted sine and cosine optimization strategy to the original NGO. To verify the performance of MSINGO, a set of comparative experiments were performed with five highly cited and six recently proposed metaheuristic algorithms on the CEC2017 test functions. Comparative experimental results show that in the vast majority of cases, MSINGO’s exploitation ability, exploration ability, local optimal avoidance ability, and scalability are superior to those of competitive algorithms. Finally, six real world engineering problems demonstrated the merits and potential of MSINGO.

The prediction of total ionospheric electron content (TEC) is of great significance for space weather monitoring and wireless communication. Recently, deep learning models have become increasingly popular in TEC prediction. However, these deep learning models usually contain a large number of hyperparameters. Finding the optimal hyperparameters (also known as hyperparameter optimization) is currently a great challenge, directly affecting the predictive performance of the deep learning models. The Beluga Whale Optimization (BWO) algorithm is a swarm intelligence optimization algorithm that can be used to optimize hyperparameters of deep learning models. However, it is easy to fall into local minima. This paper analyzed the drawbacks of BWO and proposed an improved BWO algorithm, named FAMBWO (Firefly Assisted Multi-strategy Beluga Whale Optimization). Our proposed FAMBWO was compared with 11 state-of-the-art swarm intelligence optimization algorithms on 30 benchmark functions, and the results showed that our improved algorithm had faster convergence speed and better solutions on almost all benchmark functions. Then we proposed an automated machine learning framework FAMBWO-MA-BiLSTM for TEC prediction, where MA-BiLSTM is for TEC prediction and FAMBWO for hyperparameters optimization. We compared it with grid search, random search, Bayesian optimization algorithm and beluga whale optimization algorithm. Results showed that the MA-BiLSTM model optimized by FAMBWO is significantly better than the MA-BiLSTM model optimized by grid search, random search, Bayesian optimization algorithm, and BWO.

The Jinjingzui gold (Au) deposit is located in the central part of Edong, China. The theoretical gold reserves are large with significant potential for mining and future development. This deposit is the only Au-bearing deposit discovered in the Middle-Lower Yangtze River Valley Metallogenic Belt, which has existing ore bodies that are mainly diorite. Re–Os dating of molybdenite from the Jinjingzui deposit confirm that the deposit formed in the Lower Cretaceous period, with an isochron age of 138.5 ± 2.7 Ma. The geochemical data of the rocks indicate that the diorite contains 54.75% to 56.66% SiO₂, 5.68% to 8.94% Fe₂O₃, 2.05% to 2.19% MgO, and 1.06% to 1.08% TiO₂, and with enrichment of large-ion lithophile elements (e.g., Rb and Ba). High-field-strength elements U-Nb–Ti displayed strong negative anomalies. Six pyrites from the Jinjingzui Au deposit δ³⁴S_V-PDB(‰) ranged from −2.4% to −8.4%, with an average value of −3.1%, and ²⁰⁶Pb~²⁰⁴Pb, ²⁰⁷Pb~²⁰⁴Pb, ²⁰⁸Pb~²⁰⁴Pb contents ranged from 17.77–18.58, 15.48–15.67 to 37.91–39.05, with average values of 18.14, 15.59 and 38.49, respectively. These values indicate that the metallic components originated from the upper mantle and lower crust. The Re concentrations in the molybdenites are significantly higher than those in other ores within the district (847.91~2018.58 × 10⁻⁶), suggesting a significant mantle component was involved in the mineralization process.

With the continuous acceleration of urbanization in China, the requirements for new district planning in urban construction are getting higher and higher, which requires the establishment of a more perfect, more integrated and more systematic multi-element urban geoscience service system to support urban planning and construction. It covers five levels, including exploration and evaluation, governance and restoration, engineering construction, urban operation management and industrialization base construction and development, including 13 aspects, such as geology, environment, engineering and smart city, so as to adapt to the requirements of “intensive, intelligent, green and low-carbon” new-type urbanization.