Artificial Fish Research Articles

Automatic-Detection Method for Mining Subsidence Basins Based on InSAR and CNN-AFSA-SVM

Mining subsidence disasters are common geological disasters. Accurate and effective identification of their deformation position is significant in preventing and controlling geological disasters and monitoring illegal mining. In this study, deep learning, combined with a support vector machine (SVM), has been used to establish an automatic-detection method for mining subsidence basins using Sentinel-1A data. The Huainan mining area was selected as the experimental area to verify the method. The interferogram was obtained using differential radar interferometry (D-InSAR) to process the Sentinel-1A radar data of seven landscapes, and the mining subsidence basin and other targets were extracted manually as training samples. Subsequently, AlexNet, VGG19, and ResNet50 convolutional neural networks (CNNs) were used to extract feature vectors of mining subsidence basins for the SVM classifier, and mining subsidence basins were detected in a large-area InSAR interferogram. Non-maximum suppression was used to remove the repeated search box to improve the detection accuracy of mining subsidence basins; the artificial fish swarm algorithm with strong optimization ability and good global convergence is introduced into SVM parameter optimization to construct an improved ResNet50_SVM model. The experimental results show that: (1) the three CNN_SVM methods can accurately detect dry-mining subsidence basins automatically in large regional interference maps, providing an essential scientific basis for the government to monitor illegal mining activities and prevent and control geological disasters in mining areas; (2) the accuracy of the CNN_SVM automatic-detection methods for mining subsidence basins is approximately 80%, and that of ResNet50_SVM for mining subsidence basin detection is 83.7%, superior to that of AlexNet_SVM and VGG19_SVM; (3) the accuracy of the improved ResNet50_SVM based on AFSA algorithm is 88.3%, which is better than the unimproved Resnet50_SVM model.

Hybrid Whale-fish swarm algorithm to optimize disassembly line balancing problem

The disassembly of waste products positively affects the environment, reduces the risk of environmental pollution, reduces the risk of spreading dangerous parts to the life of living entities, and provides the continuity of life on this planet.This research aims to contribute to the aforementioned of maintaining a clean environment by developing algorithms that help solve the problems of dismantling waste products and polluting the environment. In previous literature, researchers developed algorithms for artificial fish, improved artificial fish, and artificial whales to search for solutions with higher evidence instead From the local evidence, in this paper the artificial fish algorithm has been hybridized with the artificial whale algorithm for the purpose of developing solutions, and this method depends on the artificial fish flock first searching for food and here representing global solutions and avoiding predating traps (hooks with trap food), and after finding a flock The artificial swarm is the real food. The artificial whale sends bubbles to collect the swarm of artificial fish in the place of global high solutions, and gets them at once. The proposed algorithm was compared with the algorithms in the previous literature, and the results were as follows:Workstation was decreased by 0.5\% to 22.2\%, idle time was reduced by 82.5\% to 84.0\%, the demand index was reduced by 15\% to 24.2\%, and the hazardous index was reduced by 1.7\% to 3.4\%.

Research on the End Effector and Optimal Motion Control Strategy for a Plug Seedling Transplanting Parallel Robot

Due to the phenomenon of holes and inferior seedlings in trays, it is necessary to remove and replenish unqualified seedlings. The traditional operation is labor-intensive, and the degree of mechanization is low. This paper took broccoli seedlings as the research object and developed an image recognition system suitable for seedling health recognition and pose judgement, researched and designed a plug-in end effector that reduces leaf damage, and conducted orthogonal tests to obtain a substrate parameter combination containing the moisture content, seedling age, and transplanting acceleration suitable for culling operations. A parallel robot kinematics and dynamics model was built. The fifth degree B-spline curve was used to construct the joint space motion curve for seven nodes, and the motor speed, torque, and end-effector acceleration were used to construct the joint space motion curves. The end-effector acceleration was the constraint condition to plan the optimal trajectory of the joint space in time, and the optimal time was obtained using the artificial fish swarm–particle swarm hybrid optimization algorithm. A single operation time was greatly reduced; the whole machine was systematically built; the average time of single-time seedling removal was measured; and the transplanting efficiency of the whole machine was high. In the seedling damage rate gap test, the leaf damage rate was low. This research provides a reference for the localized development of greenhouse high-speed and low-loss seedling removal equipment.

An improved fish swarm algorithm to assign tasks and cut down on latency in cloud computing

Various researches have been conducted to discover the machinery that led to the evolvement of non-symmetric formation of groups by uncountable marine animals. The huge of tasks comes per unit of time brought obstacles to assign each to particular server, while task assignment have needed a fast strategy to make decision. Artificial fish affect the environment through their behavior and the behavior of their peers. Creating a synthetic fish model has two parts: variables and functions which could be used for task assignment. This paper present improved fish swarm algorithm (IFSA) for task assignment to reduce the latency in cloud computing that could achieve one green computing goals. The research trying to reduce the pending job numbers compared with exist research.

Design and Implementation of Group Animation Based on Multimedia Intelligent Algorithm

In order to improve the autonomy and intelligence of group animation behavior, this paper proposes a particle swarm optimization algorithm for automatic control of group animation behavior. After the user inputs the group animation behavior construction target, the individual path is planned according to the fixed behavior rules, and the particle swarm optimization algorithm and the improved artificial fish swarm optimization algorithm are used to select the optimal action for the individual in the group animation according to the top-down control process. The experimental results show that after the PSO converges to the global optimal solution, the evolution times of the PSO converge to the global optimal solution again when the surrounding environment changes 10 times. The population diversity of particle swarm optimization algorithm is obviously better than those of the other two algorithms in the process of automatically controlling the movement of individuals to the circle and to the stand in the crowd animation. Compared with the original algorithm AFSA, the improved artificial fish swarm algorithm proposed in this paper is easier to obtain the global optimal value, and the optimization speed is also improved. In conclusion, the example analysis verifies that the algorithm can effectively realize the automatic control of group animation behavior and has high convergence speed and high autonomy.

A Novel Oppositional Artificial Fish Swarm based clustering with improved moth flame optimization based Routing Protocol for Wireless Sensor Networks

A Novel Oppositional Artificial Fish Swarm based clustering with improved moth flame optimization based Routing Protocol for Wireless Sensor Networks

Optimization of Rolling Schedule for Single-Stand Reversible Cold Rolling Mill Based on Multiobjective Artificial Fish Swarm Algorithm

The single-stand reversible cold rolling mill is important equipment in the production of steel strips. The rolling schedule is the core technological content in the strip production of the single-stand reversible cold rolling mill. The scientific rolling schedule is the fundamental guarantee for the production capacity of the rolling mill, product quality, accuracy, shape quality, energy saving, and consumption reduction. This paper takes the dynamic rolling process of single-stand cold rolling as the research object, the purposes of increasing production capacity, saving energy, and reducing consumption are achieved by optimizing the rolling schedule. Based on the study of the mechanism model and the analysis of a large number of field measured data, a slice of mathematical models of the rolling process suitable for engineering calculation are proposed, and a few objective functions suitable for the single-stand reversible cold rolling process are designed. On this basis, the artificial fish swarm algorithm is improved into a multiobjective optimization algorithm for the optimization of rolling schedule, and the optimal rolling load distribution scheme is obtained. Finally, the optimization method of rolling schedule proposed in this paper is applied to the actual rolling production. The results show that the proposed method can improve productivity and save energy compared with the empirical rolling schedule, and the feasibility and validity of the proposed algorithm are verified.

A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple tea fields.

The complex environments and weak infrastructure constructions of hilly mountainous areas complicate the effective path planning for plant protection operations. Therefore, with the aim of improving the current status of complicated tea plant protections in hills and slopes, an unmanned aerial vehicle (UAV) multi-tea field plant protection route planning algorithm is developed in this paper and integrated with a full-coverage spraying route method for a single region. By optimizing the crossover and mutation operators of the genetic algorithm (GA), the crossover and mutation probabilities are automatically adjusted with the individual fitness and a dynamic genetic algorithm (DGA) is proposed. The iteration period and reinforcement concepts are then introduced in the pheromone update rule of the ant colony optimization (ACO) to improve the convergence accuracy and global optimization capability, and an ant colony binary iteration optimization (ACBIO) is proposed. Serial fusion is subsequently employed on the two algorithms to optimize the route planning for multi-regional operations. Simulation tests reveal that the dynamic genetic algorithm with ant colony binary iterative optimization (DGA-ACBIO) proposed in this study shortens the optimal flight range by 715.8 m, 428.3 m, 589 m, and 287.6 m compared to the dynamic genetic algorithm, ant colony binary iterative algorithm, artificial fish swarm algorithm (AFSA) and particle swarm optimization (PSO), respectively, for multiple tea field scheduling route planning. Moreover, the search time is reduced by more than half compared to other bionic algorithms. The proposed algorithm maintains advantages in performance and stability when solving standard traveling salesman problems with more complex objectives, as well as the planning accuracy and search speed. In this paper, the research on the planning algorithm of plant protection route for multi-tea field scheduling helps to shorten the inter-regional scheduling range and thus reduces the cost of plant protection.

Local and overseas carp pituitary gland in the induced breeding of Cirrhinus mrigala (Hamilton 1822)

Stimulating hormone for artificial fish breeding in hatchery industry is very crucial for hatching operation, healthy fry and fingerling production, while overall fish production of a country. The present study was conducted to assess the effectiveness of using local and overseas Carp Pituitary Gland (CPG) in the induced breeding of Cirrhinus mrigala at Jashore district from April to May 2022. The first dose of hormone, both for male and female brood fishes were injected with local and overseas CPG at 0.5mg/kg body weight. After six hours of the first dose, only female brood fishes were injected with the second dose of PG at 5 mg/kg body weight. After 6 hours of second dose CPG injection of local and overseas PGs, brood fishes started spawning. The ovulation rates for both local and overseas PGs were estimated 100%. The fertilization rates were 93.5 % and 92.6 % for local and overseas PGs; while the hatching rates were 91.2% and 89.5%, respectively. However, local CPG is highly recommended to hatchery owners considering the rate of fertilization, hatching rate and availability. Asian J. Med. Biol. Res. 2022, 8 (3), 146-153

A Classification Technique for English Teaching Resources and Merging Using Swarm Intelligence Algorithm

The evolution of social education has necessitated the optimization of various teaching approaches, and the classification of English teaching resources is one of the crucial factors. With the development of electronic computers and Big Data technologies, the classification of teaching resources that could not be realized before has become possible now. However, the traditional classification methods cannot meet the requirements of modern computing due to the limitations of implementation. The emergence of swarm intelligence algorithms makes the classification of teaching resources possible. Swarm intelligence algorithm is a swarm-based multipoint random search algorithm, which includes evolutionary algorithm, immune algorithm, particle swarm procedure (PSO), ant colony process, artificial fish swarm mechanism, and other typical intelligent techniques. The swarm intelligence algorithm has strong robustness and strong global and local search capabilities, as well as, implicit parallelism. Furthermore, it has no special requirements for objective functions and constraint functions. It has the function of “black box” and can overcome problems where traditional optimization methods are insufficient. The swarm intelligence algorithm has a large space for development and rich forms of expression, and there is an essential connection between them, so that they can be well integrated. The key goal of this study is to implement the swarm intelligence algorithm to the task of classifying English teaching resources and to provide a reference for optimizing the English teaching model. The experimental findings demonstrate that the suggested classification model for English teaching resources has excellent performance, is favorable to enhancing the utilization rate of teaching resources, and is applicable to other disciplines.

Path planning for autonomous surface vessels based on improved artificial fish swarm algorithm: a further study

ABSTRACT Featuring agile controllability, high-level autonomy, and powerful field operational advantages, autonomous surface vessels (ASVs) are gaining increasing attention worldwide. Central to the control of ASVs, path planning is one of the key technologies that ensure the navigation safety. However, many of the previous works to solve the path planning problem are to find a collision-free and shortest path, but the solutions are not satisfied by the safety requirements and the non-holonomic constraints of ASVs. This paper expands on our previous work on the improved artificial fish algorithm (IAFSA) to address these challenges. The expanding technique is introduced to modify the grid cost, and a B-spline-based path smoother is applied to the algorithm to enhance its feasibility in cooperating with the ASV controller. Simulation experiments have been conducted to illustrate the effectiveness of the proposed method. Moreover, the new algorithm is tested in the USV control system model in a practical environment. The simulation results have demonstrated that the proposed method is more efficient than the other state-of-the-art algorithms, and the simulation with the ASV model illustrated its excellent performance cooperating with the control system. Therefore, the proposed method can be considered as a reliable algorithm to solve the path planning problem of ASVs.

Stable isotope and fatty acid analysis reveal the ability of sea cucumbers to use fish farm waste in integrated multi-trophic aquaculture

Stable isotope ratios, carbon (δ13C) and nitrogen (δ15N), and fatty acids validated the trophic connection between farmed fish in a commercial nearshore fish farm and sea cucumbers in the Mediterranean Sea. This dual tracer approach evaluated organic matter transfer in integrated multi-trophic aquaculture (IMTA) and the ability of sea cucumbers to incorporate fish farm waste (fish faeces and uneaten artificial fish feed) into their tissue. Between October 2018 and September 2019, Holothuria (Roweothuria) poli Delle Chiaje, 1824, co-cultured at IMTA sites directly below one of the commercial fish cage , at 10m and 25m from the selected fish cage, and at two reference sites over 800m from the fish farm. Sea cucumbers were sampled from each site in February, May and September, except at 0m due to mass mortalities recorded here in the first month of study. Isotopic mixing models revealed that fish farm organic waste was the dominant dietary source for H. poli in IMTA at 10m and 25m from the cage. The contribution of marine plant-derived organic matter, Posidonia oceanica leaves and rhizomes, was least important. The isotopic signatures of sea cucumber tissues at reference sites were not explained by the sampled food resources. Importantly, fatty acid profiling revealed a high abundance of individual terrestrial plant fatty acids, such as oleic (18:1n-9), linoleic (18:2n-6) and eicosenoic (20:1n-9) acids in sea cucumber tissue at 10m and 25m from the fish cage, presumably linked to the terrestrial plant oil content of the fish feeds. At the reference sites, sea cucumber tissues were characterised by higher relative abundance of arachidonic acid (20:4n-6) acid, and the natural marine-based eicosapentaenoic (20:5n-3) and docosahexaenoic (22:6n-3) acids. These analyses revealed important differences in the composition of H. poli between the IMTA and reference locations, driven by aquaculture-derived waste near fish cages. Moreover, this study revealed temporal variation in food availability and quality, and possible differences in the physiological responses of H. poli. Stable isotope analysis and fatty acid profiling provided complementary evidence for the important dietary preferences of H. poli and validated the potential of sea cucumbers to uptake aquaculture organic waste as part of inshore fish-sea cucumber IMTA. It reveals the important implications that an established trophic link has on the viability of using sea cucumbers for the development of IMTA and the sustainable expansion of aquaculture.

Development of Sound-Fish Aggregating Devices (S-FAD) Applied at Lift Net

Fish produced sound from internal organs and air bubbles caused by the friction of fish bodies with the surrounding water. The sounds are used to interact with each other, and the sound produced is stronger when gathered during eating. The study aimed to develop a sound-fish aggregating device (S-FAD) and produce an artificial fish sound with suitable frequency for several fish species. The study used analytical descriptive and experimental fishing, which divided into two steps, (1) S-FAD tool construction using a descriptive method to explain every step in the construction of tool and consideration in the use of supporting tools, and (2) effectiveness testing step using an experimental fishing method to see the horde pattern (behavior) and the target strength using an echosounder. The sound wave aids trial was carried out on a lift net from morning to noon. Data retrieval by recording the fish-finder screen was carried out for 1 minute before and after the sound wave device was put into the water. The S-FAD test was done 60 times and hauling lift net in every multiple of 4 trials. The results showed that the average fish that approached and came together before and after the S-FAD installation was 2.18 ± 0.98 and 2.79 ± 0.71 fish. The highest number of caught fish when hauling at lift net with four times repetition was 79 fish from 7 types, including Selaroides leptolepis, Stolephorus sp., Sphyraenidae sp., Scatophagus argus, Mugil sp., Portunus pelagicus, and Loligo sp.

An adaptive immune‐following algorithm for intelligent optimal schedule of multiregional agricultural machinery

Aiming at low efficiency of agricultural machinery scheduling, this paper proposes an adaptive immune-following algorithm (AIFA) based on immune algorithm and artificial fish swarm algorithm. The adaptive crossover operator is used to accelerate convergence, and adaptive mutation operator ensures good diversity of population. After the adaptive evolution operations are performed, the following operator based on the following behavior of artificial fish swarm algorithm is embedded into the algorithm, which improves the convergence precision and obtains the promising optimization results. Experiments on scheduling considering the breakdown of agricultural machinery are performed based on multiple regions and multiple agricultural machineries. Compared with the immune algorithm and genetic algorithm, the simulation results demonstrate that AIFA can converge faster and achieve a better optimal solution.

Compensation of wavefront aberration using oppositional-breeding artificial fish swarm algorithm in free space optical communication

Compensation of wavefront aberration using oppositional-breeding artificial fish swarm algorithm in free space optical communication

A cooperative mobility model for multiple autonomous vehicles

A mobility model is a basis of constructing the simulation environment for vehicular ad hoc network (VANET) research. Most existing models mainly focus on the geographical movement of individual mobile communication devices. However, few works focus on the cooperative movement of multiple autonomous vehicles. In this paper, we propose a cooperative mobility model for multiple autonomous vehicles, making vehicles run as a swarm in an orderly manner. Specifically, inspired by artificial fish swarm algorithms, we draw on the cooperative behaviors of the fish swarm to model the collaboration and self-organization in multi-vehicle formation. Then we design several force functions to express the interactions between vehicles and the influence of the driving environment based on the artificial potential field. Under Newtonian dynamics, the proposed mobility model determines the coordinated movement of multiple autonomous vehicles by force functions. Furthermore, we introduce a parallel orientation area in the interaction area division to improve vehicle stability. Following existing works, we assume that the road is straight and of infinite length. This is, the considered environment is suitable for intersection-free double-lane roads. To comprehensively verify the effectiveness of our proposed approach, we conduct extensive simulations under different traffic scenarios. Simulation results confirm that using our mobility model, multiple vehicles are able to keep driving in the center of the lane at the allowed speed limit, form an ordered collision-free motorcade, and collaboratively avoid collisions with obstacles. Particularly, our proposed mobility model has better stability.

Improved particle swarm optimization algorithms for aerodynamic shape optimization of high-speed train

In this paper, improved particle swarm optimization algorithms are presented for improving the aerodynamic optimization efficiency of a high-speed train head shape. A hybrid particle swarm optimization algorithm, which employs an artificial fish swarm algorithm (AFSA) and a backward learning strategy in particle swarm optimization, is first proposed for constructing an optimal least squares support vector regression (OPT-LSSVR) model. The prediction accuracy of various surrogate models was evaluated, and the results indicate that the OPT-LSSVR model has the smallest prediction errors, where the prediction error for the total aerodynamic drag coefficient is reduced to about 0.45% and reduced to within 5% for the lift coefficient. Besides, an elite-evolved multi-objective particle swarm optimizer (EMPSO), which employs a grouping-based stochastic elite competition mechanism and elite gathering behavior, is proposed to improve the multi-objective optimization efficiency. The verified results indicate that the EMPSO algorithm is more efficient and capable of tackling complex multi-objective problems. Based on the OPT-LSSVR model and the EMPSO algorithm, the aerodynamic shape optimization of the high-speed train is performed. After optimization, the aerodynamic drag coefficient and the aerodynamic lift coefficient are reduced by 3.63% and 10.59%, respectively. Improved algorithms are simple yet efficient, and they have significant implications for the research and design of high-speed trains at higher speeds.

Differences in the genetic structure between and within two landlocked Ayu groups with different migration patterns in Lake Biwa revealed by environmental DNA analysis

AbstractAyu (Plecoglossus altivelis) is largely an annual amphidromous fish, although a landlocked population lives in Lake Biwa, the largest lake in Japan. The landlocked population comprises two migrant groups, spring migrants and autumn migrants, which run to inlet rivers from the lake at different seasons. We used environmental DNA (eDNA) analysis, which is reported to be more sensitive and cost‐effective than capture surveys, to clarify the genetic structure of this landlocked Ayu population with different migration patterns in Lake Biwa. We took water samples in 11 inlet rivers in the spring and autumn for 2 years in a row and quantitatively detected a total of 265 haplotypes of the mitochondrial D‐loop region. The pairwise fixation index (FST) value and haplotype diversity indicated that there were genetic differences between the two migrant groups in their respective rivers, and the FST values were negatively related to latitude and the presence of artificial fish stocking. Additionally, isolation by distance within spring migrant group was observed when the lake was divided into the east and west sides. These findings show that the landlocked Ayu population in Lake Biwa has genetic structure associated with migration patterns and geographical distance. This study demonstrates that the eDNA approach will be effective for conducting a large‐scale investigation of genetic structure beyond simple presence/absence tests.

Data-driven estimation for permeability of simplex pore-throat reservoirs via an improved light gradient boosting machine: A demonstration of sand-mud profile, Ordos Basin, northern China

Based on the theory of logging interpretation, permeability of the sand-mud reservoirs routinely can be viewed as a comprehensive response of some reservoir characters including porosity, clay content, median grain diameter, etc., and since each of these characters can be directly revealed by different well log, there then exists a multivariable fitting relationship between permeability and logging sequences. Light gradient boosting machine (LightGBM), a kind of ensemble learning, is demonstrated as one state-of-the-art solver for a variety of regression issue, and therefore can be regarded as a potential candidate to address the fitting of permeability via some logs. However, for this model, as the embedded exclusive feature bundling (EFB) algorithm appears incompatibly to logging data and meanwhile a great deal of hyper-parameters are employed during the training, a raw usage of LightGBM seems to be ineffective for a logging-based permeability regression. Thus, continuous restricted Boltzmann machine (CRBM) and artificial fish swarm algorithm (AFSA) are adopted as assistants to enhance the predicting capability of LightGBM, which will respectively deal with the transition of input logs and the optimization of hyper-parameters. Accordingly, a new hybrid predictor for the permeability fitting is proposed and named as CRBM-AFSA-LightGBM. To further verify the working performance, robustness, and generalization of the new predictor, the dataset collected from the tight sandstone reservoirs of member of Chang 4 + 5, Ordos Basin, Northern China, is applied to implement a validation. Simultaneously, to highlight the validating effect, two sophisticated fitting models, support vector regression (SVR) and extreme gradient boosting (XGBoost), are introduced as competitors. To fairly conduct the designed competitions, the integration of CRBM and AFSA also will be embedded in SVR and XGBoost, and then the real forms of two competitors are CRBM-AFSA-SVR and CRBM-AFSA-XGBoost. Given a comprehensive analysis of the gained experimental results, four points are summarized as the critical conclusion: 1) compared to SVR-cored predictor, the closer permeability results fitted by XGBoost-cored and LightGBM-cored predictors are proved more reliable, but the latter hybrid model only averagely spends 1/23 shorter times on the regression, hence argued as a higher-efficient fitting solver for the permeability of sand-mud reservoirs; 2) training more learning samples is testified effectively to raise an enhancement on the predicting capability of LightGBM-cored predictor, and then the application of a larger-volumetric learning dataset is encouraged during the training; 3) LightGBM-cored predictor will perform worse under the employment of a learning dataset derived from different study zone, thereby presenting an undesirable generalization, while such unsatisfactory predicting circumstance can be improved if more samples are learned during the establishment of input-output mapping; 4) since LightGBM-cored predictor has the capability to figure out the reasonable permeability outcomes even utilizing a sparse learning dataset, a strong robust nature is demonstrated for CRBM-AFSA-LightGBM.

Using biomimicry and bibliometric mapping to guide design and production of artificial coral reefs

Worldwide, artificial reefs are being installed to simultaneously attract recreational divers and protect deteriorating natural reefs. This study uses a bibliometric review of artificial coral reefs to identify five clusters as gate criteria for artificial reef design. These clusters enable the conceptualization and testing of artificial reefs for optimum integration of sociotechnical requirements, biological integrity, and ecological marine health. The five clusters are: (1) applications, solutions, and performance; (2) management, technology, and science; (3) calcification, biomineralization, chemistry, and ocean acidification; (4) coral species survival, mortality, and photosynthesis; and (5) artificial reef development, and coral and fish recruitment. The six biomimicry design stages are: define, biologize, discover, abstract, emulate, and evaluate. The 3D printing and hard corals design attracted a large number of planula larvae and different inhabitant corals, and a high species diversity in the surrounding waters. Practical implications include biomimicry-based means for coral reef restoration and recreational ecosystem services.