Solution Quality Research Articles

DCWPSO: particle swarm optimization with dynamic inertia weight updating and enhanced learning strategies.

Particle swarm optimization (PSO) stands as a prominent and robust meta-heuristic algorithm within swarm intelligence (SI). It originated in 1995 by simulating the foraging behavior of bird flocks. In recent years, numerous PSO variants have been proposed to address various optimization applications. However, the overall performance of these variants has not been deemed satisfactory. This article introduces a novel PSO variant, presenting three key contributions: First, a novel dynamic oscillation inertia weight is introduced to strike a balance between exploration and exploitation; Second, the utilization of cosine similarity and dynamic neighborhood strategy enhances both the quality of solution and the diversity of particle populations; Third, a unique worst-best example learning strategy is proposed to enhance the quality of the least favorable solution and consequently improving the overall population. The algorithm's validation is conducted using a test suite comprised of benchmarks from the CEC2014 and CEC2022 test suites on real-parameter single-objective optimization. The experimental results demonstrate the competitiveness of our algorithm against recently proposed state-of-the-art PSO variants and well-known algorithms.

Impact of Core Banking Solutions in Public Sector Bank comparison with HDFC Bank

A study has been analysed the “Impact of core banking solutions in public sector bank comparison with HDFC bank.” There is no significant relationship between customers perception towards dimensions of service quality of core banking solutions offered by public sector and private banks. A method was used to random sample was conducted on 2 respondents selected from Bhagalpur district and the primary data was collected from 60 respondents (customers) of core banking solutions, public sector and HDFC banks selected from Bhagalpur district. In order to draw the conclusion, the primary data collected through interview schedule from the customers of core banking, public and sector banks comparison with HDFC bank and analysed with the help of statistical tools. The study reveals the respondents positively view about the core banking services offered by public and HDFC bank respondents positively view about the customers using selected channel of core banking services towards public bank and HDFC bank.

Transit Dynamic Operation Optimization Using Combination of Stop-Skipping Strategy and Local Route Optimization

The efficient operation and intelligent upgrading of public transportation can effectively enhance the attractiveness of conventional public transportation. In order to improve the delicacy management level of bus operations, this study designed a new dynamic optimization model for single-line bus operations with the dual optimization objectives of the lowest passenger travel cost and lowest operation cost, using a combination of the strategy of stop-skipping control and local route optimization. Simulated annealing (SA) was introduced into the genetic algorithm (GA) to design a hybrid heuristic algorithm for model solving. The effectiveness of the optimization model and the hybrid algorithm were verified and evaluated by using the No. 115 bus line in Ganzhou City as an example. The results showed that the proposed optimization model had a good usability, which can effectively improve the average vehicle speed, shorten the overall waiting time of passengers, and enhance the operational efficiency of the line. The hybrid algorithm saw significant improvement in terms of the iteration speed and the quality of the optimal solution compared with the conventional genetic algorithm.

An improved Coati Optimization Algorithm with multiple strategies for engineering design optimization problems

Aiming at the problems of insufficient ability of artificial COA in the late optimization search period, loss of population diversity, easy to fall into local extreme value, resulting in slow convergence and lack of exploration ability; In this paper, an improved COA algorithm based on chaotic sequence, nonlinear inertia weight, adaptive T-distribution variation strategy and alert updating strategy is proposed to enhance the performance of COA (shorted as TNTWCOA). The algorithm introduces chaotic sequence mechanism to initialize the position. The position distribution of the initial solution is more uniform, the high quality initial solution is generated, the population richness is increased, and the problem of poor quality and uneven initial solution of the Coati Optimization Algorithm is solved. In exploration phase, the nonlinear inertial weight factor is introduced to coordinate the local optimization ability and global search ability of the algorithm. In the exploitation phase, adaptive T-distribution variation is introduced to increase the diversity of individual population under low fitness value and improve the ability of the algorithm to jump out of the local optimal value. At the same time, the alert update mechanism is proposed to improve the alert ability of COA algorithm, so that it can search within the optional range. When Coati is aware of the danger, Coati on the edge of the population will quickly move to the safe area to obtain a better position, while Coati in the middle of the population will randomly move to get closer to other Coatis. IEEE CEC2017 with 29 classic test functions were used to evaluate the convergence speed, convergence accuracy and other indicators of TNTWCOA algorithm. Meanwhile, TNTWCOA was used to verify 4 engineering design optimization problems, such as pressure vessel optimization design and welding beam design. The results of IEEE CEC2017 and engineering design Optimization problems are compared with Improved Coati Optimization Algorithm (ICOA), Coati Optimization Algorithm (COA), Golden Jackal Optimization Algorithm (GJO), Osprey Optimization Algorithm (OOA), Sand Cat Swarm Optimization Algorithm (SCSO), Subtraction-Average-Based Optimizer (SABO). The experimental results show that the improved TNTWCOA algorithm significantly improves the convergence speed and optimization accuracy, and has good robustness. Three‑bar truss design problem, The Gear Train Design Problem, Speed reducer design problem shows a strong solution advantage. The superior optimization ability and engineering practicability of TNTWCOA algorithm are verified.

Novel Hybrid Optimization Technique for Solar Photovoltaic Output Prediction Using Improved Hippopotamus Algorithm

This paper introduces a novel hybrid optimization technique aimed at improving the prediction accuracy of solar photovoltaic (PV) outputs using an Improved Hippopotamus Optimization Algorithm (IHO). The IHO enhances the traditional Hippopotamus Optimization (HO) algorithm by addressing its limitations in search efficiency, convergence speed, and global exploration. The IHO algorithm used Latin hypercube sampling (LHS) for population initialization, significantly enhancing the diversity and global search potential of the optimization process. The integration of the Jaya algorithm further refines solution quality and accelerates convergence. Additionally, a combination of unordered dimensional sampling, random crossover, and sequential mutation is employed to enhance the optimization process. The effectiveness of the proposed IHO is demonstrated through the optimization of weights and neuron thresholds in the extreme learning machine (ELM), a model known for its rapid learning capabilities but often affected by the randomness of initial parameters. The IHO-optimized ELM (IHO-ELM) is tested against benchmark algorithms, including BP, the traditional ELM, the HO-ELM, LCN, and LSTM, showing significant improvements in prediction accuracy and stability. Moreover, the IHO-ELM model is validated in a different region to assess its generalization ability for solar PV output prediction. The results confirm that the proposed hybrid approach not only improves prediction accuracy but also demonstrates robust generalization capabilities, making it a promising tool for predictive modeling in solar energy systems.

A novel Soft Actor–Critic framework with disjunctive graph embedding and autoencoder mechanism for Job Shop Scheduling Problems

The Job-Shop Scheduling Problem (JSSP) is a well-established and classic NP-hard combinatorial optimization issue. The quality of its scheduling scheme directly affects the operational efficiency of manufacturing systems. Priority Dispatching Rules (PDRs) are often utilized to address JSSP in real-world contexts, but the process of creating effective PDRs can be daunting and time-consuming. It also necessitates comprehensive domain knowledge, typically resulting in mediocre performance. In this paper, we introduce a novel reinforcement learning (RL) model called Disjunctive Graph Embedding with Autoencoder Mechanism for Job Shop Scheduling Problems (DGEAM-JSSP), designed to automate PDRs learning. Our proposed model confronts the issue using a Graph Neural Network (GNN) to learn node features that encapsulate the spatial structure of the JSSP graph representation. The ensuing policy network is size-agnostic, enabling effective generalization on larger-scale instances. Additionally, we employ a transformer encoder, incorporating parallel encoding and a self-attention mechanism, to successfully recognize long-term dependencies among operations in large-scale scheduling problems. We also implemented an end-to-end training approach using the Soft Actor–Critic (SAC) algorithm to instruct the two modules. Computational experiment results reveal that, with a single training, our agent successfully learns a superior dispatching policy, surpassing PDRs and state-of-the-art RL frameworks specifically tailored for each JSSP instance size in solution quality, as well as OR-Tools in execution speed. Moreover, results from random and benchmark instances illustrate that the uniquely-modeled learned policies have impressive generalization performance on real-world instances and significantly larger-scale scenarios involving up to 2000 operations.

Enhancing quantum annealing accuracy through replication-based error mitigation*

Abstract Quantum annealers like those manufactured by D-Wave Systems are designed to find high quality solutions to optimization problems that are typically hard for classical computers. They utilize quantum effects like tunneling to evolve toward low-energy states representing solutions to optimization problems. However, their analog nature and limited control functionalities present challenges to correcting or mitigating hardware errors. As quantum computing advances towards applications, effective error suppression is an important research goal. We propose a new approach called replication based mitigation (RBM) based on parallel quantum annealing (QA). In RBM, physical qubits representing the same logical qubit are dispersed across different copies of the problem embedded in the hardware. This mitigates hardware biases, is compatible with limited qubit connectivity in current annealers, and is well-suited for currently available noisy intermediate-scale quantum annealers. Our experimental analysis shows that RBM provides solution quality on par with previous methods while being more flexible and compatible with a wider range of hardware connectivity patterns. In comparisons against standard QA without error mitigation on larger problem instances that could not be handled by previous methods, RBM consistently gets better energies and ground state probabilities across parameterized problem sets.

Multiobjective optimization of collaborative robotic task sequence assignment problems under collision-free constraints

This paper proposes a multiobjective optimization approach to address the challenge of collaborative manufacturing with multiple robot arms. Given the necessity for multiple robot arms to work together, the potential for collisions between robotic motions is a significant concern, and the automated task sequence assignment for robots becomes increasingly complex. Previous research has either simplified the collision-free conditions in a limited working area, or employed a master-slave approach to obtain only a local solution. Consequently, we propose a unified global optimization approach for simultaneously addressing various collaborative manufacturing issues, including robotic task sequence assignment (RTSA), multiple inverse kinematics (IK) selection, joint-space collision-free operations and multiple manufacturing objectives. As the optimal collaborative RTSA problem is a combinatorial optimization problem with non-deterministic polynomial-time hard (NP-hard) complexity, this paper presents a hybrid nondominated sorting genetic algorithm III (NSGA-III) method that integrates a Hamming-distance-based method and a greedy strategy within NSGA-III to improve population diversity and solution quality. To validate the efficacy of the proposed approach, simulation experiments were conducted on cooperative manufacturing scenarios, with two objectives: task completion time and task load balancing. The experimental results demonstrate that the proposed approach is effective in obtaining collision-free Pareto solutions. Furthermore, the proposed hybrid NSGA-III method obtains superior solutions compared to the original method in the studied problem, as measured by two performance indices.

Multi-objective Harris Hawks optimization algorithm for selecting best location and size of distributed generation in radial distribution system

Distributed Generating Units (DGUs) are widely used in backup power, renewable energy integration, voltage regulation, and energy storage applications. The DGUs are small-scale power-generating units located near the load centers of a power system, as opposed to large, centralized power plants located far away. However, the DGUs must reduce power losses and environmental impacts and improve the voltage profile. This was achieved by effective optimization of the position and rating of DGU. However, the conventional optimization methods failed to optimize the power losses, voltage-current balancing issues, and harmonic distortions. So, this article presents the selection of the best location and size of DGU in radial distribution systems using a nature-inspired multi-objective Harris Hawks optimization (HHO) algorithm while considering improvements in the voltage profile and reductions in active power losses. The HHO algorithm is inspired by the supportive actions of intelligent birds, such as Harris Hawks, while searching for prey. The distinguished hunting process of Harris Hawks using other family members makes it unique, which is useful for searching for better quality solutions while optimizing multi-objective fitness functions. Fitness function is determined by considering each bus's voltage profile and branches' active power losses. The proposed method is tested on two circle distribution systems (69 bus and 118 bus). The results are compared to those obtained using some of the more well-known optimization techniques given by scholars in recent years. The optimization of seven DGU in 118 bus radial distribution systems resulted in 405.32 kW of active power losses, a 68.78% decrease in losses, and 0.9723 Vs of minimum voltage.

Reinforcement learning-enabled swarm intelligence method for computation task offloading in Internet-of-Things blockchain

Blockchain technology, based on decentralized data storage and distributed consensus design, has became a promising solution to address data security risks and provide privacy protection in the Internet-of-Things (IoT) due to its tamper-proof and non-repudiation features. Although blockchain typically does not require the endorsement of third-party trust organizations, it mostly needs to perform necessary mathematical calculations to prevent malicious attacks, which results in stricter requirements for computation resources on the participating devices. By offloading the computation tasks required to support blockchain consensus to edge service nodes or the cloud, while providing data privacy protection for IoT applications, it can effectively address the limitations of computation and energy resources in IoT devices. However, how to make reasonable offloading decisions for IoT devices remains an open issue. Due to the excellent self-learning ability of Reinforcement Learning (RL), this paper proposes a RL enabled Swarm Intelligence Optimization Algorithm (RLSIOA) that aims to improve the quality of initial solutions and achieve efficient optimization of computation task offloading decisions. The algorithm considers various factors that may affect the revenue obtained by IoT devices executing consensus algorithms (e.g., Proof-of-Work), It optimizes the proportion of sub-tasks to be offloaded and the scale of computing resources to be rented from the edge and cloud to maximize the revenue of devices. Experimental results show that RLSIOA can obtain higher-quality offloading decision-making schemes at lower latency costs compared to representative benchmark algorithms.

Estimating data complexity and drift through a multiscale generalized impurity approach

The quality of machine learning solutions, and of classifier models in general, depend largely on the performance of the chosen algorithm, and on the intrinsic characteristics of the input data. Although work has been extensive on the former of these aspects, the latter has received comparably less attention. In this paper, we introduce the Multiscale Impurity Complexity Analysis (MICA) algorithm for the quantification of class separability and decision-boundary complexity of datasets. MICA is both model and dimensionality-independent and can provide a measure of separability based on regional impurity values. This makes it so that MICA is sensible to both global and local data conditions. We show MICA to be capable of properly describing class separability in a comprehensive set of both synthetic and real datasets and comparing it against other state-of-the-art methods. After establishing the robustness of the proposed method, alternative applications are discussed, including a streaming-data variant of MICA (MICA-S), that can be repurposed into a model-independent method for concept drift detection.

Oppositional arithmetic optimization algorithm for network reconfiguration and simultaneous placement of DG and capacitor in radial distribution networks

AbstractThe prime objective of this study is the simultaneous network reconfiguration with distributed generation (DG) and capacitor placement in radial distribution networks (RDN) to get the techno and economic benefits for two separate objectives, which are the minimization of actual power loss and annual economic loss as well as a multi objective combining these two single objectives using an oppositional arithmetic optimization algorithm (OAOA). It is an improved version of the currently suggested arithmetic optimization algorithm (AOA) used in the field of engineering for the optimization task. Though the recently developed AOA shows its efficacy in different optimization tasks but to improve the quality of solutions, convergence behavior, and to avoid the local optima, oppositional behavior is added to AOA. The efficacy and exactness of OAOA are tested on three test systems (33‐bus, 69‐bus, and 118‐bus). For the reduction of power loss and annual economic loss as well as the multi objective optimization, two scenarios with different cases are executed using OAOA in RDNs. In scenario 1, the installation of the capacitor (case 1), the installation of unity power factor (UPF) based DG (case 2), and the placement of optimal power factor (OPF) based DG (case 3) have been executed. In scenario 2, allocation of UPF based DG and capacitors simultaneously (case 1), placement of OPF based DG and capacitors simultaneously (case 2) and simultaneous reconfiguration with installation of OPF based DG and capacitor (case 3) has been executed. This recommended OAOA algorithm provides the percentage improvement in real power loss and yearly economic loss for all cases of 33‐bus, and 69‐bus systems (34.28%, 65.50%, 94.43%, 93.26%, 94.89%, and 95.11%), (28.54%, 56.69%, 83.42%, 79.62%, 83.65%, and 83.71%), and (35.51%, 69.16%, 98.10%, 97.52%, 98.22%, and 98.25%), (30.26%, 61.68%, 88.75%, 85.42%, 88.81%, and 88.98%), respectively. The results and comparative study reveal that the OAOA is better than several optimization algorithms in terms of solution quality and good results. This algorithm has a good speed of response and convergence behavior.

An enhanced snow ablation optimizer for UAV swarm path planning and engineering design problems

The Snow Ablation Optimizer (SAO) is an advanced optimization algorithm. However, it suffers from slow convergence and a tendency to become trapped in local optima. To address these limitations, we propose an Enhanced Snow Ablation Optimization algorithm (ESAO). Initially, an adaptive T-distribution control strategy is employed to improve the algorithm's exploratory position adjustments, facilitating the identification of the global optimum. Furthermore, we introduce a Cauchy mutation strategy, endowing individuals with a robust capability to escape local extrema and steer the population towards more favorable directions. A leader-based boundary control strategy is also proposed to enhance the optimizer's search performance, significantly increasing the accuracy, speed, and stability of the algorithm in tackling complex problems. To validate the performance of ESAO, we utilize 29 CEC2017 benchmark functions for comparison against eight popular algorithms across various dimensions. Our algorithm ranked first in all comparisons, demonstrating ESAO's effectiveness. Additionally, to evaluate the practical applicability of the proposed method, we mathematically modeled the UAV swarm and solved the UAV swarm path planning problem using various competitor algorithms. Furthermore, we applied different competitor algorithms to two engineering design problems. The results demonstrate that ESAO performs the best. In general, ESAO outperforms its counterparts in terms of solution quality and stability, showcasing its superior application potential.

The set team orienteering problem

We introduce the Set Team Orienteering Problem (STOP), a generalised variant of the Set Orienteering Problem (SOP), in which customer locations are split into multiple clusters (or groups). Each cluster is associated with a profit that can be gained only if at least one customer from the cluster is visited. There is a fleet of homogeneous vehicles at a depot, and each vehicle has a limited travel time. The goal of the STOP is to find a set of feasible vehicle routes to collect the maximum profit. We first formulate the problem as a Mixed Integer Linear Programming (MILP) to mathematically describe it. A branch-and-price (B&P) algorithm is then developed to solve the problem to optimality. To deal with large instances, we propose a Large Neighbourhood Search (LNS), which relies on problem-tailored solution representation, removal, and insertion operators. Multiple experiments on newly generated instances confirm the performance of our approaches. Remarkably, when tested on the SOP using benchmarks available in the literature, our B&P method achieves optimality in 61.9% of these instances. This is the first time such a large number of SOP instances are solved to optimality. Our LNS outperforms existing algorithms proposed to solve the SOP in terms of solution quality. Out of 612 considered instances, it improves 40 best-known solutions.

Application of Metaheuristics for Multi-Trip Capacitated Vehicle Routing Problem with Time Window

Abstract This study focuses on the delivery routing problem faced by a transport company located in Phuket, Thailand. The goal of this study is to find a daily optimum route in order to minimize the total transportation cost, which comprises fixed costs associated with vehicle rental and variable costs calculated based on factors of travel distance, fuel prices, and fuel consumption. The complexity of this problem is compounded by the fact that customer demand often exceeds a vehicle capacity, in terms of weight and volume. In addition, delivery must be made within specific time windows. To tackle this issue, the delivery routing problem is classified as a multi-trip capacitated vehicle routing problem with time window (MTCVRPTW). Since the problem is NP-hard, an application of metaheuristic is more practical to determine the delivery routing of the company within a reasonable computing time. In this study, Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithm are applied to solve MTCVRPTW. The numerical results show that DE provides better solution quality compared to those obtained from PSO and company current practices.

Explicit and approximate solutions for a classical hyperbolic fragmentation equation using a hybrid projected differential transform method

Population balance equations are widely used to study the evolution of aerosols, colloids, liquid–liquid dispersion, raindrop fragmentation, and pharmaceutical granulation. However, these equations are difficult to solve due to the complexity of the kernel structures and initial conditions. The hyperbolic fragmentation equation, in particular, is further complicated by the inclusion of double integrals. These challenges hinder the analytical solutions of number density functions for basic kernel classes with exponential initial distributions. To address these issues, this study introduces a new approach combining the projected differential transform method with Laplace transform and Padé approximants to solve the hyperbolic fragmentation equation. This method aims to provide accurate and efficient explicit solutions to this challenging problem. The approach's applicability is demonstrated through rigorous mathematical derivation and convergence analysis using the Banach contraction principle. Additionally, several numerical examples illustrate the accuracy and robustness of this new method. For the first time, new analytical solutions for number density functions are presented for various fragmentation kernels with gamma and other initial distributions. This method significantly enhances solution quality over extended periods using fewer terms in the truncated series. The solutions are compared and verified against the finite volume method and the homotopy perturbation method, showing that the coupled approach not only estimates number density functions accurately but also captures integral moments with high precision. This research advances computational methods for particle breakage phenomena, offering potential applications in various industrial processes and scientific disciplines.

Machine Learning based Algorithm Selection and Genetic Algorithms for serial-batch scheduling

Whenever combinatorial optimization problems cannot be solved by exact solution methods in reasonable time, tailor-made algorithms (heuristics, meta-heuristics) are developed. Often, these heuristics exploit structural properties and perform well on selected subsets of the problem space. For example, this is how the two best-known construction heuristics solve the scheduling problem investigated in this study (i.e., the scheduling of parallel serial-batch processing machines with incompatible job families, restricted batch capacities, arbitrary batch capacity demands, and sequence-dependent setup times). However, when the properties change, the performance of one algorithm might decrease, and another algorithm might have been the better choice. To resolve this issue, we propose using Machine Learning to exploit the strengths of different algorithms and to select the probably best-performing algorithm for each problem instance individually. To that, we investigate a variety of methods from the “learning-to-rank” literature and propose several adaptations. Furthermore, because there is no algorithm for the considered scheduling problem that is capable to explore the entire solution space, we developed two Genetic Algorithms for the improvement of initial solutions computed by the selected algorithms. Here, we put special emphasis on ensuring that the solution representation (encoding) reflects the entire solution space and that the operators (e.g., for recombination and mutation) are appropriate to explore and exploit this space completely. Our computational experiments show an average increase of 39.19% in solution quality.

AdaGuiDE: An adaptive and guided differential evolution for continuous optimization problems

Differential evolution (DE) has been proven as a simple yet powerful meta-heuristic algorithm on tackling continuous optimization problems. Nevertheless most existing DE methods still suffer from certain drawbacks including the use of ineffective mechanisms to adjust mutation strategies and their control parameters that may possibly mislead the search directions, and also the lack of intelligent guidance and reset mechanisms to escape from local optima. Therefore, to enhance the adaptability of DE-based search frameworks and the robustness on optimizing complex problems full of local optima, an adaptive and guided differential evolution (AdaGuiDE) algorithm is proposed. Essentially, the adaptability of the AdaGuiDE search framework is enhanced by three schemes to iteratively refine the search behaviour at two different levels. At the macroscopic level, the AdaGuiDE search framework revises the existing adaptive mechanism for selecting appropriate DE search strategies by counting the actual contributions in terms of solution quality. In addition, the adaption strategy is extended to the microscopic level where a penalty-based guided DE search is employed to guide the search escaping from local optima through temporarily penalizing the local optima and their neighborhood. Furthermore, a systematic boundary revision scheme is introduced to dynamically adjust the search boundary for locating any potential regions of interest during the search. For a rigorous evaluation of the proposed search framework, the AdaGuiDE algorithm is compared against other well-known meta-heuristic approaches on three sets of benchmark functions involving different dimensions in which the AdaGuiDE algorithm attained remarkable results especially on the high-dimensional and complex optimization problems. More importantly, the proposed AdaGuiDE framework shed lights on many possible directions to further enhance the adaptability of the underlying DE-based search strategies in tackling many challenging real-world applications.

Improving decomposition-based MOEAs for combinatorial optimisation by intensifying corner weights

In the real world, a class of common problems such as supply chain management, project scheduling, portfolio optimisation and facility location design are multi-objective combinatorial optimisation problems (MOCOPs), where there are multiple objectives and the set of feasible solutions is discrete. In MOCOPs, corner solutions are solutions in which at least one objective reaches the optimal value. Corner solutions are important as they are likely to be preferred by the decision maker and are able to help improve algorithm performance. In this paper, we first reveal that in decomposition-based MOEAs, improving the corner weights (as opposed to improving the middle weights) significantly enhances the generation of corner solutions, thereby enhancing the overall performance of algorithms. Based on this, we propose a method to enhance the search for corner solutions in MOCOPs. We act on a class of popular MOEAs, decomposition-based MOEAs, and in their evolutionary mechanism we intensify the weights in the corner areas. To verify the proposed method, we conduct experiments by incorporating the method into three decomposition-based MOEAs, MOEA/D, MOEA/D-DRA-UT and MOEA/D-LdEA (the latter two were designed specifically for enhancing the search of corner solutions). The experimental results demonstrate that the proposed method can improve the spread of solution sets found, without compromising the quality of internal solutions.

Multisource data framework for prehospital emergency triage in real-time IoMT-based telemedicine systems

Background and objectiveThe Internet of Medical Things (IoMT) has revolutionized telemedicine by enabling the remote monitoring and management of patient care. Nevertheless, the process of regeneration presents the difficulty of effectively prioritizing the information of emergency patients in light of the extensive amount of data generated by several integrated health care devices. The main goal of this study is to be improving the procedure of prioritizing emergency patients by implementing the Real-time Triage Optimization Framework (RTOF), an innovative method that utilizes diverse data from the Internet of Medical Things (IoMT). MethodsThe study’s methodology utilized a variety of Internet of Medical Things (IoMT) data, such as sensor data and texts derived from electronic medical records. Tier 1 supplies sensor and textual data, and Tier 3 imports textual data from electronic medical records. We employed our methodologies to handle and examine data from a sample of 100,000 patients afflicted with hypertension and heart disease, employing artificial intelligence algorithms. We utilized five machine-learning algorithms to enhance the accuracy of triage. ResultsThe RTOF approach has remarkable efficacy in a simulated telemedicine environment, with a triage accuracy rate of 98%. The Random Forest algorithm exhibited superior performance compared to the other approaches under scrutiny. The performance characteristics attained were an accuracy rate of 98%, a precision rate of 99%, a sensitivity rate of 98%, and a specificity rate of 100%. The findings show a significant improvement compared to the present triage methods. ConclusionsThe efficiency of RTOF surpasses that of existing triage frameworks, showcasing its significant ability to enhance the quality and efficacy of telemedicine solutions. This work showcases substantial enhancements compared to existing triage approaches, while also providing a scalable approach to tackle hospital congestion and optimize resource allocation in real-time. The results of our study emphasize the capacity of RTOF to mitigate hospital overcrowding, expedite medical intervention, and enable the creation of adaptable telemedicine networks. This study highlights potential avenues for further investigation into the integration of the Internet of Medical Things (IoMT) with machine learning to develop cutting-edge medical technologies.