Invasive Weed Optimization Algorithm Research Articles

A Novel Wetland Detection and Classification Using Invasive Weed Optimization Algorithm with Deep Learning Model

A Novel Wetland Detection and Classification Using Invasive Weed Optimization Algorithm with Deep Learning Model

Energy and exergy analysis of ternary nanofluid for electric vehicle coolant through invasive weed optimisation algorithm—a numerical study

Energy and exergy analysis of ternary nanofluid for electric vehicle coolant through invasive weed optimisation algorithm—a numerical study

Osprey optimization algorithm for MPPT of PV system with forward DC to DC converter under partial shading conditions

Abstract Electric power is required anywhere for multiple purposes to do various tasks. Production of electricity from renewable sources such solar energy is feasible solution for many problems. Photovoltaic modules are generally used to produce electricity through solar irradiance. However, a maximum power point tracking device (MPPTD) must be incorporated to Photovoltaic system to ensure its maximum utilization or best efficiency. A forward DC to DC circuit is selected to work as MPPTD in this paper. Many conventional algorithms including incremental conductance, perturb & observe are available to work effectively for MPPTD under uniform solar irradiances. However, non-uniform irradiances will be received by Photovoltaic modules where many are connected to form a power generation system due to trees, shading, dust, birds, clouds, etc Such phenomenon drags the system into Partial Shading Condition (PSC). Under PSC, conventional algorithms cannot identify the best location to harvest more energy. Hence, an optimization technique is required to operate Photovoltaic system at its outfit utilization during PSC. An optimization technique namely ‘Osprey Optimization Algorithm (OOA)’ is developed in this paper on MPPTD to harvest more energy during PSC. The comparison among OOA, Grey Wolf Optimization (GWO), Modified Invasive Weed Optimization (MIWO) and Whale Optimization Algorithm (WOA) is also included in this paper. The Hardware—in the—Loop is establish by using OPAL-RT technology to collect various results for presenting analysis under different operating conditions.

Optimizing Underground Coal Mine Safety: Leveraging Advanced Computational Algorithms for Roof Fall Rate Prediction and Risk Mitigation

AbstractThe utilization and consumption of coal in various nations have emphasized the pivotal role played by coal mines. However, aside from the substantial contribution of coal mines, miners, engineers, and craftsmen in this industry have long been exposed to numerous risks and financial losses resulting from roof collapses in underground coal mines. Hence, due to the heightened sensitivity surrounding this issue, the accurate and low-error forecasting and assessment of the roof fall rate (RFR) are deemed crucial and of utmost importance. Nonetheless, due to the intricate and uncertain inherent characteristics of the rock formations, assessing the RFR has encountered multiple challenges that cannot be precisely approximated through traditional methods. In this paper, algorithms such as the harmony search algorithm (HS) and the invasive weed Optimization algorithm (IWO) are harnessed to address the aforementioned challenges. To model the RFR, a total of 109 data points were used, incorporating input parameters such as primary roof support (PRSUP), depth of cover (D), coal mine roof rating (CMRR), mine height (MH), and intersection diagonal span (IS). For effective data analysis and model development, the dataset was split into two separate groups: one for training and the other for testing. Specifically, 80% of the data was used to build the model, while the remaining 20% was allocated for model evaluation and validation. Based on the outcomes of three statistical metrics R2, MSE, and RMSE, it is evident that the deployment of HS and IWO algorithms demonstrates high performance, with predicted values closely aligning with actual ones. Consequently, the utilization of intelligent algorithms in the field of rock engineering is positioned as a potent tool for researchers and engineers. In conclusion, a sensitivity analysis is carried out with the help of the @RISK software as a means of ranking the influence that the input parameters have on the output of the model. Its results indicate that among different parameters, the CMRR parameter with a sensitivity degree of 0.11 has the most impact on the model, even with the smallest change in this parameter, a significant change is made in the model output.

A Novel Invasive Weed Optimization and its Variant for the Detection of Polycystic Ovary Syndrome

Introduction: This study intends to provide a novel Invasive Weed Optimization (IWO) algorithm for the detection of Polycystic Ovary Syndrome (PCOS) from ultrasound ovarian images. PCOS is an intricate anarchy described by hyperandrogenemia and irregular menstruation. Indian women are increasingly finding reproductive disorders, namely PCOS. Methods: The women having PCOS grow more small follicles in their ovaries. The radiologists take a look into women's ovaries by use of ultrasound scanning equipment to manually count the number of follicles and their size for fertility treatment. These may lead to error diagnosis. Results: This paper proposed an automatic follicle detection system for identifying PCOS in the ovary using IWO. The performance of IWO is improved in Modified Invasive Weed Optimization (MIWO). This algorithm imitates the biological weeds' behavior. The MIWO is employed to obtain the optimal threshold by maximizing the between-class variance of the modified Otsu method. The efficiency of the proposed method has been compared with the well-known optimization technique called Particle Swarm Optimization (PSO) and with IWO. Conclusion: Experimental results proved that the MIWO finds an optimal threshold higher than that of IWO and PSO.

Optimizing PID control for enhanced stability of a 16‐DOF biped robot during ditch crossing

AbstractThe current research article discusses the design of a proportional–integral–derivative (PID) controller to obtain the optimal gait planning algorithm for a 16‐degrees‐of‐freedom biped robot while crossing the ditch. The gait planning algorithm integrates an initial posture, position, and desired trajectories of the robot's wrist, hip, and foot. A cubic polynomial trajectory is assigned for wrist, hip, and foot trajectories to generate the motion. The foot and wrist joint angles of the biped robot along the polynomial trajectory are obtained by using the inverse kinematics approach. Moreover, the dynamic balance margin was estimated by using the concept of the zero‐moment point. To enhance the smooth motion of the gait planner and reduce the error between two consecutive joint angles, the authors designed a PID controller for each joint of the biped robot. To design a PID controller, the dynamics of the biped robot are essential, and it was obtained using the Lagrange–Euler formulation. The gains, that is, KP, KD, and KI of the PID controller are tuned with nontraditional optimization algorithms, such as particle swarm optimization (PSO), differential evolution (DE), and compared with modified chaotic invasive weed optimization (MCIWO) algorithms. The result indicates that the MCIWO‐PID controller generates more dynamically balanced gaits when compared with the DE and PSO‐PID controllers.

Invasive weed optimization based compact hybridized fractal antenna design for RF energy harvesting and multifunctional wireless applications

This paper describes the optimal design and analysis of compact multiband Hybrid Fractal Antenna using Invasive Weed Optimization (HFAIWO). The configured structure involves the fusion of Minkowski, Sierpinski carpet and Giuseppe Peano in a coherent manner. The two-iterative conglomerated fractal antenna is realized physically by considering FR4 epoxy material (dielectric constant, εr = 4.4 and mass density = 1,900 kg/m3). The volumetric dimensions of the fabricated prototype are 36 x 36 x 1.6 mm3. In the designing process, the geometrical descriptors, namely, feedline width ‘WF’ and ground plane dimension ‘LG’ are optimized specifically using Invasive Weed Optimization (IWO) and Harmony Search Algorithm (HSA) strategies. The optimal solution is searched by changing the descriptor values under a set of practical constraints. The examined values of ‘WF’ and ‘LG’ using IWO and HSA are 3 and 31 mm, and 2.6 and 28.7 mm, respectively. Comparative results reveal that IWO offers superior solution quality and convergence than HSA. Afterwards, experimentation of the Projected HFAIWO is done to justify the recommended fractal hybridization approach. Measurements reveal that for VSWR ≤ 2, the fabricated structure produces nine resonance points (1.84, 3.99, 5.15, 5.55, 6.30, 6.58, 8.00, 9.29, and 12.01 GHz) with appreciable gain values. At the respective resonance points, the −10 dB impedance bandwidth is 3.9 %, 3.1 %, 2.1 %, 2.2 %, 1.4 %, 1.2 %, 6.9 %, 1.2 %, and 12.23 %. The provided radiation patterns are arbitrary bidirectional/omnidirectional. By applying the above-said approaches, the radiator size is reduced by 54 %. Importantly, the constructed structure covers two important bands i.e. 1.84 GHz (GSM 1800 band, downlink) and 5.55 GHz (WLAN (Lower) 5.150–5.725 GHz) associated with energy harvesting applications. The practical outcomes suggest that the introduced prototype is a proficient candidate for energy harvesting systems, Satellite communication for downlink, Long range tracking, Battlefield surveillance, Digital broadcast satellite service, Weather Radar, and Maritime navigation radar.

Dual resource constrained flexible job shop scheduling with sequence‐dependent setup time

AbstractThis study addresses the imperative need for efficient solutions in the context of the dual resource constrained flexible job shop scheduling problem with sequence‐dependent setup times (DRCFJS‐SDSTs). We introduce a pioneering tri‐objective mixed‐integer linear mathematical model tailored to this complex challenge. Our model is designed to optimize the assignment of operations to candidate multi‐skilled machines and operators, with the primary goals of minimizing operators' idleness cost and sequence‐dependent setup time‐related expenses. Additionally, it aims to mitigate total tardiness and earliness penalties while regulating maximum machine workload. Given the NP‐hard nature of the proposed DRCFJS‐SDST, we employ the epsilon constraint method to derive exact optimal solutions for small‐scale problems. For larger instances, we develop a modified variant of the multi‐objective invasive weed optimization (MOIWO) algorithm, enhanced by a fuzzy sorting algorithm for competitive exclusion. In the absence of established benchmarks in the literature, we validate our solutions against those generated by multi‐objective particle swarm optimization (MOPSO) and non‐dominated sorted genetic algorithm (NSGA‐II). Through comparative analysis, we demonstrate the superior performance of MOIWO. Specifically, when compared with NSGA‐II, MOIWO achieves success rates of 90.83% and shows similar performance in 4.17% of cases. Moreover, compared with MOPSO, MOIWO achieves success rates of 84.17% and exhibits similar performance in 9.17% of cases. These findings contribute significantly to the advancement of scheduling optimization methodologies.

Brain Tumor Detection and Segmentation using Improved Bat Algorithm with Improved Invasive Weed Optimization

Brain tumours are serious diseases that grow in the brain and are made up of a collection of abnormal and unwanted cells. Therefore, the use of magnetic resonance imaging (MRI) for segmentation and early detection of such tumors is more important to save lives. When it comes to finding people with brain tumors, MRI is very effective and has a slightly higher detection rate than other imaging tests. Detection of brain tumors is an important complex issue in medical imaging systems due to their size, appearance, and irregular shape. Detection of brain tumors is a difficult task with medical imaging systems. In order to address the above-said issue and to develop an effective brain tumor detection technique, an improved Bat algorithm with improved Invasive Weed Optimization algorithm has been proposed in this paper. The improved Invasive Weed Optimization (IIWO) approach has been utilized in the proposed IBIIW algorithm along with the improved Bat algorithm (IBA). In the proposed work weight factor which is used for classification has been improved. Extensive experiments has been performed and it suggests that, the use of MR imaging to segment tumors has a significant influence on early detection of brain cancers. In addition to this, with MR images, the deep learning-based technique produced better detection results. The proposed method has outperformed against the baseline methods in terms of accuracy, sensitivity, and specificity with remarkable results giving values of 0.9394, 0.9281, and 0.9165 respectively.

Hybrid algorithm for global optimization based on periodic selection scheme in engineering computation

PurposeMetaheuristic algorithms based on biology, evolutionary theory and physical principles, have been widely developed for complex global optimization. This paper aims to present a new hybrid optimization algorithm that combines the characteristics of biogeography-based optimization (BBO), invasive weed optimization (IWO) and genetic algorithms (GAs).Design/methodology/approachThe significant difference between the new algorithm and original optimizers is a periodic selection scheme for offspring. The selection criterion is a function of cyclic discharge and the fitness of populations. It differs from traditional optimization methods where the elite always gains advantages. With this method, fitter populations may still be rejected, while poorer ones might be likely retained. The selection scheme is applied to help escape from local optima and maintain solution diversity.FindingsThe efficiency of the proposed method is tested on 13 high-dimensional, nonlinear benchmark functions and a homogenous slope stability problem. The results of the benchmark function show that the new method performs well in terms of accuracy and solution diversity. The algorithm converges with a magnitude of 10-4, compared to 102 in BBO and 10-2 in IWO. In the slope stability problem, the safety factor acquired by the analogy of slope erosion (ASE) is closer to the recommended value.Originality/valueThis paper introduces a periodic selection strategy and constructs a hybrid optimizer, which enhances the global exploration capacity of metaheuristic algorithms.

Optimizing pile bearing capacity prediction: Insights from dynamic testing and smart algorithms in geotechnical engineering

In contemporary mining and geotechnical projects, various approaches are employed to predict the bearing capacity of piles (Qu). However, accurately modeling pile behavior using numerical, experimental, analytical, and regression methods proves challenging and, at times, infeasible due to the intricate nature of geotechnical materials, uncertainties, and the interaction between soil and piles. Consequently, formulations are generally presented, incorporating assumptions and simplifications that deviate from the actual complexity of the problem. Moreover, despite the high accuracy of pile loading tests as a reliable method in various design stages, their high costs and time requirements deter designers from conducting field tests. To address these challenges, this study performed 50 dynamic tests (HSDT) on precast concrete piles in Indonesia, Pekanbaru, to generate the necessary datasets. To mitigate experimental costs, two optimization algorithms fruit fly optimization (FFO) and invasive weed optimization (IWO) were employed. These models incorporated pile set (S), drop height (H), hammer weight (W), cross-sectional area (A), and length (L) as input parameters. Finally, the models' accuracy was evaluated using squared correlation coefficient (R2), variance accounted for (VAF), mean square error (MSE), mean absolute percentage error (MAPE), and root mean square error (RMSE). The results revealed that the FFO algorithm achieved an accuracy range of 0.971–0.978. Similarly, the IWO algorithm exhibited an accuracy range of 0.984–0.988. Additionally, sensitivity analysis indicated that, among the input parameters, W had the most significant impact on the Qu in both algorithms.

Determining the Moho topography using an improved inversion algorithm: a case study from the South China Sea

The Parker-Oldenburg method, as a classical frequency-domain algorithm, has been widely used in Moho topographic inversion. The method has two indispensable hyperparameters, which are the Moho density contrast and the average Moho depth. Accurate hyperparameters are important prerequisites for inversion of fine Moho topography. However, limited by the nonlinear terms, the hyperparameters estimated by previous methods have obvious deviations. For this reason, this paper proposes a new method to improve the existing Parker-Oldenburg method by taking advantage of the invasive weed optimization algorithm in estimating hyperparameters. The synthetic test results of the new method show that, compared with the trial and error method and the linear regression method, the new method estimates the hyperparameters more accurately, and the computational efficiency performs excellently, which lays the foundation for the inversion of more accurate Moho topography. In practice, the method is applied to the Moho topographic inversion in the South China Sea. With the constraints of available seismic data, the crust-mantle density contrast and the average Moho depth in the South China Sea are determined to be 0.535 g/cm3 and 21.63 km, respectively, and the Moho topography of the South China Sea is inverted based on this. The results of the Moho topography show that the Moho depth in the study area ranges from 5.7 km to 32.3 km, with more obvious undulations. Among them, the shallowest part of the Moho topography is mainly located in the southern part of the Southwestern sub-basin and the southern part of the Manila Trench, with a depth of about 6 km. Compared with the CRUST 1.0 model and the model calculated by the improved Bott’s method, the RMS between the Moho model and the seismic point difference in this paper is smaller, which proves that the method in this paper has some advantages in Moho topographic inversion.

Nature-inspired optimal tuning of input membership functions of fuzzy inference system for groundwater level prediction

We present a novel regression algorithm that combines a Fuzzy Inference System (FIS) with a nature-inspired algorithm to predict variations in GroundWater Levels (GWLs). Initially, we considered several input features, including precipitation, temperature, evaporation, relative humidity, soil type, and GWL Lag. A feature importance analysis using regression tree ensemble learning reveals GWL lag as the most relevant feature and soil type as the least relevant. We eliminate features with low importance scores (soil type, temperature, and evaporation) to improve the computational efficiency. Our approach leverages Fuzzy C-Means (FCM) clustering to develop a Sugeno-type FIS with definite clusters. The dataset is clustered based on feature similarity, and Gaussian membership functions are assigned to each cluster. The mapping of each point is controlled by the range and standard deviation of the Gaussian membership functions. We train the model keeping 70% of the dataset, iteratively tuning the parameters of the Gaussian membership function for all clusters through the Invasive Weed Optimization (IWO) algorithm. The performance of the model is then evaluated using the remaining 30% of the datasets. The F-IWO-GWL model accurately predicts GWL fluctuations, achieving a high correlation coefficient (R = 0.89), low normalized root mean square error (nRMSE = 0.18), and minimal bias (bias = 0.08). A comparative analysis involving seventeen benchmark algorithms reveals the superior performance of our algorithm. To ensure a fair comparison, we calculated key metrics including Akaike’s Information Criterion (AIC), Bayesian Information Criterion (BIC), and Corrected AIC (AICc). The F-IWO-GWL algorithm exhibits the lowest values for AIC, BIC, and AICc among all tested algorithms, suggesting the best goodness-of-fit. This study provides a robust approach for predicting GWL fluctuation, applicable to various groundwater management scenarios. It offers valuable insights for policymakers and stakeholders involved in groundwater management, aiding informed decision-making.

A New Approach for Software Cost Estimation with a Hybrid Tabu Search and Invasive Weed Optimization Algorithms

Due to the ever-increasing progress of software projects and their widespread impact on all industries, models must be designed and implemented to analyze and estimate costs and time. Until now, most of the software cost estimation (SCE) has been based on the analyst’s experiences and similar projects and these models are often inaccurate and inappropriate. The project will not be finished in the specified time and will include additional costs. Algorithmic models such as COCOMO are not very accurate in SCE. They are linear and the appropriate value for effort factors is not considered. On the other hand, artificial intelligence models have made significant progress in the cost estimation modeling of software projects in the past three decades. These models determine the correct value for effort factors through iteration and training, providing a more accurate estimate compared to algorithmic models. This paper employs a hybrid model incorporating the Tabu Search (TS) algorithm and the Invasive Weed Optimization (IWO) algorithm for SCE. IWO algorithm solutions are improved using the TS algorithm. The NASA60, NASA63, NASA93, KEMERER, and MAXWELL datasets are used for the evaluation. The proposed model has been able to reduce the MMRE rate compared to the IWO algorithm and the TS algorithm. The proposed model on the NASA60, NASA63, NASA93, KEMERER, and MAXWELL datasets obtained values of MMRE of 15.43, 17.05, 28.75, 58.43, and 22.46, respectively.

Identifying Influential Spatial Drivers of Forest Fires through Geographically and Temporally Weighted Regression Coupled with a Continuous Invasive Weed Optimization Algorithm

Identifying the underlying factors derived from geospatial and remote sensing data that contribute to forest fires is of paramount importance. It aids experts in pinpointing areas and periods most susceptible to these incidents. In this study, we employ the geographically and temporally weighted regression (GTWR) method in conjunction with a refined continuous invasive weed optimization (CIWO) algorithm to assess certain spatially relevant drivers of forest fires, encompassing both biophysical and anthropogenic influences. Our proposed approach demonstrates theoretical utility in addressing the spatial regression problem by meticulously accounting for the autocorrelation and non-stationarity inherent in spatial data. We leverage tricube and Gaussian kernels to weight the GTWR for two distinct temporal datasets, yielding coefficients of determination (R2) amounting to 0.99 and 0.97, respectively. In contrast, traditional geographically weighted regression (GWR) using the tricube kernel achieved R2 values of 0.87 and 0.88, while the Gaussian kernel yielded R2 values of 0.8138 and 0.82 for the same datasets. This investigation underscores the substantial impact of both biophysical and anthropogenic factors on forest fires within the study areas.

Tuning of PID Controller for Speed Control of DC-Motor by using Generalized Regression Neural Network and Invasive Weed Optimization

Tuning of PID Controller for Speed Control of DC-Motor by using Generalized Regression Neural Network and Invasive Weed Optimization

Enhancing daily rainfall prediction in urban areas: a comparative study of hybrid artificial intelligence models with optimization algorithms

Forecasting precipitation is a crucial input to hydrological models and hydrological event management. Accurate forecasts minimize the impact of extreme events on communities and infrastructure by providing timely and reliable information. In this study, six artificial intelligent hybrid models are developed to predict daily rainfall in urban areas by combining the firefly optimization algorithm (FA), invasive weed optimization algorithm (IWO), genetic particle swarm optimization algorithm (GAPSO), neural network (ANN), group method of data handling (GMDH), and wavelet transformation. Optimization algorithms increase forecasting accuracy by controlling all stages. A variety of criteria are used for validating the models, including correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), critical success index (CSI), probability of detection (POD), and false alarm ratio (FAR). The proposed models are also evaluated in an urban area in Ahvaz, Iran. The GAPSO-Wavelet-ANN model is superior to other models for predicting daily rainfall, with an RMSE of 1.42 mm and an R of 0.9715.

Perovskite lattice constant prediction framework using optimized artificial neural network and fuzzy logic models by metaheuristic algorithms

Perovskites have gained significant attention in recent years due to their unique and versatile material properties. The lattice parameters of the perovskite compounds play a crucial role in the engineering of layers and substrates for heteroepitaxial thin films. As an essential parameter in the cubic perovskite structure, the lattice constant, plays a significant role in the development of materials for specific technological applications and serves as a distinctive identifier of the crystal structure of the material. In the field of materials science, advanced Computational Intelligence (CI)-based techniques have become increasingly important for simulating the relationship between the physicochemical parameters of chemical elements and the physical properties of materials and compounds. Hence, this paper presents efficient techniques based on artificial neural network (ANN) and fuzzy logic to predict the lattice constants of pseudo-cubic and cubic perovskites. The identification of optimized parameters for the ANN and fuzzy logic models is accomplished using innovative metaheuristic algorithms such as, Particle Swarm Optimization (PSO), Invasive Weed Optimization (IWO) and Imperialist Competitive Algorithm (ICA). In the first part, the study assessed, the effectiveness of various metaheuristic algorithms (PSO-IWO-ICA) in tuning the parameters of the ANN prediction structure in order to get the optimal parameter of the ANN. Whereas in the second part, once we extracted the best optimization algorithm, we combined it with the fuzzy logic technique and then we compared the effectiveness of the two techniques, ANN and Fuzzy logic. On the basis of root mean square error (RMSE), mean absolute error (MAE) and the coefficient of determination (R2), the proposed PSO-ANN and PSO-Fuzzy based models are compared with existing and recent models such as Ubic, Sidey, and Owolabi. The proposed PSO-Fuzzy model performs better than our PSO-ANN model, the Ubic, Sidey, and Owolabi models, with performance improvement of 70.90%, 90.36%, 89.74% 84.46%, respectively on the basis of RMSE. Similarly, it attains performance improvement of 71.26%, 90.31%, 89.58%, and 85.02% on the basis of MAE. Furthermore, the developed PSO-ANN based model outperforms the Ubic, Sidey and Owolabi models with performance improvement of 66.86%, 64.74% and 46.60% respectively, on the basis of RMSE and percentage enhancement of 66.27%, 63.75%, and 47.90% when compared on the basis of MAE. Although the PSO-Fuzzy model has the best performance of all the compared models, the developed PSO-ANN based model possesses the advantage of easy implementation in addition to its moderate performance.

Traffic and fragmentation aware algorithm for routing and spectrum assignment in Elastic Optical Network (EON)

The Elastic Optical Network (EON) provides a good solution for flexible connection and supports high-bandwidth applications. This work is mainly focused on routing and spectrum allocation problems in dynamic traffic behavior. In the time domain, it is consistently high during business hours and low outside of those hours; in the bandwidth domain, it is unevenly dispersed over the network's connections and may range from 10 (Gbps) to 100 (Gbps). As a consequence of this inefficient use of spectral resources, more connection requests are being blocked. This work addresses traffic balance and spectrum allocation issues. This work combined the features of Invasive Weed and Spotted Hyena Optimizer algorithms (IWO-RSA). The Invasive Weed Optimization Algorithm is used for routing and spectrum allocation and the Spotted Hyena is used for fragmentation issues in spectrum allocation. It has been shown, through extensive simulations, that the suggested traffic balancing-based IWO-RSA technique performs better than the benchmarked approach across the field.

A non-clustered approach to platelet collection routing problem

One of the blood components that can be extracted from whole blood is the platelet, which has a wide range of uses in medical fields. Due to the perishable nature of platelets, it is recommended that the separation occurs within six hours after the donation. Moreover, platelets constitute less than one percent of the whole blood volume, yet they are highly demanded. Given the importance of platelets in healthcare, their perishability, and their limited supply, an effective platelet supply chain leans on well-managed whole blood collection operations. In this study, we consider a blood collection problem (BCP) focusing on the collection of whole blood donations from the blood donation sites (BDSs). Different from the basic form of BCP, we consider the processing time limit (PTL) of blood and arbitrary donation patterns of donors as well as relaxing the assumption of assigning each blood collection vehicle (BCV) to a set of BDSs. Therefore, we define the non-clustered maximum blood collection problem (NCMBCP) as a variant of BCP. In this study, we examine routing decisions for platelet collections while relaxing the clustering requirement from the BDSs, which results in a significant increase in the complexity of the problem. In order to solve the problem, we propose a hybrid genetic algorithm (HGA) and an invasive weed optimization (IWO) algorithm that provide considerable improvements over the best solution in the literature for the clustered variant of the problem and outperform it (on average) by 8.68% and 8.16%, respectively.