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In this study, we introduce the pied kingfisher optimizer (PKO), a novel swarm-based meta-heuristic algorithm that draws inspiration from the distinctive hunting behavior and symbiotic relationships observed in pied kingfishers in the natural world. The PKO algorithm is structured around three distinct phases: perching/hovering for prey (exploration/diversification), diving for prey (exploitation/intensification), and fostering symbiotic relations. These behavioral aspects are translated into mathematical models capable of effectively addressing a wide array of optimization challenges across diverse search spaces. The algorithm’s performance is rigorously evaluated across thirty-nine test functions, which encompass various unimodal, multimodal, composite, and hybrid ones. Additionally, eight real-world engineering optimization problems, including both constrained and unconstrained scenarios, are considered in the assessment. To gauge PKO’s efficacy, it is subjected to a comparative analysis against 3 categories of rival optimizers. The 1st category comprises well-established and widely-cited optimizers such as particle swarm optimization and genetic algorithm. The 2nd category encompasses recently published algorithms, including Harris Hawks optimization, Whale optimization algorithm, sine cosine algorithm, Grey Wolf optimizer, gravitational search algorithm, and moth-flame optimization. The 3rd category includes advanced algorithms, such as covariance matrix adaptation evolution strategy and Ensemble Sinusoidal Differential Covariance Matrix Adaptation with Euclidean Neighborhood (LSHADE-cnEpSin). The comparative analysis employs various performance metrics, including the Friedman mean rank and the Wilcoxon rank-sum test, to reveal PKO’s effectiveness and efficiency. The overall results highlight PKO’s exceptional ability to tackle intricate optimization problems characterized by challenging search spaces. PKO demonstrates superior exploration and exploitation tendencies while effectively avoiding local optima. The source code for the PKO algorithm is publicly accessible at  https://www.mathworks.com/matlabcentral/fileexchange/160043-pied-kingfisher-optimizer-pko .

Long-term use of agrochemicals in the oil palm plantation sector, particularly chemical pesticides for pest control, can have a negative impact on the environment. The use of biological control has long been advocated as an alternative to pesticides, but empirical evidence in the context of oil palm plantations is very limited. This study aims to assess whether the installation of artificial bird perches can attract predatory birds, thereby enhancing the potential for biological pest control, particularly against rodents in oil palm plantations. We installed six artificial bird perches in a mature oil palm plantation in Keratong, Pahang, Peninsular Malaysia. Our data showed the presence of eight farmland bird species that utilised artificial perches, specifically White-throated Kingfisher, Collared Kingfisher, Oriental Magpie-Robin, Javan Myna, Barn Owl, Spotted Wood-Owl, Crested Serpent-Eagle, and Spotted Dove, and one forest associated species, Crested Goshawk. Our study is the first to describe predation of pest rodents by many of these eight farmland birds in oil palm plantations during daytime. Of the 1013 images taken, 48 showed rodent predation involving White-throated Kingfisher, Collared Kingfisher, Crested Serpent-Eagle, Crested Goshawk, Barn Owl and Spotted Wood-Owl. In a 24 h period, bird visits to artificial perches peaked between 10:00 am and 5:30 pm. Our findings suggest that artificial bird perches can facilitate the occurrence of diurnal and nocturnal predatory birds in oil palm plantations to control rodent infestation. The installation of artificial bird perches within oil palm plantations has the potential to enhance the ecosystem service of rodent biological control and increase avian biodiversity in palm oil producing countries.

Maritime activities have become increasingly frequent with the deepening of economic globalization, highlighting the burgeoning significance of maritime rescue. However, in practical applications, UAVs for maritime rescue face numerous challenges, such as limited endurance and inadequate autonomous planning capabilities. To optimize flight routes and circumvent adverse sea conditions, an improved Pied Kingfisher Optimizer (IPKO) that incorporates refraction reverse learning, variable spiral search, and Cauchy mutation strategies was proposed. Comparative experiments conducted on CEC2005 and CEC2022 datasets with seven traditional algorithms demonstrate that the proposed algorithm exhibits superior precision and convergence speed. Subsequently, a path planning objective function was constructed based on trajectory cost and threat cost to simulate a 3D space for UAV maritime rescue missions, and the IPKO algorithm was applied to address the UAV path planning problem. The results showed that the total cost incurred by the IPKO algorithm decreased by 5.77% compared to the PKO algorithm and by 51.19% compared to the SCA algorithm. Finally, through UAV flight tests validating its practical applicability, it is ascertained that IPKO can enhance rescue efficiency in complex maritime rescue environments.

Previous research has reported avian plastic ingestion in marine bird species. Yet, while research attention on plastic pollution is shifting from marine to freshwater ecosystems, very few information on plastic ingestion is available for freshwater birds. Here, we examined the presence of microplastic in regurgitated pellets of the common kingfisher ( Alcedo atthis ) collected along the Ticino River (North Italy). In total, 133 kingfisher’s pellets were examined between March and October 2019 from 54 transects along the river. Plastic elements were detected and identified by visual inspection followed by μ-FTIR and SEM-EDS. Overall, we found 12 (micro)plastics from at least three different polymers in 7.5% of the pellets. This study provides the first report of plastic uptake of this bird species. It highlights the importance of spectroscopic techniques in plastic monitoring studies in order to avoid misidentification of items found. Documenting the presence of plastic ingestion by top carnivores such as fish-eating birds is necessary to understand the pervasiveness and impact of (micro)plastic pollution in food webs of freshwater ecosystems.

Color aberrations in birds corresponds with important ecological functions, including thermoregulation and physiological impacts, camouflage and increased predation, and social interactions with conspecifics. Color aberrations in birds have been reported frequently in the scientific literature, but aberrations in many species remain undocumented or understudied. We investigated records of leucism in malachite kingfishers (Corythornis cristatus) from observations of community scientists on iNaturalist and eBird in Uganda. Leucistic kingfishers were only observed within the Queen Elizabeth National Park (QENP), Uganda. When considering all observations of malachite kingfishers that included photographs within the QENP, leucistic individuals accounted for 13.0% and 10.4% of total malachite kingfisher observations within the study area from iNaturalist and eBird, respectively. Leucistic observations were recorded from September 2015 through February 2017, making up 60.0% and 68.2% of observations of malachite kingfishers within the study area from iNaturalist and eBird during that time, respectively. The localized and short documentation period suggests observations represent a single individual, while the high observation rate likely corresponds with collection bias due to the novelty of the individual. Our findings help to better understand the ecological importance and potential consequences for color‐aberrant individuals, although color aberration did not appear to inhibit our subject's ability to find a mate. Our work also highlights how participatory science can promote the documentation of color‐aberrant individuals in wild populations, although it poses challenges when trying to estimate abundance. We investigated records of hypopigmentation in malachite kingfishers (Corythornis cristatus) from observations of community scientists on iNaturalist and eBird in Uganda. Hypopigmented individuals accounted for 13.0% and 10.4% of total malachite kingfisher observations within the study area from iNaturalist and eBird, respectively; but from September 2015 through February 2017, hypopigmented individuals made up 60.0% and 68.2% of observations. The localized and short documentation period suggests observations represent a single individual, while the high observation rate likely corresponds with collection bias due to the novelty of the individual.

This paper proposes a new fuzzified multi-objective wireless communication optimization model that maximizes the quantity and placement of Reconfigurable Intelligent Surfaces (RISs). In order to meet realistic deployment constraints like non-overlapping and acceptable location, the model aims to decrease the number of deployed RISs while raising the achievable rate. The Modified Pied Kingfisher Optimization Algorithm (MPKOA) is suggested as a solution to this intricate optimization issue. MPKOA features many significant improvements over the traditional Pied Kingfisher Optimization Algorithm (PKOA), such as energy-based motion control, adaptive subgrouping, flock cooperation, and memory-driven re-perching. These techniques speed up convergence, improve solution precision, reduce computation time, and balance exploration and exploitation. MPKOA performs better than standard PKOA, Enhanced version of PKOA (EPKO), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and other existing algorithms, according to extensive comparisons. MPKOA can achieve up to 20% higher optimization values and 30% faster convergence, according to simulation data. In addition, the proposed MPKOA reduces computational complexity and runtime by about 50% when compared to standard PKOA-based approaches since it only requires single fitness evaluation per iteration. This enables the deployment of fewer RISs while still achieving higher communication rates. In multiuser wireless systems, MPKOA offers a robust and effective approach to RIS placement optimization, which helps to boost capacity and provide more energy-efficient 6G communication networks.

This study proposes a new approach for optimizing the penetration level of photovoltaic units (PVUs) to achieve both engineering and economic benefits in a standard distribution power system. The Mirage Search Optimization (MSO) and the Pied Kingfisher Optimizer (PKO) are applied to minimize the total active power loss (TRPL) in the IEEE 69-node system. Two cases are considered: Case 1, where PVUs inject only active power, and Case 2, where PVUs inject both active and reactive power. The results demonstrate that PKO outperforms MSO and several metaheuristic algorithms reported in the literature. In Case 2, the optimal PVU penetration level of 67.17% significantly reduces TRPL compared with Case 1. The effectiveness of this optimized penetration level is further evaluated by comparing it with four other penetration levels: 25%, 50%, 75%, and 100%. PKO is then used to optimize the 24 h energy cost considering load variation and dynamic PV generation during four months of the year, including December, September, June, and March, which are ordered by increasing solar radiation across seasons under Vietnam’s climatic conditions. The results show that although the 75% penetration level slightly reduces the energy purchasing cost compared with the optimal level, it requires higher power capacity. Therefore, the optimized penetration level of 67.17% provides a balanced solution for reducing power losses while maintaining economic efficiency.

Inspired by the hovering, diving, and cooperative hunting behaviors of the pied kingfisher, the Pied Kingfisher Optimizer (PKO) has demonstrated competitive performance in optimization tasks. However, it exhibits several phase-specific limitations, including uneven population distribution caused by random initialization, insufficient use of historical information during exploration, over-reliance on the global best during exploitation, and weakly guided perturbation in the symbiosis phase. To address these issues, this study proposes an Improved Pied Kingfisher Optimizer (IPKO), which incorporates biologically inspired adaptive strategies. Drawing inspiration from the kingfisher’s diverse perching, gaze adjustment during hovering, evasive diving after failed strikes, and territory shifting based on flock position, four mechanisms are developed. Specifically, sine chaotic opposition-based initialization enhances population diversity; adaptive directional search regulates the exploration–exploitation balance; stochastic perturbation-based information fusion improves the ability to escape local optima; and centroid-based adaptive boundary handling strengthens constraint adaptability. The performance of IPKO is evaluated on the CEC2017 benchmark suite (10, 30, 50, and 100 dimensions) and two real-world engineering problems. Experimental results show that IPKO achieves superior overall performance compared with eleven state-of-the-art algorithms, with statistical significance confirmed by the Friedman test and Holm’s post-hoc procedure. Ablation studies further verify the contribution of each strategy. In engineering applications such as cold chain logistics and dynamic multi-UAV cooperative path planning, the IPKO algorithm demonstrates superior solution quality, robustness, and constraint-handling capability compared with competing algorithms. These results demonstrate that IPKO is a robust and effective bio-inspired optimization approach for solving complex, high-dimensional constrained engineering problems.

This paper proposes a multi-strategy improved pied kingfisher optimizer (MSIPKO), a novel metaheuristic algorithm designed to address constrained optimization problems (COPs). COPs are widely encountered in engineering and industrial applications and are characterized by complex constraints that restrict the feasible solution space and often lead to multiple local optima. To enhance the performance of the original pied kingfisher optimizer (PKO), three strategies are incorporated: (i) a reverse differential crossover mechanism to improve global exploration and maintain population diversity; (ii) an enhanced diving-fishing operator to strengthen local exploitation; and (iii) an improved commensalism phase to enrich search directions and increase robustness. The performance of MSIPKO is evaluated on 12 benchmark functions from the IEEE Congress on Evolutionary Computation 2006 (CEC 2006) test suite and six classical engineering optimization problems. Experimental results demonstrate that MSIPKO outperforms several state-of-the-art algorithms in terms of optimization accuracy, convergence speed, and stability, particularly for high-dimensional, nonlinear, and multi-constrained problems. Moreover, MSIPKO achieves superior or comparable solutions with fewer function evaluations, indicating its high efficiency and adaptability. These results confirm that MSIPKO is a promising tool for solving complex real-world constrained optimization problems. Future work will focus on extending the proposed algorithm to multi-objective and large-scale optimization scenarios.