Intelligent Fish Behavior Classification Using Modified Invasive Weed Optimization with Ensemble Fusion Model

Accurate and rapid detection of fish behaviors is critical to perceive health and welfare by allowing farmers to make informed management deci-sions about recirculating the aquaculture system while decreasing labor.The classic detection approach involves placing sensors on the skin or body of the fish,which may interfere with typical behavior and welfare.The progress of deep learning and computer vision technologies opens up new opportunities to understand the biological basis of this behavior and precisely quantify behaviors that contribute to achieving accurate management in precision farming and higher production efficacy.This study develops an intelligent fish behavior classification using modified invasive weed optimization with an ensemble fusion(IFBC-MIWOEF)model.The presented IFBC-MIWOEF model focuses on identifying the distinct kinds of fish behavior classification.To accomplish this,the IFBC-MIWOEF model designs an ensemble of Deep Learning(DL)based fusion models such as VGG-19,DenseNet,and Effi-cientNet models for fish behavior classification.In addition,the hyperparam-eter tuning of the DL models is carried out using the MIWO algorithm,which is derived from the concepts of oppositional-based learning(OBL)and the IWO algorithm.Finally,the softmax(SM)layer at the end of the DL model categorizes the input into distinct fish behavior classes.The experimental validation of the IFBC-MIWOEF model is tested using fish videos,and the results are examined under distinct aspects.An Extensive comparative study pointed out the improved outcomes of the IFBC-MIWOEF model over recent approaches.

Control Effect of Several Replacement Plants on Invasive Weed Eupatorium adenophorum Spreng

To select the suitable plant species controlling Eupatorium adenophorum in hilly area, ecological adaptability, competitiveness and control efficacy of many replacement plants with economic value in Guizhou Province of China were studied. The results showed that the coverage of paspalum wetsfeteini and Dolichos lablab were 100% and 93%, which increased by 3.70 and 3.44 times compared with Lolium perenne, respectively; their relative crowding coefficient with E. adenophorum were 7.09 and 22.78, which increased by 2.43 and 7.80 times compared with L perenroe, respectively. Using excavation method, the control efficacies of replacement plants were 99.3 % and 96.9%, respectively, while the control efficacy of replacement plants using mowing method was lower than 66.4%. The overwintering rate of P. wetsfeteini in the following year was 95% ; its coverage was still 100% and its control efficacy against E. adenophorum remained over 99%. D. lablab was difficult to survive the winter in the north region beyond 26.2°N, so it could only be applied as annual replacement plant. The coverage of Setaria anceps, Cajanus cajan and other test plants were less than 90%, with poor control efficacy against E. adenophorum.

Invasive Weed Mapping of Lebanon

Surveying and early detection of invasive weeds are essential for strategic management and monitoring. Accordingly, a weed mapping was conducted during July 2011, against native （Orobanche ramosa, Cuscuta spp., Sorghum halepense and Xanthium strumarium） and non native （Abutilon theophrasti, Datura stramonium, Solanum elaeagnifolium and Verbesina encelioide） weeds of Lebanon. A global positioning system （Garmin 2006） was used for precise waypoint, elevation, navigation and distance. The result of interviewing and interacting with the residents in 95 villages distributed between the Beq＇aa and the North governorates of Lebanon, along with the observations made on the route, yielded the first detection of Abutilon theophrasti in both governorates. Solanum elaeagnifolium and Verbesina encelioide were not found in the agro-ecosystems of either governorates. This is the first report of the introduction ofAbutilon theophrasti in Lebanon and the establishment of a baseline data on weeds of Lebanon. The adoption of an integrated weed management program with a quarantine and control techniques and methods is needed to manage the spreading of weeds and to lessen their ability to adapt to a constantly changing system which uses several control practices.

Invasive weed disrupts facilitation of nutrient uptake in grass-clover assemblage

Nutrient constraints in low-fertility soil were modified by different species combinations.Grass-clover assemblages benefited both species in terms of nutrient procurement.Interplay of competition and facilitation is demonstrated.An invasive weed removed essential nutrients from the grazing cycle.To investigate the interplay of competition and facilitation between plants in low-fertility pasture grasslands of New Zealand,we compared nutrient uptake and acquisition of key nutrients of three species from different func-tional groups.Combinations of Pilosella officinarum(mouse-eared hawk-weed,an invasive weed),Trifolium repens(white clover,a nitrogen fixer)and Dactylis glomerata(cocksfoot,a pasture grass)were planted into a soil with low-to-deficient concentrations of key nutrients.Highest yields were achieved by the grass growing alone but,when the clover and grass had grown together,there were complementary benefits in terms of procurement of a wide range of nutrients from soil despite lower root biomass.The inva-sive weed negated these benefits,and soil nutrients were exploited less efficiently when Pilosella had grown alone or in a mixture with the other species.Competition from the weed removed the benefits of grass-legume coexistence.These findings are interpreted to suggest that requirements for legumes to be the main source of nitrogen in pasture grasslands may be compromised unless competitive weeds are controlled to avoid disrupted procurement of key nutrients.It is likely these constraints to nutrient procurement would similarly impact conservation grasslands.

A Novel Hardware/Software Partitioning Method Based on Position Disturbed Particle Swarm Optimization with Invasive Weed Optimization

With the development of the design complexity in embedded systems, hardware/software （HW/SW） partitioning becomes a challenging optimization problem in HW/SW co-design. A novel HW/SW partitioning method based on position disturbed particle swarm optimization with invasive weed optimization （PDPSO-IWO） is presented in this paper. It is found by biologists that the ground squirrels produce alarm calls which warn their peers to move away when there is potential predatory threat. Here, we present PDPSO algorithm, in each iteration of which the squirrel behavior of escaping from the global worst particle can be simulated to increase population diversity and avoid local optimum. We also present new initialization and reproduction strategies to improve IWO algorithm for searching a better position, with which the global best position can be updated. Then the search accuracy and the solution quality can be enhanced. PDPSO and improved IWO are synthesized into one single PDPSO-IWO algorithm, which can keep both searching diversification and searching intensification. Furthermore, a hybrid NodeRank （HNodeRank） algorithm is proposed to initialize the population of PDPSO-IWO, and the solution quality can be enhanced further. Since the HW/SW communication cost computing is the most time-consuming process for HW/SW partitioning algorithm, we adopt the GPU parallel technique to accelerate the computing. In this way, the runtime of PDPSO-IWO for large-scale HW/SW partitioning problem can be reduced efficiently. Finally, multiple experiments on benchmarks from state-of-the-art publications and large-scale HW/SW partitioning demonstrate that the proposed algorithm can achieve higher performance than other algorithms.

A new hybrid aerodynamic optimization framework based on differential evolution and invasive weed optimization

Since many aerodynamic optimization problems in the area of aeronautics contain highly nonlinear objectives and multiple local optima, it is still a challenge for most of the traditional optimization methods to find the global optima. In this paper, a new hybrid optimization framework based on Differential Evolution and Invasive Weed Optimization（IWO_DE/Ring） is developed, which combines global and local search to improve the performance, where a Multiple-Output Gaussian Process（MOGP） is used as the surrogate model. We first use several test functions to verify the performance of the IWO_DE/Ring method, and then apply the optimization framework to a supercritical airfoil design problem. The convergence and the robustness of the proposed framework are compared against some other optimization methods. The IWO_DE/Ringbased approach provides much quicker and steadier convergence than the traditional methods.The results show that the stability of the dynamic optimization process is an important indication of the confidence in the obtained optimum, and the proposed optimization framework based on IWO_DE/Ring is a reliable and promising alternative for complex aeronautical optimization problems.

Combined heat and power economic dispatch problem using the invasive weed optimization algorithm

Cogeneration units which produce both heat and electric power are found in many process industries. These industries also consume heat directly in addition to electricity. The cogeneration units operate only within a feasible zone. Each point within the feasible zone consists of a specific value of heat and electric power. These units are used along with other units which produce either heat or power exclusively. Hence the economic dispatch problem for these plants optimizing the fuel cost is quite complex and several classical and meta-heuristic algo- rithms have been proposed earlier. This paper applies the invasive weed optimization algorithm which is inspired by the ecological process of weed colonization and distribu- tion. The results obtained have been compared with those obtained by other methods earlier and showed a marked improvement over earlier ones.

A solution to unit commitment problem using invasive weed optimization algorithm

Unit commitment （UC） is one of the most important aspect of power generation in the world today. Though, there is no method to find the exact optimized solution, there exists several meta-heuristic algorithms to determine the close to exact solution. This paper proposes a novel solution to effectively determine UC and generation cost using the technique of invasive weed optimization （IWO）. The existing technique distributes the load demand among all the generating units. The method proposed here utilizes the output of UC obtained by using the Lagrangian relaxation （LR） method and calculates the required generation from only the plants that are ON discarding the OFF generator units and thereby giving a faster and more accurate response. Moreover, the results show the comparison between the LR-particle swarm optimization （PSO） and LR-IWO, and prove that the cost of generation for a 4 unit, 8 hour schedule is much less in the case of IWO when compared to PSO.

Application of the invasive weed optimization algorithm to economic dispatch problems

In this paper the invasive weed optimization algorithm has been applied to a variety of economic dispatch （ED） problems. The ED problem is concerned with minimizing the fuel cost by optimally loading the electrical generators which are committed to supply a given demand. Some involve prohibited operating zones, transmission losses and valve point loading. In general, they are non-linear non-convex optimization problems which cannot be directly solved by conventional methods. In this work the invasive weed algorithm, a meta-heuristic method inspired by the proliferation of weeds, has been applied to four numerical examples and has resulted in promising solutions compared to published results.

Modeling and Algorithm Application of Weapon Assignment System

In order to improve weapon assignment(WA)accuracy in real scenario,an artificial neural network(ANN)model is built to calculate real-time weapon kill probabilities.Considering the WA characteristic,each input representing one assessment index should be normalized properly.Therefore,the modified WA model is oriented from constant value to dynamic computation.Then an improved invasive weed optimization algorithm is applied to solve the WA problem.During search process,local search is used to improve the initial population,and seed reproduction is redefined to guarantee the mutation from multipoint to single point.In addition,the idea of vaccination and immune selection in biology is added into optimization process.Finally,simulation results verify the model′s rationality and effectiveness of the proposed algorithm.

Bio-inspired optimization algorithms for optical parameter extraction of dielectric materials: A comparative study

Particle swarm optimization(PSO) and invasive weed optimization(IWO) algorithms are used for extracting the modeling parameters of materials useful for optics and photonics research community. These two bio-inspired algorithms are used here for the first time in this particular field to the best of our knowledge. The algorithms are used for modeling graphene oxide and the performances of the two are compared. Two objective functions are used for different boundary values. Root mean square(RMS) deviation is determined and compared.

Distribution and Abundance of <i>Parthenium hysterophorus</i>L. in Metekel Zone, Northwest Ethiopia

Parthenium hysterophorus L. is a harmful invasive weed to plant biodiversity and human health. It is native to American tropics and first introduced to Ethiopia in the 1970s. Today, it is widely distributed across the country and severely affecting the biodiversity, crop, and animal production in the country. In the Metekel Zone, there was no report on its distribution and impacts so far. Therefore, this study was aimed to assess the distribution and abundance of the plant in the zone. The distribution and abundance data of the weed were recorded at five km intervals following all accessible roads of the zone. The result reveals that P. hysterophorus L. was less distributed in the area with a 4.95% frequency. However, it was found abundantly growing at roadsides, wastelands, around habitation, market place, and around Zeghibridge where it can rapidly spread to most economical lands like the arable and grazing lands. Moreover, it has aggressively invaded a nursery site, which enables the weed to enter agricultural fields directly. This suggests that the weed is on a fast move to agricultural lands in the zone. The regular active development activities such as agricultural investment, construction of roads, and factories are presumed to promote its spread. Therefore, a decisive and timely decision is needed to mitigate the weed when it is still sparse and small.

Near Optimal PID Controllers for the Biped Robot While Walking on Uneven Terrains

The execution of the gaits generated with the help of a gait planner is a crucial task in biped locomotion. This task is to be achieved with the help of a suitable torque based controller to ensure smooth walk of the biped robot. It is important to note that the success of the developed proportion integration differentiation （PID） controller depends on the selected gains of the controller. In the present study, an attempt is made to tune the gains of the PID controller for the biped robot ascending and descending the stair case and sloping surface with the help of two non-traditional optimization algorithms, namely modified chaotic invasive weed optimization （MCIWO） and particle swarm optimization （PSO） algorithms. Once the optimal PID controllers are developed, a simulation study has been conducted in computer for obtaining the optimal tuning parameters of the controller of the biped robot. Finally, the optimal gait angles obtained by using the best controller are fed to the real biped robot and found that the biped robot has successfully negotiated the said terrains.