Planning Optimization Algorithm Research Articles

A multi-strategy improved rime optimization algorithm for three-dimensional USV path planning and global optimization

The RIME optimization algorithm (RIME) represents an advanced optimization technique. However, it suffers from issues such as slow convergence speed and susceptibility to falling into local optima. In response to these shortcomings, we propose a multi-strategy enhanced version known as the multi-strategy improved RIME optimization algorithm (MIRIME). Firstly, the Tent chaotic map is utilized to initialize the population, laying the groundwork for global optimization. Secondly, we introduce an adaptive update strategy based on leadership and the dynamic centroid, facilitating the swarm's exploitation in a more favorable direction. To address the problem of population scarcity in later iterations, the lens imaging opposition-based learning control strategy is introduced to enhance population diversity and ensure convergence accuracy. The proposed centroid boundary control strategy not only limits the search boundaries of individuals but also effectively enhances the algorithm's search focus and efficiency. Finally, to demonstrate the performance of MIRIME, we employ CEC 2017 and CEC 2022 test suites to compare it with 11 popular algorithms across different dimensions, verifying its effectiveness. Additionally, to assess the method's practical feasibility, we apply MIRIME to solve the three-dimensional path planning problem for unmanned surface vehicles. Experimental results indicate that MIRIME outperforms other competing algorithms in terms of solution quality and stability, highlighting its superior application potential.

Application of Many-Objective Arithmetic Optimization Algorithm and TOPSIS for Optimal Planning of DGS in Distribution Systems

The traditional planning of distribution networks is changing because of the accelerated expansion of distributed generation (DG) technologies in various capacities and forms. However, the improper integration of DGs in current distribution networks can give rise to several technical difficulties despite the advantages provided by distributed generation technologies. This paper presents the optimal DG planning in the distribution system using a Pareto-based many-objective arithmetic optimization algorithm (MOAOA) for optimal DG planning problems in the distribution system. This work focuses on improving four technical metrics related to distribution systems: mitigation of electrical energy not served (EENS), total voltage deviation (TVD) minimization, voltage stability index (VSI) maximization, and energy loss mitigation. Two scenarios are considered: the first scenario primarily focuses on optimal planning of DGs supporting active power only (e.g. Micro-Turbines DGs), and the second scenario focuses on optimal planning of DGs supporting both active and reactive power support (e.g. BIOMASS DGs). The optimal Pareto fronts between the competing objectives are generated using the Pareto-based MOAOA algorithm. The TOPSIS (a technique for order performance by similarity to ideal solution) multi-criteria decision-making technique is utilized for selecting the best trade-off solution from the optimal Pareto front. The posited method is examined on two standard IEEE-69 bus distribution systems. The efficacy of the MOAOA is compared with the outcomes of MOPSO, MOGWO and NSGA-II.

A hybrid optimization algorithm for multi-agent dynamic planning with guaranteed convergence in probability

The paper aims to solve the problem of multi-agent path planning in complex environment using optimization algorithm. To address the issue of local optimum and premature convergence, a new method is proposed based on the whale optimization algorithm, combining the chaotic initialization, the reverse search and the differential evolution methods. It is theoretically proved that this algorithm is globally convergent in probability. When applied to path planning problems, the proposed optimization algorithm can effectively find a globally optimal and smoother path. Through simulation experiments with multi-UAVs, it is demonstrated that the proposed algorithm has better performance than the state-of-the-art methods in environment with both static and dynamic obstacles, reflecting the global convergence and robustness of the proposed algorithm.

An enhanced ant colony optimization algorithm for global path planning of deep-sea mining vehicles

Due to the complex variations in slope within deep-sea mining areas, effective path planning for mining vehicle operations is crucial for minimizing energy consumption. However, traditional ant colony algorithms (ACO) neglect the effect of a terrain slope in mining areas. Additionally, these algorithms exhibit limitations such as slow convergence and susceptibility to local optima. To address these issues, this study proposes an enhanced ant colony algorithm, called DYACO, for mining vehicle path optimization. This algorithm dynamically adjusts heuristic information, pheromone volatilization factor, pheromone update strategy, and state transition probability during the iterative process to enhance traditional ACO. Simulation experiments were conducted to comprehensively assess the proposed model, revealing that DYACO not only generates optimal solutions but also demonstrates significant advantages in terms of convergence speed and turning times. Furthermore, DYACO converts the time required for mining vehicles to traverse different slope regions into distances, then incorporating slope effects to path planning for deep-sea mining vehicles. In comparison to ACO, DYACO achieves a 15.3% reduction in the length of an optimal path and a 70.0% decrease in the number of turn times.

Hybrid genetic ant colony optimization algorithm for full-coverage path planning of gardening pruning robots

Hybrid genetic ant colony optimization algorithm for full-coverage path planning of gardening pruning robots

A*–Ant Colony Optimization Algorithm for Multi-Branch Wire Harness Layout Planning

The planning of multi-branch cable harness layouts holds significant practical importance in aircraft industrial contexts, yet it has received limited attention in prior research. This study aims to address the matter concerning the significance of managing multiple constraints and preventing loops. It formulates the problem as an optimization problem in 3D free-form space and resolves it using an extended A* path planning approach in combination with the ant colony optimization algorithm. Initially, a feasible search space for wiring is established through the repair and simplification of the input CAD model. Subsequently, the topology of a multi-branched wiring harness is identified, taking into account industrial requirements related to cable physics, turning, support, bundling, and electromagnetic compatibility constraints. Specifically, the disassembly or merging of branches and loops is employed to avoid wire loops. Ultimately, we propose an A*–ant colony optimization algorithm (A*-ACO) with an enhanced heuristic function for neighboring points, incorporating a concentration increment model. Experimental tests illustrate the effectiveness of this approach in minimizing wire loops and reducing the total cable layout cost, considering factors such as length, bundling, and turning costs. It results in a reduction of 67.0%, 68.5%, and 51.1% compared to A*, ACO, and manual wiring methods, respectively.

A PSO-based energy-efficient data collection optimization algorithm for UAV mission planning.

With the development of the Internet of Things (IoT), the use of UAV-based data collection systems has become a very popular research topic. This paper focuses on the energy consumption problem of this system. Genetic algorithms and swarm algorithms are effective approaches for solving this problem. However, optimizing UAV energy consumption remains a challenging task due to the inherent characteristics of these algorithms, which make it difficult to achieve the optimum solution. In this paper, a novel particle swarm optimization (PSO) algorithm called Double Self-Limiting PSO (DSLPSO) is proposed to minimize the energy consumption of the unmanned aerial vehicle (UAV). DSLPSO refers to the operational principle of PSO and incorporates two new mechanisms. The first mechanism is to restrict the particle movement, improving the local search capability of the algorithm. The second mechanism dynamically adjusts the search range, which improves the algorithm's global search capability. DSLPSO employs a variable population strategy that treats the entire population as a single mission plan for the UAV and dynamically adjusts the number of stopping points. In addition, the proposed algorithm was also simulated using public and random datasets. The effectiveness of the proposed DSLPSO and the two new mechanisms has been verified through experiments. The DSLPSO algorithm can effectively improve the lifetime of the UAV, and the two newly proposed mechanisms have potential for optimization work.

Optimal planning and scheduling of information processes during interaction among mobile objects

The purpose of the research was to improve the quality of information process control in the interaction of mobile object grouping. This proposal is based on developing and implementing new combined models with algorithms for optimal planning and scheduling of information processes during interaction among mobile objects. The proposed approach would guarantee the required level of aircraft maintenance at the airport. The novel aspect of the proposed solution is that it divides the spatially distributed task of selecting a plan and a schedule for information processes in mobile objects into two subtasks, describing the spatial and temporal features of the original task. The spatial component of the original problem is described as a static model. Using this model, the optimal distribution of information operations over the resources of mobile objects is carried out without binding these operations to time. The time component is described as a dynamic model, within which the information operations are tied to time and the corresponding resources. Examples of construction and research of synthesized plans and schedules are given. Abbreviations: DM, Dynamic model; FIFO, First-In First-Out; Gr, Grouping; HSC, Hardware and software complex; ITS, Intelligent Transport System; IP, Information Processes; MO, Mobile object; PC, Programmed control; SM, Static model; SMS, Special mathematic support; LDM, Logical-dynamic models

A novel parallel ant colony optimization algorithm for mobile robot path planning.

With the continuous development of mobile robot technology, its application fields are becoming increasingly widespread, and path planning is one of the most important topics in the field of mobile robot research. This paper focused on the study of the path planning problem for mobile robots in a complex environment based on the ant colony optimization (ACO) algorithm. In order to solve the problems of local optimum, susceptibility to deadlocks, and low search efficiency in the traditional ACO algorithm, a novel parallel ACO (PACO) algorithm was proposed. The algorithm constructed a rank-based pheromone updating method to balance exploration space and convergence speed and introduced a hybrid strategy of continuing to work and killing directly to address the problem of deadlocks. Furthermore, in order to efficiently realize the path planning in complex environments, the algorithm first found a better location for decomposing the original problem into two subproblems and then solved them using a parallel programming method-single program multiple data (SPMD)-in MATLAB. In different grid map environments, simulation experiments were carried out. The experimental results showed that on grid maps with scales of 20 $ \times $ 20, 30 $ \times $ 30, and 40 $ \times $ 40 compared to nonparallel ACO algorithms, the proposed PACO algorithm had less loss of solution accuracy but reduced the average total time by 50.71, 46.83 and 46.03%, respectively, demonstrating good solution performance.

A Lightning Search Algorithm for Optimal Planning of Power Systems With Short/Long-Term Storage

A Lightning Search Algorithm for Optimal Planning of Power Systems With Short/Long-Term Storage

Formulating an optimization algorithm for optimal planning and assessment of rule-based hybrid load following micro-combined heat and power systems

This manuscript presents a logical-based method with a constrained programming approach for making use of the principle. The proposed optimal approach is the improved chameleon swarm algorithm (ICSA). The navigating and hunting behavior of CSA is improved with the crossover and mutation processes, and it is defined as the ICSA approach. The proposed hybrid approach is the number one power consumption, greenhouse gas, and total annual cost (TAC), in addition to including conditions largely based entirely on the monitoring approach, which is a mixture of those standards. These three power-corresponding parameters have been exploited to examine the overall performance of every method corresponding to the absorbed power providers employing the construction call for requests. The calculation of the established effectiveness for four varieties of residents’ homes suggests the not-infrequent place hybrid method. This increases the exploitation of the cogeneration system as well as the proposed method because the most appropriate techniques include measures capable of obtaining excellent conciliation answers done with TAC at 55%, primary energy consumption (PEC) at 14%, and greenhouse generation (GHG) at 9% discounts correspondingly. However, the proposed method obtains optimum discounts in TAC and optimum overall performance for power matching by utilizing identical boundaries for GHG and PEC in assessment to the always infrequent place hybrid technique. The prey-in cost for energy era with nearly 60 in TAC is perceived as the maximum leading monetary constraint for sensibility evaluation when micro-cogeneration units use over 3% gain and are the most disrupted outcome for randomly evaluated.

Improved multi-objective brain storm optimization algorithm for RFID network planning

Improved multi-objective brain storm optimization algorithm for RFID network planning

An improved spider monkey optimization algorithm for multi-objective planning and scheduling problems of PCB assembly line

In recent years, the demand for printed circuit boards (PCBs) with various electronic components has tremendously increased due to high customization in smart electronic devices. To meet the market competition, efficient and realistic decisions with optimum planning and scheduling are required to enhance the productivity of PCB industries. Processing time of surface mounting technology (SMT) machines defines the pace of PCB production because of time consuming and critical process. In addition, it is observed that various planning and scheduling problems exist in PCB assembly line which are required to emphasize to increase the productivity. Therefore, in this study critical planning and scheduling problems i.e., component allocation problem (CAP) and component placement sequence problem (CPSP) are considered to optimize. Moreover, the periodic maintenance (PM) is also considered to maintain the efficiency of machines and make the problem more realistic. A mathematical model is formulated to minimize the maximum completion time, total energy consumption and the total maintenance time simultaneously. To solve this multi-objective NP-hard problem, an improved spider monkey optimization (ISMO) algorithm is developed. The efficiency and effectiveness of ISMO algorithm is improved by embedding few additional features. a) Two heuristics to ensure better initial solutions; b) evolutionary state judgment based on change in Pareto entropy to ensure the tradeoff between exploration and exploitation; c) an archive-based Q-Learning strategy to ensure the parametric adaptive tuning. Finally, computational experiments and comprehensive performance comparisons with other well-known multi-objective algorithms are performed with several size problem instances. The statistical results and analysis demonstrate that the proposed ISMO algorithm outperforms the other competitors.

Fully automated volumetric modulated arc therapy technique for radiation therapy of locally advanced breast cancer

BackgroundThis study aimed to evaluate an a-priori multicriteria plan optimization algorithm (mCycle) for locally advanced breast cancer radiation therapy (RT) by comparing automatically generated VMAT (Volumetric Modulated Arc Therapy) plans (AP-VMAT) with manual clinical Helical Tomotherapy (HT) plans.MethodsThe study included 25 patients who received postoperative RT using HT. The patient cohort had diverse target selections, including both left and right breast/chest wall (CW) and III-IV node, with or without internal mammary node (IMN) and Simultaneous Integrated Boost (SIB). The Planning Target Volume (PTV) was obtained by applying a 5 mm isotropic expansion to the CTV (Clinical Target Volume), with a 5 mm clip from the skin. Comparisons of dosimetric parameters and delivery/planning times were conducted. Dosimetric verification of the AP-VMAT plans was performed.ResultsThe study showed statistically significant improvements in AP-VMAT plans compared to HT for OARs (Organs At Risk) mean dose, except for the heart and ipsilateral lung. No significant differences in V95% were observed for PTV breast/CW and PTV III-IV, while increased coverage (higher V95%) was seen for PTV IMN in AP-VMAT plans. HT plans exhibited smaller values of PTV V105% for breast/CW and III-IV, with no differences in PTV IMN and boost. HT had an average (± standard deviation) delivery time of (17 ± 8) minutes, while AP-VMAT took (3 ± 1) minutes. The average γ passing rate for AP-VMAT plans was 97%±1%. Planning times reduced from an average of 6 h for HT to about 2 min for AP-VMAT.ConclusionsComparing AP-VMAT plans with clinical HT plans showed similar or improved quality. The implementation of mCycle demonstrated successful automation of the planning process for VMAT treatment of locally advanced breast cancer, significantly reducing workload.

Developing a primal-dual algorithm for optimal planning of groundwater resources

Abstract The exploitation of water resources to provide water for agriculture follows methods that increase the efficiency of adopted policies. One of the effective ways to improve the efficiency of these systems is to evaluate the role of flow estimation in improving performance indicators such as reliability and vulnerability. In the correlation of water, energy and food, the purpose of decision-making is to achieve a balance between water extraction and energy consumption, which will lead to a reduction in the risk of supplying the water needed by the plant during periods of drought stress. In this article, a decision-making method using discrete wavelet transform and primal-dual algorithm is introduced to estimate the amount of monthly groundwater extraction. The proposed model has been evaluated by the Nash–Sutcliffe method and the mean squared error and optimized to increase the reliability of agricultural water supply. The results indicate the strong role of the accuracy of the proposed method in the efficiency of the aforementioned policies, as it has shown an 8% increase in reliability.

Deep Learning-Based Dose Prediction Model for Automated Spot-Scanning Proton Arc Planning

Spot-scanning proton arc (SPArc) is a novel technique that employs a planning optimization algorithm to select the energies and positions of spots along a dynamic rotational arc trajectory. The SPArc technique has the potential to achieve superior dose conformality and treatment delivery efficiency over intensity-modulated proton therapy. However, creating such a SPArc plan using existing approaches is time-consuming and computationally extensively. This study investigated the feasibility of using the deep learning (DL) technique to predict the 3D dose distribution of the SPArc treatment plan, leveraging the prior knowledge acquired from conventional intensity-modulated radiation therapy (IMRT) plans. A DL model, 3D-Unet with residual connections and attention gates, was trained using an open-source database of CT images, critical structures, and IMRT plans from 340 head and neck cancer patients (HNC) as the base model. Transfer learning technique was applied to fine-tune the model parameters using the SPArc treatment plans created on the HNC patients from an in-house dataset, where the SPArc treatment plans (including control point sampling, energy layer distribution, arc trajectory, etc.,) were optimized using a previously developed iterative approach. The performance of the DL model was evaluated by comparing predicted and planned doses over 17 SPArc treatment plans by using 4-fold cross-validation. The SPArc planning time per patient was 8∼12 hours, while the dose prediction time was reduced to 2∼3 minutes using the proposed DL model. The deviation of D95 in the target was (-1.8±1.6) %. The deviation of the mean dose in the parotids, cord, mandible, and brainstem were (2.5±6.5) %, (-0.5±4.3) %, (1.4±3.9) %, and (3.4±8) % of the prescription, respectively. The dice similarity coefficients of the 80%, 70%, and 60% isodose lines were (0.9±0.09), (0.93±0.01), and (0.94±0.01), respectively. Our results demonstrate that a DL-based dose prediction model can be created with a limited number of SPArc treatment plans through transfer learning. The DL model can directly predict the 3D dose distribution in minutes for automated planning. This study paves the roadmap to develop a quick clinical decision platform for the optimal selection among the multi-treatment modalities.

Graph-based robot optimal path planning with bio-inspired algorithms

Recently, bio-inspired algorithms have been increasingly explored for autonomous robot path planning on grid-based maps. However, these approaches endure performance degradation as problem complexity increases, often resulting in lengthy search times to find an optimal solution. This limitation is particularly critical for real-world applications like autonomous off-road vehicles, where high-quality path computation is essential for energy efficiency. To address these challenges, this paper proposes a new graph-based optimal path planning approach that leverages a sort of bio-inspired algorithm, improved seagull optimization algorithm (iSOA) for rapid path planning of autonomous robots. A modified Douglas–Peucker (mDP) algorithm is developed to approximate irregular obstacles as polygonal obstacles based on the environment image in rough terrains. The resulting mDP-derived graph is then modeled using a Maklink graph theory. By applying the iSOA approach, the trajectory of an autonomous robot in the workspace is optimized. Additionally, a Bezier-curve-based smoothing approach is developed to generate safer and smoother trajectories while adhering to curvature constraints. The proposed model is validated through simulated experiments undertaken in various real-world settings, and its performance is compared with state-of-the-art algorithms. The experimental results demonstrate that the proposed model outperforms existing approaches in terms of time cost and path length.

Path Planning Algorithm for a Wheel-Legged Robot Based on the Theta* and Timed Elastic Band Algorithms

Aimed at the difficulty of path planning resulting from the variable configuration of the wheel-legged robot for future deep space explorations, this paper proposes a path planning algorithm based on the Theta* algorithm and Timed Elastic Band (TEB) algorithm. Firstly, the structure of the wheel-legged robot is briefly introduced, and the workspace of a single leg is analyzed. Secondly, a method to judge complete obstacles and incomplete obstacles according to the height of the obstacles is proposed alongside a method to search for virtual obstacles, to generate a grid map of the wheel and a grid map of the body, respectively. By dividing obstacles into complete obstacles and incomplete obstacles, the path planning of the wheel-legged robot is split into the planning of the body path and the planning of the wheel path. The body can be still simplified as a point by searching for the virtual obstacle, which avoids the difficulty of a planning path of a variable shape. Then, we proposed hierarchical planning and multiple optimization algorithms for the body path and wheel path based on the Theta* algorithm and TEB algorithm. The path can be optimized and smoothed effectively to obtain a shorter length and higher safety. On that basis, the proposed algorithm is simulated by Matlab. The results of simulations show that the algorithm proposed in this paper can effectively plan the path of the wheel-legged robot by using variable configurations for different types of obstacles. The path-planning algorithm of the wheel-legged robot proposed in this paper has a broad prospect for deep space exploration.

Technology Upgrade Assessment for Open-Pit Mines through Mine Plan Optimization and Discrete Event Simulation

Digital technologies are continually gaining traction in the mining and mineral processing industries. Several studies have shown the benefits of their application to help improve various aspects of the mineral value chain. Nevertheless, quantitatively assessing new technologies using a holistic approach is vital to evaluate whether the potential localized benefits ultimately translate to an overall increase in project net present value (NPV). This study develops an integrated system-wide methodology for open-pit mines, supporting the technoeconomic assessment of implementing new technology that impacts strategic and operational timeframes. The first part of the framework relies on a state-of-the-art mine plan optimization algorithm that incorporates geological uncertainty. The resulting outputs are then fed into the discrete event simulation portion of the framework (second part) to maximize plant throughput using alternate modes of operation (blending strategy) and operational stockpiles to deal with unexpected changes in ore feed attributes. Sample calculations loosely based on a gold deposit located in the Maricunga belt, Chile, are presented in the context of evaluating different intelligent ore sorting technology options.

UAV Network Path Planning and Optimization Using a Vehicle Routing Model

Unmanned aerial vehicle (UAV) remote sensing has been applied in various fields due to its rapid implementation ability and high-resolution imagery. Single-UAV remote sensing has low efficiency and struggles to meet the growing demands of complex aerial remote sensing tasks, posing challenges for practical applications. Using multiple UAVs or a UAV network for remote sensing applications can overcome the difficulties and provide large-scale ultra-high-resolution data rapidly. UAV network path planning is required for these important applications. However, few studies have investigated UAV network path planning for remote sensing observations, and existing methods have various problems in practical applications. This paper proposes an optimization algorithm for UAV network path planning based on the vehicle routing problem (VRP). The algorithm transforms the task assignment problem of the UAV network into a VRP and optimizes the task assignment result by minimizing the observation time of the UAV network. The optimized path plan prevents route crossings effectively. The accuracy and validity of the proposed algorithms were verified by simulations. Moreover, comparative experiments with different task allocation objectives further validated the applicability of the proposed algorithm for various remote sensing applications