Solution Quality Research Articles

A comparison of representations in grammar-guided genetic programming in the context of glucose prediction in people with diabetes

The representation of individuals in Genetic Programming (GP) has a large impact on the evolutionary process. In previous work, we investigated the evolutionary process of three Grammar-Guided GP (GGGP) methods, Context-Free Grammars GP (CFG-GP), Grammatical Evolution (GE) and Structured Grammatical Evolution (SGE), in the context of the complex, real-world problem of predicting the glucose level of people with diabetes two hours ahead of time. We concluded that representation choice is more impactful with a higher maximum depth, and that CFG-GP better explores the search space for deeper trees, achieving better results. Furthermore, we find that CFG-GP relies more on feature construction, whereas GE and SGE rely more on feature selection. Additionally, we altered the GGGP methods in two ways: using ϵ-lexicase selection, which solved the overfitting problem of CFG-GP and helps it to adapt to patients with high glucose variability; and with a penalization of complex trees, to create more interpretable trees. Combining ϵ-lexicase selection with CFG-GP performed best. In this work, we extend on the previous work and evaluated the impact of initialization methods in the quality of solutions. We found that they have no significant impact, even when the change of representation has.

An Enhanced Crowned Porcupine Optimization Algorithm Based on Multiple Improvement Strategies

The Crowned Porcupine Optimization (CPO) algorithm exhibits certain deficiencies in initialization efficiency, convergence speed, and adaptability. To address these issues, this paper proposes an enhanced Crowned Porcupine Optimization algorithm (ICPO) based on multiple improvement strategies. ICPO optimizes the initialization process by introducing Logistic chaotic mapping, thereby expanding the search space. It accelerates convergence through an elite retention strategy and enhances global search capability by integrating stochastic operations, mutation-like operations, and crossover-like operations to increase population diversity. Additionally, adaptive step tuning based on fitness values is employed to comprehensively improve the algorithm’s performance. To verify the effectiveness of ICPO, 23 standard functions were used for a comprehensive evaluation, and its practicality was further validated through optimization of actual engineering design problems. The experimental results demonstrate significant improvements in convergence speed, solution quality, and adaptability with ICPO.

Research on UAV Conflict Detection and Resolution Based on Tensor Operation and Improved Differential Evolution Algorithm

With the widespread application of unmanned aerial vehicles (UAVs) in civilian and military fields, how to effectively detect and resolve conflicts of large-volume and high-density UAV flights in local airspace has become an important issue. This paper proposes a method for UAV conflict detection and resolution based on tensor operation and an improved differential algorithm. Firstly, the UAV protection zone model and airspace rasterization model are constructed, and the rapid detection of flight conflicts is achieved by using the properties of tensor Hadamard product operations and prime factorization. Then, for the detected conflicts, a hybrid improved differential evolution algorithm is used for resolution. This algorithm improves the solution speed and quality by using an adaptive mutation operator and introducing a redundant evaluation mechanism and a confidence-based selection strategy. Simulation results show that this method can quickly and accurately detect and resolve flight conflicts in high-density UAV scenarios, with high timeliness and conflict resolution capability.

Mixed‐Integer Motion Planning for Nonholonomic Robots Under Visible Light Communication Constraints

ABSTRACTThis work is concerned with the problem of motion planning for a group of nonholonomic robots under Visible Light Communication (VLC) connectivity requirements. Recently, multi‐robot systems operating with VLC networks have been demonstrated as an attractive solution for inspection tasks in pipelines and underground facilities. However, there are still no established methodologies to automate their operation. We address this problem with an optimization framework based on Mixed‐Integer Linear Programming (MILP) to design a motion planner that safely coordinates the robot team while jointly addressing the nonlinearity of the robots' dynamics, the directed line‐of‐sight requirement for VLC connections, and the connectivity maintenance of the ensuing directed networks. We propose a novel method to encode the nonlinear dynamics of nonholonomic robots into the MILP formulation and demonstrate through comparative trials that, although the resulting optimization search space is more restricted, the approach still outperforms mixed‐integer nonlinear programming in terms of optimization time and solution quality. The efficacy of the proposal is also demonstrated through realistic simulations of the Turtlebot3 robot platform.

Emulating Wildfire Plume Injection Using Machine Learning Trained by Large Eddy Simulation (LES).

Wildfires have a major influence on the Earth system, with costly impacts on society. Despite decades of research, wildfires are still challenging to represent in air quality and chemistry-climate models. Wildfire plume rise (injection) is one of those poorly resolved processes and is also a major source of uncertainty in evaluating the wildfire impacts on air quality. Studies have shown that current plume rise models are subject to large uncertainties, including the Freitas Scheme, a widely used 1-dimensional, cloud-resolving subgrid model. In this work, a new machine learning-based plume rise emulator is presented, trained using a high-resolution, turbulence-resolving large eddy simulation (LES) model coupled with microphysics. The preliminary results show that this machine learning emulator outperforms the benchmark model, the Freitas scheme, in both accuracy and computational efficiency. Furthermore, a bagging ensemble is built to further increase the robustness and to battle internal variability. Efforts have been made to ensure that the machine learning emulator is robust, transparent, and not overtrained, and the results are interpretable and physically sound. Overall, this Plume Rise Emulating System using Machine Learning (PRESML) is a promising solution for regional and global air quality and chemistry-climate models.

Multi-Objective Optimization of Friction Stir Processing Tool with Composite Material Parameters

Compared to base aluminum alloys, the surface composites of aluminum alloys are more widely used in the automotive, aerospace, and other industries. The ability to yield enhanced physical properties and a smoother microstructure has made friction stir processing (FSP) the method of choice for developing aluminum-based surface composites in recent times. In this work, the Goal Programming (GP) approach is adopted for the Multi-Objective Optimization of FSP processes with three Artificial Intelligence (AI)-based metaheuristics, viz., Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO), and Teaching–Learning-Based Optimization (TLBO). Three parameters, copper percentage (Cu%), graphite percentage (Gr%), and number of passes, are considered, and multi-factor non-linear regression prediction models are developed for the three responses, Tool Vibrations, Power Consumption, and Cutting Force. The TLBO algorithm outperformed the ABC and PSO algorithms in terms of solution quality and robustness, yielding significant improvements in tool life. The results with TLBO were improved by 20% and 14% compared to the PSO and ABC algorithms, respectively. This proves that the TLBO algorithm performed better compared with the ABC and PSO algorithms. However, the computation time required for the TLBO algorithm is higher compared to the ABC and PSO algorithms. This work has opened new avenues towards applying the GP approach for the Multi-Objective Optimization of FSP tools with composite parameters. This is a significant step towards toll life improvement for the FSP of composite alloys, contributing to sustainable manufacturing.

High-fidelity prediction of forming quality for self-piercing riveted joints in aluminum alloy based on machine learning

The effect of rivet length and sheet thickness on the cross-sectional formation and tensile-shear performance of self-piercing riveted joints in AA5754 aluminum alloy was examined through experimental investigation. The influence degree of joining parameters on the forming quality was analyzed. It was revealed that rivet length and sheet thickness are pivotal factors influencing the tensile-shear strength of the joint, culminating in the identification of four optimal riveting process schemes:LCh1Ah2A、LAh1Ah2B、LAh1Ah2B and LBh1Ah2B. A simulation model for self-piercing riveting was established, employing the GISSMO failure model and the modified Mohr-Coulomb (MMC) failure criteria to predict the damage and fracture of the aluminum alloy. A plethora of high-quality datasets depicting the cross-sections of the joints were derived from simulation analysis. Subsequently, the structure and hyperparameter determination method of traditional neural network prediction models were elucidated. By amalgamating the Aquila Optimization (AO) algorithm with the African Vultures Optimization Algorithm (AVOA), a hybrid optimization algorithm model known as MIC_AOAVOA was developed. This model effectively harnesses the strengths of various algorithms to augment search efficiency and optimization capabilities. Strategies for population initialization and adaptive weight adjustments were incorporated to enhance the algorithm's convergence velocity and the quality of solutions. The cauchy opposition-based learning (COBL) and fitness-distance balance (FDB) strategy further refined the composite algorithm, bolstering its global search capabilities and population diversity. Comparative analyses were performed with single algorithm models and traditional BP neural network models, with an in-depth examination of the MIC_AOAVOA_BP model's prediction outcomes. Comprehensive evaluations utilizing error statistics and composite evaluation indicators demonstrated that the model consistently achieved mean absolute percentage error (MAPE) values below 10%, correlation coefficients (R²) exceeding 0.98, and stable mean squared error (MSE) values around 0.0002 across the prediction of three metrics. These results underscore the model's high precision and stability. Consequently, the proposed enhanced model offers a solution that is more stable, accurate, and robust for the prediction of forming quality in self-piercing riveted joints within engineering applications.

A generalized bilevel optimization model for large-scale task scheduling in multiple agile earth observation satellites

Scheduling large-scale tasks within a multi-agile earth observation satellite system poses a formidable challenge. Given the complexity of this optimization problem, the responding solving strategy should exhibit both efficiency and effectiveness in terms of computational time and solution quality. The current strategies can broadly be categorized into all-in-one and two-stage methodologies. The latter, more conducive to real-world scenarios, owing to reducing the problem complexity and practical operational flexibility, dissects this challenge into task allocation and single-satellite scheduling. However, existing two-stage strategies still consider the objectives and constraints at the equivalent level and only adapt to specific algorithms. In this way, the pivotal issues concerning the solution complexity and strategy compatibility remain fundamentally unaddressed. To address this limitation, we proposed a generalized bilevel optimization model called Question-and-Answer model, which establishes two distinct optimization model with different contains and objectives. In this model, the upper questions are highly indispensable in the response from the lower level and constraints are considered separately at two stages. To ascertain the generalization of this framework, we conduct a comprehensive range of experiments employing various proposed strategies and algorithms in two stages. Diverse algorithms ranging from heuristic principles, and evolutionary strategies, to reinforcement learning can be seamlessly combined and integrated within this framework. The results demonstrate that the scale of scenarios does not affect the effectiveness of any amalgamated algorithms within this framework. Furthermore, the transition of upper questions according to the lower answers indeed improves the objectives but concurrently intensify the computational time.

3D path planning for UAV based on A hybrid algorithm of marine predators algorithm with quasi-oppositional learning and differential evolution

3D path planning is a critical requirement for the autonomy of unmanned aerial vehicle (UAV) navigation systems. In this paper, we propose a novel approach, the differential evolution combined marine predators algorithm (DECMPA), specifically tailored to address the UAV 3D path planning problem in complex scenarios. To handle stringent constraints, DECMPA adopts a multi-step approach. Initially, it utilizes quasi-oppositional learning to generate a more uniformly distributed initial population. Throughout the iterative process, it probabilistically generates quasi-oppositional populations and merges them with existing populations, selecting the superior individuals for the next generation to mitigate local optima. Additionally, DECMPA integrates the variance, crossover, and selection processes of differential evolution into the marine predators algorithm iterations. The greedy selection of test and target vectors further accelerates population convergence. Numerical optimization experiments are conducted using the 10 and 20-dimensional CEC 2021 test suite. Compared to other algorithms, DECMPA exhibits superior solution accuracy and convergence speed, resulting in substantial performance enhancement, thus establishing itself as an efficient algorithm. Furthermore, the adoption of spherical vector coordinates in solution encoding ensures higher quality and facilitates the production of feasible solutions. To verify the effectiveness and practicality of the proposed algorithm, comprehensive path planning simulation experiments are conducted. DECMPA is compared against nine other state-of-the-art heuristic algorithms across six 3D scenarios of varying complexity. The experimental results demonstrate DECMPA’s superiority in terms of optimal cost acquisition, solution quality, and stability. In conclusion, DECMPA presents a promising solution for addressing the challenges of UAV 3D path planning in complex environments. Its innovative approach and superior performance underscore its potential for real-world application in autonomous UAV navigation systems.

A novel method for solving dynamic flexible job-shop scheduling problem via DIFFormer and deep reinforcement learning

Due to the dynamic changes of manufacturing environments, heuristic scheduling rules are unstable in dynamic scheduling. Although meta-heuristic methods provide the best scheduling quality, their solution efficiency is limited by the scale of the problem. Therefore, a novel method for solving the dynamic flexible job-shop scheduling problem (DFJSP) via diffusion-based transformer (DIFFormer) and deep reinforcement learning (D-DRL) is proposed. Firstly, the DFJSP is modeled as a Markov decision process, where the state space is constructed in the form of the heterogeneous graph and the reward function is designed to minimize the makespan and maximize the machine utilization rate. Secondly, DIFFormer is used to encode the operation and machine nodes to better capture the complex dependencies between nodes, which can effectively improve the representation ability of the model. Thirdly, a selective rescheduling strategy is designed for dynamic events to enhance the solution quality of DFJSP. Fourthly, the twin delayed deep deterministic policy gradient (TD3) algorithm is adopted for training an efficient scheduling model. Finally, the effectiveness of the proposed D-DRL is validated through a series of experiments. The results indicate that D-DRL achieves better solution quality and higher solution efficiency when solving DFJSP instances.

Deep reinforcement learning for Agile Earth Observation Satellites scheduling problem with variable image duration

The Agile Earth Observation Satellites (AEOS) possess active imaging capabilities, which enable variable image durations during observations. Due to the problem’s high complexity, traditional heuristic algorithms are unable to provide optimal solutions within a feasible timeframe. In this paper, we propose an improved deep reinforcement learning approach (IDRL), including IDRL based on optimal observation quality (IDRL-MQ) and IDRL based on the longest observation duration (IDRL-MD), to solve the multi-objective scheduling problem for AEOS with variable image duration (MO-SPVID). Two different strategies IDRL-MQ and IDRL-MD, were designed to determine the start time and duration of observation. The experimental results demonstrate that IDRL-MD outperforms both IDRL-MQ and the superior heuristic algorithm ALNS-NSGAII in terms of solution quality and solution diversity. The demonstrated effectiveness of the appropriate heuristic strategies provides evidence for their rationality. Furthermore, the results obtained on instances of varying scales indicate that IDRL exhibits a high level of generality and robustness.

Train assignment and handling capacity arrangement in multi-yard railway container terminals: An enhanced adaptive large neighborhood search heuristic approach

Expanding terminal scale and constructing multiple railway handling yards have become popular strategies for leading railway container terminals globally to cope with the ever-increasing handling volume. Although such an approach greatly enhances the terminal’s productivity, it also intensifies handling operations and introduces additional inter-yard interactions, complicating terminal management. To address these challenges, this paper investigates an integrated optimization approach for multi-yard railway container terminals, which feature both rail-road and rail-rail container transshipment operations. The train assignment plan for each yard and the handling capacity arrangement for each train are jointly optimized while considering inter-yard container transits, workload allocation for yards, and safety requirements in train shunting. This problem is formulated as a nonlinear programming model, with the objective to minimize operational delay and service time for each incoming train, and to minimize workload differences and container transit volume among yards. To efficiently solve this problem, this research develops an enhanced adaptive large neighborhood search (EALNS) heuristic, which includes several customized operators and feasibility repair methods, and is further enhanced with a local search method and backtracking mechanism compared to the standard ALNS framework. Computational experiments with different data scales and problem settings demonstrate the superiority of the EALNS in terms of solution quality and stability compared with three other solution methods. Additionally, practical insights for terminal operations are drawn through detailed analysis of different infrastructure configurations, transshipment train characteristics, and unit cost settings.

Telemedicine integration with cloud-based IoT: A literature review approach

This paper discusses the possibilities of integrating cloud-based IoT with telemedicine as the ways to advance the healthcare industry performance and change its context for the better. This paper aims at exploring how these technologies can be integrated to enhance the existing solutions to the present problems of health care access, patient interactions and use of analytical data in practices. Possible ways in which IoT devices and cloud technology can enhance patient care and assist clinicians in their process of arriving at diagnoses are problems that the research seeks to uncover. The method used is a qualitative systematic review of prior studies related to telemedicine, IoT, and cloud computing, accompanied by a thematic analysis to reveal emerging patterns and the gaps in the literature examined in the study. The new model ‘‘Telemedicine Integration with Cloud-Based IoT,’’ was categorized thematically by differentiation of real-time severity, data privacy, patient interaction and technological difficulties. This model considers layers for acquiring IoT data, storing and analysis, a healthcare remote interface, a machine learning based analytics, and security, which offer a holistic way to enhance healthcare services. Real-time IoT, cloud-based storage, AI for early diagnosis, secure telemedicine platforms, data protection, and complete os compatibility. Based on this study, the authors posited that the convergence of IoT and cloud is capable of redesigning telemedicine by delivering better quality, efficient, and accessible healthcare solutions. However, they lack socio-economic factors for example, the feasibility cost for implementing it for the health companies and difficulties faced by the patient in the less-developed regions. The main limitation observed in the current research is related to the barriers hindering its implementation and future studies are suggested to address these challenges by exploring improved cost-effective approaches to overcome these and other barriers and analysing the issue of data security and patients’ privacy regulation. Studies should also aim at the long-term implications on the user satisfaction in order to introduce or show the long term positive and negative effects of cloud IoTs in telemedicine.

Dynamic scheduling for flexible job shop based on MachineRank algorithm and reinforcement learning

This paper investigates the Dynamic Flexible Job Shop Scheduling Problem (DFJSP), which is based on new job insertion, machine breakdowns, changes in processing time, and considering the state of Automated Guided Vehicles (AGVs). The objective is to minimize the maximum completion time and improve on-time completion rates. To address the continuous production status and learn the most suitable actions (scheduling rules) at each rescheduling point, a Dueling Double Deep Q Network (D3QN) is developed to solve this problem. To improve the quality of the model solutions, a MachineRank algorithm (MR) is proposed, and based on the MR algorithm, seven composite scheduling rules are introduced. These rules aim to select and execute the optimal operation each time an operation is completed or a new disturbance occurs. Additionally, eight general state features are proposed to represent the scheduling status at the rescheduling point. By using continuous state features as the input to the D3QN, state-action values (Q-values) for each scheduling rule can be obtained. Numerical experiments were conducted on a large number of instances with different production configurations, and the results demonstrated the superiority and generality of the D3QN compared to various composite rules, other advanced scheduling rules, and standard Q-learning agents. The effectiveness and rationality of the dynamic scheduling trigger rules were also validated.

Graph topology-constrained BILP for optimal PMU placements

In the monitoring and control of power systems, the deployment of Phasor Measurement Units (PMUs) is of paramount importance, yet their high installation costs have limited their widespread application. To address this issue, this paper proposes an innovative method based on graph topology constraints, known as the Graph Topology-constrained Binary Integer Linear Programming (GT-BILP) approach, aimed at achieving the optimal configuration of PMUs. This method not only takes into account cost-effectiveness but also ensures the complete observability of the system. By quantifying the contribution of each node and bus to the system's observability within the network, a novel contribution matrix is constructed. This matrix integrates the dynamic characteristics of network topology and information flow, providing precise decision support for the deployment of PMUs. By combining the matrix with the network's planning model, the GT-BILP model is obtained, which can effectively solve the PMU layout requirements for large power grids. For the special case of zero-injection buses (ZIBs), the Topology Transformation algorithm has been improved, significantly enhancing the solution efficiency and quality. Through simulation experiments on multiple IEEE standard test systems, the effectiveness and superiority of the proposed GT-BILP model have been verified. This research is of significant importance for improving the monitoring and control capabilities of power systems and provides a new perspective for technological advancement in this field.

Quantum Ant Colony Algorithm for Solving the Traveling Salesman Problem: A Theoretical and Practical Analysis

The quantum ant colony algorithm (QACO) is explored as a solution to the traveling salesman problem (TSP), targeting inefficiencies such as slow convergence and local optima entrapment found in traditional ant colony optimization (ACO) methods. By integrating quantum computing elements, specifically quantum rotation gates and qubits, into the ACO framework, QACO demonstrates enhanced efficiency and faster convergence. This paper delves into the foundational principles of quantum computing and their application to refine ACO, effectively solving the TSP. An in-depth analysis is provided, showcasing QACO's advantages over traditional ACO through empirical examples. These examples illustrate QACO's potential to improve path planning for applications within the rapidly growing e-commerce and logistics sectors. The integration of quantum mechanisms facilitates a more dynamic pheromone update process, which significantly reduces the likelihood of premature convergence and enhances solution quality in complex optimization scenarios. This advancement indicates a promising direction for future research in algorithmic optimization in both theoretical and practical applications.

Integrating Differential Evolution into Gazelle Optimization for advanced global optimization and engineering applications

The Gazelle Optimization Algorithm (GOA) is an innovative metaheuristic inspired by the survival tactics of gazelles in predator-rich environments. While GOA demonstrates notable advantages in solving unimodal, multimodal, and engineering optimization problems, it struggles with local optima and slow convergence in high-dimensional and non-convex scenarios. This paper proposes the Hybrid Gazelle Optimization Algorithm with Differential Evolution (HGOADE), which combines Differential Evolution (DE) with GOA to leverage their complementary strengths for addressing limitations. HGOADE initializes a population of candidate solutions using GOA, then enhances these solutions through DE’s mutation and crossover operations. The algorithm subsequently employs GOA’s exploration and exploitation phases to refine the solutions. By integrating DE’s robust exploration capabilities with GOA’s dynamic search patterns, HGOADE aims to improve global and local search performance. The effectiveness of HGOADE is validated through experiments on benchmark functions from the CEC 2017, CEC 2020, CEC 2022 suite, comparing with ten established optimization techniques, including classical GOA, Salp Swarm Algorithm (SSA), Grey Wolf Optimizer (GWO), Gravitational Search Algorithm (GSA), Arithmetic Optimization Algorithm (AOA), Constriction Coefficient-Based Particle Swarm Optimization Gravitational Search Algorithm (CPSOGSA), Sine Cosine Algorithm (SCA), Particle Swarm Optimization (PSO), Biogeography-Based Optimization (BBO), and DE. Additionally, the performance of HGOADE is assessed against prominent winners from CEC competitions, specifically CMA-ES, LSHADEcnEpSin, and LSHADESPACMA, using the CEC-2017 test suite. Statistical analyses using the Wilcoxon Rank Sum Test and Wilcoxon Signed-Rank Test, along with the Weighted Aggregated Sum Product Assessment (WASPAS) method, confirm that HGOADE significantly outperforms existing algorithms in terms of solution quality and convergence speed. HGOADE’s superiority is validated through six complex engineering design problems, demonstrating its higher feasibility and effectiveness than GOA and other methods. This paper addresses GOA’s shortcomings and advances metaheuristic optimization by integrating DE strategies, offering valuable insights and practical applications for global optimization and engineering design.

Power quality solutions for rail transport using AI-based unified power quality conditioners

This paper proposes an artificial intelligence-based Unified Power Quality Conditioner (AI-UPQC) and a unique Dynamic K-factor PI (DKPI) algorithm for PI-UPQC to address power quality issues in electrified railway systems. The widespread use of power electronic converters in modern traction drives has exacerbated problems such as harmonics, voltage fluctuations, and resonance phenomena. The proposed AI-UPQC utilizes artificial neural networks (ANNs) to generate optimal reference signals for controlling series and shunt active power filters. A detailed 25 kV, 50 Hz traction power supply system model is developed in MATLAB/Simulink to evaluate the AI-UPQC's performance. Simulation results demonstrate that the AI-UPQC significantly outperforms conventional PI-controlled UPQCs in reducing voltage and current total harmonic distortion (THD), improving power factor, and providing faster response times under varying load conditions. The AI-UPQC reduced source current THD from 25.16 to 1.12% and load voltage THD from 6.62 to 2.07%. Sensitivity analysis further validates the robustness of the proposed system across different operating parameters. The AI-UPQC shows promise as an effective solution for enhancing power quality in modern electrified railway networks.

A dynamic artificial bee colony for fuzzy distributed energy-efficient hybrid flow shop scheduling with batch processing machines

Distributed energy-efficient hybrid flow shop scheduling problem (DEHFSP) with batch processing machines (BPMs) is rarely considered, let alone DEHFSP with BPMs and uncertainty. In this study, a fuzzy DEHFSP with BPMs at a middle stage and no precedence between some stages is presented, and a dynamic artificial bee colony (DABC) is proposed to simultaneously optimize the total agreement index, fuzzy makespan, and fuzzy total energy consumption. To produce high quality solutions, Metropolis criterion is used, dynamic employed bee phase based on neighborhood structure dynamic selection is implemented, and group-based onlooker bee phase with bidirectional communication is given. Migration operator is also adopted to replace scout bee phase. Extensive experiments are conducted, and the optimal combination of key parameters for DABC is decided by the Taguchi method. Comparative results and statistical analysis show that new strategies of DABC are effective, and DABC is highly competitive in solving the considered fuzzy DEHFSP.

A Biochemistry-Inspired Algorithm for Path Planning in Unmanned Ground Vehicles

Unmanned ground vehicles (UGVs) have gained significant attention due to their extensive applications in both military and civilian sectors. For effective UGV deployment, path planning algorithms must prioritize computational efficiency, solution reliability, and runtime performance while maintaining path quality. Autonomous path planning remains a critical challenge in UGV navigation, as conventional methods, while effective, often suffer from considerable computational overhead. To address this issue, we propose a novel biochemistry-inspired path planning algorithm designed specifically for static grid-based scenarios. MetaPath demonstrates remarkable computational efficiency while maintaining solution quality across different obstacle densities in benchmark environments. Specifically, the algorithm achieves path lengths within ±5% of all benchmark algorithms while dramatically reducing the exploration space, visiting up to 10% of the cells explored by conventional approaches such as A*. This superior efficiency translates into exceptional runtime performance, executing up to 3000 times faster than bio-inspired algorithms like Ant Colony Optimization (ACO) and the Genetic Algorithm (GA), performing nearly three times faster than the widely used A* algorithm, and maintaining competitive performance with efficient algorithms like Breadth-First Search (BFS) and Particle Swarm Optimization (PSO), thereby establishing the algorithm as a highly efficient pathfinding solution. Most notably, MetaPath introduces a novel approach as the first chemistry-inspired pathfinding algorithm, guaranteeing path discovery when one exists within reasonable computational time, a crucial advantage over some benchmark algorithms that may fail to converge or require excessive computational resources in complex scenarios.