Hybrid Swarm Research Articles

Geographically consistent hybridization dynamics between the Black-crested and Tufted titmouse with evidence of hybrid zone expansion

Abstract We studied hybridization between the Black-crested and Tufted titmouse across two geographically distinct transects that differ in the timing of secondary contact by hundreds to thousands of years. We found that hybridization patterns correspond to localized hybrid swarms and that the titmouse hybrid zone is likely slowly expanding over time, a product of short post-natal dispersal distances coupled with weak or absent selection against admixture. We show the southern part of the hybrid zone located in Texas is four times wider than the northern region of hybridization in Oklahoma, which is likely due to geographic differences in hybrid zone age. Despite differences in width, most individuals in both transects are advanced-generation hybrids and backcrosses, suggesting geographically consistent hybridization dynamics. We documented a strong correlation between genotypes and plumage index, suggesting that hybridization has not yet resulted in the decoupling of plumage and genome-wide ancestry as observed in some other avian hybrid zones. Although our results suggest the ongoing expansion of the hybrid zone, the rate of expansion appears to be slow, on the scale of tens of meters a year, and it will likely take hundreds of thousands to millions of years before homogenization of the parental populations. While we did not find support for partial reproductive isolation in the hybrid zone itself, there is the possibility that ecological or sexual selection limits introgression into allopatric regions. Broadly, the results of our study highlight the value of multiple, geographically distant, transects across a hybrid zone for assessing the evolutionary dynamics of hybridizing lineages.

Population genetics and geographic origins of mallards harvested in northwestern Ohio.

The genetic composition of mallards in eastern North America has been changed by release of domestically-raised, game-farm mallards to supplement wild populations for hunting. We sampled 296 hatch-year mallards harvested in northwestern Ohio, October-December 2019. The aim was to determine their genetic ancestry and geographic origin to understand the geographic extent of game-farm mallard introgression into wild populations in more westward regions of North America. We used molecular analysis to detect that 35% of samples were pure wild mallard, 12% were early generation hybrids between wild and game-farm mallards (i.e., F1-F3), and the remaining 53% of samples were assigned as part of a hybrid swarm. Percentage of individuals in our study with some form of hybridization with game-farm mallard (65%) was greater than previously detected farther south in the mid-continent (~4%), but less than the Atlantic coast of North America (~ 92%). Stable isotope analysis using δ2Hf suggested that pure wild mallards originated from areas farther north and west than hybrid mallards. More specifically, 17% of all Ohio samples had δ2Hf consistent with more western origins in the prairies, parkland, or boreal regions of the mid-continent of North America, with 55%, 35%, and 10% of these being genetically wild, hybrid swarm, and F3, respectively. We conclude that continued game-farm introgression into wild mallards is not isolated to the eastern population of mallards in North America, and may be increasing and more widespread than previously detected. Mallards in our study had greater incidence of game-farm hybridization than other locales in the mid-continent but less than eastern North American regions suggesting further need to understand game-farm mallard genetic variation and movement across the continent.

The allopolyploid origin(s) and diversification of New Caledonian Grevillea (Proteaceae)

ABSTRACT The assembly of island plant communities is the result of a number of processes: immigration (dispersal), speciation, and extinction. Using four plastid genes and one low-copy nuclear gene, we investigated the origin of the New Caledonian Grevillea (Proteaceae), an otherwise largely Australian genus. In the combined plastid analysis, the species form two distinct clades, the exul (four species) and gillivrayi groups (six species), within group 3 of Grevillea. All New Caledonian Grevillea display two distinct copies of PHYA, one copy in group 3, and another in group 4. Previously published chromosome counts for G. meisneri and two new genome size estimates of G. gillivrayi and G. rubiginosa suggest that these plants are tetraploids. Altogether, the current data available suggest that New Caledonian Grevillea are allotetraploids, resulting from one or two hybridisation events between two or three distinct parents. A possible scenario is that modern Grevillea has descended from a hybrid swarm that formed 9–13 Mya between multiple immigrants (now extinct) that reached the island. Grevillea can be added to the list of island plant radiations with an early history of hybridisation and polyploidy. The relative importance, location and timing of these two mechanisms remain to be elucidated. The two groups of New Caledonian Grevillea may be easily distinguished by their inflorescences, flowers and fruits, with the gillivrayi group displaying greater ecological and morphological diversity. The seed characteristics of New Caledonian Grevillea are consistent with their pioneer behavior.

Ingenuity of Shannon entropy-based fractional order hybrid swarming strategy to solve optimal power flows

An important issue for researchers looking into the effectiveness of power distribution systems is the Optimal Reactive-Power Dispatch (ORPD) problem, which aims to reduce real power line losses. Numerous techniques have been created with the express purpose of improving upon the optimization method's performance in tuning operational variables and exploring it through the estimation of results. Using entropy evolution (EE) and fractional calculus (FC) concepts, the research presents a novel method that is implemented in the hybrid meta-heuristic computational paradigm of Moth Flame Optimization (MFO) and Particle Swarm Optimization (PSO) algorithms for the ORPD problem. These techniques were incorporated into the MFO-PSO algorithm to enhance its memory effect, robustness, and stability. This was done to address the MFO-PSO vulnerability. ORPD problem using the IEEE-57 bus and IEEE-118 bus standards are used to test the novel Entropy design MFO and Fractional-order PSO algorithms as (EMFO-FPSO). The superiority of the proposed EMFO-FPSO algorithm has been demonstrated through a comparative analysis study with well-known optimizer solvers from the literature.

Swarm Optimization of Fuzzy Systems for Mobile Robots with Remote Control

This paper is dedicated to the development and research of the advanced approach for optimization of fuzzy control systems (FCS) for mobile robots (MR) with remote control based on bioinspired swarm techniques. The proposed approach makes it possible to create effective intelligent control systems for MRs based on the principles of hierarchical multi-level control, remote IoT-based control, fuzzy logic control, and intelligent optimization of fuzzy control devices. The applied hybrid particle swarm optimization (PSO) techniques with elite strategy allow effectively optimizing various parameters of FCSs, finding the optimal solution to the problem, and, at the same time, have a higher convergence rate compared with the basic PSO algorithms. To evaluate the effectiveness of the obtained advanced approach based on hybrid swarm techniques, the optimization process of the FCS for the speed control of the multi-purpose caterpillar MR, which can move on inclined and vertical ferromagnetic surfaces, is carried out. The presented research results fully confirm the high efficiency of the proposed approach, as well as the expediency of its application for the optimization of fuzzy control systems for various remotely controlled mobile robots.

Forest health in the Anthropocene: the emergence of a novel tree disease is associated with poplar cultivation.

Plant domestication and movement are large contributors to the success of new diseases. The introduction of new host species can result in accelerated evolutionary changes in pathogens, affecting long-established coevolutionary dynamics. This has been observed in poplars where severe epidemics of pathogens that were innocuous in their natural pathosystems occurred following host domestication. The North American fungus Sphaerulina musiva is responsible for endemic leaf spots on Populus deltoides. We show that the expansion of poplar cultivation resulted in the emergence of a new lineage of this pathogen that causes stem infections on a new host, P. balsamifera. This suggests a host shift since this is not a known host. Genome analysis of this emerging lineage reveals a mosaic pattern with islands of diversity separated by fixed genome regions, which is consistent with a homoploid hybridization event between two individuals that produced a hybrid swarm. Genome regions of extreme divergence and low diversity are enriched in genes involved in host-pathogen interactions. The specialization of this emerging lineage to a new host and its clonal propagation represents a serious threat to poplars and could affect both natural and planted forests. This work provides a clear example of the changes created by the intensification of tree cultivation that facilitate the emergence of specialized pathogens, jeopardizing the natural equilibrium between hosts and pathogens. This article is part of the theme issue 'Infectious disease ecology and evolution in a changing world'.

A hybrid swarm intelligence feature selection approach based on time-varying transition parameter

Feature selection aims to reduce the dimensionality of a dataset by removing superfluous attributes. This paper proposes a hybrid approach for feature selection problem by combining particle swarm optimization (PSO), grey wolf optimization (GWO), and tournament selection (TS) mechanism. Particle swarm enhances the diversification at the beginning of the search mechanism, grey wolf enhances the intensification at the end of the search mechanism, while tournament selection maintains diversification not only at the beginning but also at the end of the search process to achieve local optima avoidance. A time-varying transition parameter and a random variable are used to select either particle swarm, grey wolf, or tournament selection techniques during search process. This paper proposes different variants of this approach based on S-shaped and V-shaped transfer functions (TFs) to convert continuous solutions to binaries. These variants are named hybrid tournament grey wolf particle swarm (HTGWPS), followed by S or V letter to indicate the TF type, and followed by the TF’s number. These variants were evaluated using nine high-dimensional datasets. The results revealed that HTGWPS-V1 outperformed other V’s variants, PSO, and GWO on 78% of the datasets based on maximum classification accuracy obtained by a minimal feature subset. Also, HTGWPS-V1 outperformed six well-known-metaheuristics on 67% of the datasets.

A new subspecies of Papilio saharae Oberthür, 1879 (Lepidoptera: Papilionidae) from Lampedusa, Italy

An investigation into the only representative of the genus Papilio on the island of Lampedusa, the largest island of the Pelagian group, was conducted as part of a broader Mediterranean-wide study on the machaon complex. Over a three-year period, adults, larvae and ova were collected for further research during field visits, while an in-house breeding programme involving wild-collected gravid females was initiated with a view to examine an adequately sized series of specimens. A total of 38 adults, including 23 male specimens, >150 ova, 233 larvae, and 220 pupae were examined. In addition to a thorough morphometric assessment, statistical tests were performed using one-way ANOVA and multivariate analysis. Results demonstrate morphological characters of P. saharae and, to a lesser degree, of P. machaon, suggesting that the taxon is plausibly the outcome of a hybrid swarm whose occurrence on the island was facilitated by Pleistocene lowstands (when the island was physically connected with the north African continental landmass, the taxon's centre of origin). Based on results, involving all four stages of metamorphosis, it is proposed to 'anchor' the taxon to Papilio saharae Oberthür, 1879 and assign it subspecific status, aferpilaggi ssp. nov.

Migration direction in a songbird explained by two loci

Migratory routes and remote wintering quarters in birds are often species and even population specific. It has been known for decades that songbirds mainly migrate solitarily, and that the migration direction is genetically controlled. Yet, the underlying genetic mechanisms remain unknown. To investigate the genetic basis of migration direction, we track genotyped willow warblers Phylloscopus trochilus from a migratory divide in Sweden, where South-West migrating, and South-East migrating subspecies form a hybrid swarm. We find evidence that migration direction follows a dominant inheritance pattern with epistatic interaction between two loci explaining 74% of variation. Consequently, most hybrids migrate similarly to one of the parental subspecies, and therefore do not suffer from the cost of following an inferior, intermediate route. This has significant implications for understanding the selection processes that maintain narrow migratory divides.

Swarm Intelligence Research: From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm Intelligence

Swarm intelligence has become a hot research field of artificial intelligence. Considering the importance of swarm intelligence for the future development of artificial intelligence, we discuss and analyze swarm intelligence from a broader and deeper perspective. In a broader sense, we are talking about not only bio-inspired swarm intelligence, but also human-machine hybrid swarm intelligence. In a deeper sense, we discuss the research using a three-layer hierarchy: in the first layer, we divide the research of swarm intelligence into bio-inspired swarm intelligence and human-machine hybrid swarm intelligence; in the second layer, the bio-inspired swarm intelligence is divided into single-population swarm intelligence and multi-population swarm intelligence; and in the third layer, we review single-population, multi-population and human-machine hybrid models from different perspectives. Single-population swarm intelligence is inspired by biological intelligence. To further solve complex optimization problems, researchers have made preliminary explorations in multi-population swarm intelligence. However, it is difficult for bio-inspired swarm intelligence to realize dynamic cognitive intelligent behavior that meets the needs of human cognition. Researchers have introduced human intelligence into computing systems and proposed human-machine hybrid swarm intelligence. In addition to single-population swarm intelligence, we thoroughly review multi-population and human-machine hybrid swarm intelligence in this paper. We also discuss the applications of swarm intelligence in optimization, big data analysis, unmanned systems and other fields. Finally, we discuss future research directions and key issues to be studied in swarm intelligence.

An efficient hybrid swarm intelligence optimization algorithm for solving nonlinear systems and clustering problems

An efficient hybrid swarm intelligence optimization algorithm for solving nonlinear systems and clustering problems

Optimization of Chemotherapy Using Hybrid Optimal Control and Swarm Intelligence

This study aimed to minimize the tumor cell population by using minimal medicine for chemotherapy treatment while maintaining the effector-immune cell population at a healthy threshold. Therefore, a mathematical model was developed in the form of ordinary differential equations (ODE), and the solution to Multi-Objective Optimal Control Problem (MOOCP) was obtained using Multi-Objective Optimization algorithms. In this study, the interaction of the tumor-effector cell population with chemotherapy was investigated using Pure MOOCP and Hybrid MOOCP methods. The handling of constraints and the Pontryagin Maximum Principle (PMP) differ among these methods. Swarm Intelligence (SI) and Evolutionary Algorithms (EA) were used to process the results of these methods. The numerical outcomes of SI and EA are displayed via Pareto Optimal Front. In addition, the solutions from these algorithms were further analyzed using the Hypervolume Indicator. The findings of this study demonstrate that the hybrid method outperforms Pure MOOCP via Multi-Objective Differential Evolution (MODE). The MODE produces a point on the Pareto Optimal Front with minimal distance to the origin, where the distance represents the best solution.

Software fault prediction using hybrid swarm intelligent cuckoo and bat-based k-means++ clustering technique

Software fault prediction using hybrid swarm intelligent cuckoo and bat-based k-means++ clustering technique

FineAP: Fine-Grained Access Point Deployment Strategy for 60 GHz Millimeter-Wave Wireless Networks

Traditional access point (AP) deployment is based on the principle of minimum area coverage, which takes the AP signal range as the radius and performs a geometric calculation to determine the deployment location. These methods lack support for various contexts and requirements, making them only useful in a conventional rectangular environment. As a result, we design a fine-grained AP deployment (FineAP) strategy, which first uses ray-tracing to determine the signal distribution of various locations in the environment, and then uses a hybrid swarm intelligence optimization algorithm based on the principle of signal matrix maximization to determine the AP deployment location. When compared to traditional deployment methods, FineAP can handle a variety of application scenarios with close to upbound performance and the least minimum of overhead.

Improved Hybrid Swarm Intelligence for Optimizing the Energy in WSN

In this current century, most industries are moving towards automation, where human intervention is dramatically reduced. This revolution leads to industrial revolution 4.0, which uses the Internet of Things (IoT) and wireless sensor networks (WSN). With its associated applications, this IoT device is used to compute the received WSN data from devices and transfer it to remote locations for assistance. In general, WSNs, the gateways are a long distance from the base station (BS) and are communicated through the gateways nearer to the BS. At the gateway, which is closer to the BS, energy drains faster because of the heavy load, which leads to energy issues around the BS. Since the sensors are battery-operated, either replacement or recharging of those sensor node batteries is not possible after it is deployed to their corresponding areas. In that situation, energy plays a vital role in sensor survival. Concerning reducing the network energy consumption and increasing the network lifetime, this paper proposed an efficient cluster head selection using Improved Social spider Optimization with a Rough Set (ISSRS) and routing path selection to reduce the network load using the Improved Grey wolf optimization (IGWO) approach. (i) Using ISSRS, the initial clusters are formed with the local nodes, and the cluster head is chosen. (ii) Load balancing through routing path selection using IGWO. The simulation results prove that the proposed optimization-based approaches efficiently reduce the energy through load balancing compared to existing systems in terms of energy efficiency, packet delivery ratio, network throughput, and packet loss percentage.

Hybrid Swarm Intelligence Optimization Methods for Low-Embodied Energy Steel-Concrete Composite Bridges

Bridge optimization is a significant challenge, given the huge number of possible configurations of the problem. Embodied energy and cost were taken as objective functions for a box-girder steel–concrete optimization problem considering both as single-objective. Embodied energy was chosen as a sustainable criterion to compare the results with cost. The stochastic global search TAMO algorithm, the swarm intelligence cuckoo search (CS), and sine cosine algorithms (SCA) were used to achieve this goal. To allow the SCA and SC techniques to solve the discrete bridge optimization problem, the discretization technique applying the k-means clustering technique was used. As a result, SC was found to produce objective energy function values comparable to TAMO while reducing the computation time by 25.79%. In addition, the cost optimization and embodied energy analysis revealed that each euro saved using metaheuristic methodologies decreased the energy consumption for this optimization problem by 0.584 kW·h. Additionally, by including cells in the upper and lower parts of the webs, the behavior of the section was improved, as were the optimization outcomes for the two optimization objectives. This study concludes that double composite action design on supports makes the continuous longitudinal stiffeners in the bottom flange unnecessary.

On fly hybrid swarm optimization algorithms for clustering of streaming data

Clustering is an important data analysis technique for extracting knowledge and hidden patterns in the data. Recently hybrid clustering algorithms have been proposed to solve the local optimum and poor robustness problem due to improper selection of initial cluster centroids in the traditional clustering algorithms. In hybrid algorithms, clustering is solved as an optimization problem by invoking optimization algorithms to find the optimal cluster centroid. Many hybrid clustering algorithms integrating different swarm algorithms and traditional clustering algorithms have been proposed. However, these algorithms were not designed for the on-fly clustering of streaming data. This is necessary for real-time streaming requirements in financial domains like stock markets. This work proposes an on-fly hybrid swarm optimization algorithm for the clustering of streaming data.

Retraction Note: Medical Data Security for Healthcare Applications Using Hybrid Lightweight Encryption and Swarm Optimization Algorithm

Retraction Note: Medical Data Security for Healthcare Applications Using Hybrid Lightweight Encryption and Swarm Optimization Algorithm

Indonesian pharmacy retailer segmentation using recency frequency monetary-location model and ant K-means algorithm

<span lang="EN-US">We proposed an approach of retailer segmentation using a hybrid swarm intelligence algorithm and recency frequency monetary (RFM)-location model to develop a tailored marketing strategy for a pharmacy industry distribution company. We used sales data and plug it into MATLAB to implement ant clustering algorithm and K-means, then the results were analyzed using RFM-location model to calculate each clusters’ customer lifetime value (CLV). The algorithm generated 13 clusters of retailers based on provided data with a total of 1,138 retailers. Then, using RFM-location, some clusters were combined due to identical characteristics, the final clusters amounted to 8 clusters with unique characteristics. The findings can inform the decision-making process of the company, especially in prioritizing retailer segments and developing a tailored marketing strategy. We used a hybrid algorithm by leveraging the advantage of swarm intelligence and the power of K-means to cluster the retailers, then we further added value to the generated clusters by analyzing it using RFM-location model and CLV. However, location as a variable may not be relevant in smaller countries or developed countries, because the shipping cost may not be a problem. </span>

Hybrid swarm intelligent algorithm for multi-UAV formation reconfiguration

Formation flight of unmanned aerial vehicles (UAVs) utilizes reconfiguration procedures to handle a variety of emergencies, such as collision avoidance, malfunctions, fuel savings, and member replacement. As UAVs have limited computing power and energy resources, it is necessary to optimize the control inputs to reduce the distance travelled by UAVs while reducing the computing costs during formation reconfiguration. In this paper, the problem of multi-UAV reconfiguration is decoupled into two stages: task assignment and control input optimization of UAVs. For a solution to the above problem, we propose an adaptive hybrid particle swarm optimization and differential evolution algorithm (AHPSODE) to optimize minimize the distance of the total movement and reduce the computing cost of formation reconfiguration. Based on the idea of receding horizon control (RHC) and the nonlinear model of multi-UAV formation reconfiguration, an RHC controller using AHPSODE is designed to optimize the control input of the UAV group to obtain the shortest movement distance, and this method can reduce the computation time. We use the CEC 2017 test suit to test the performance of our proposed AHPSODE algorithm, and simulate the AHPSODE-based RHC controller to manage formation reconfiguration. The results show that our proposed AHPSODE performed well in convergence and accuracy and the RHC controller is effective.