Performance Of Heuristics Research Articles

A Distributed Task Allocation Method for Multi-UAV Systems in Communication-Constrained Environments

This paper addresses task allocation to multi-UAV systems in time- and communication-constrained environments by presenting an extension to the novel heuristic performance impact (PI) algorithm. The presented algorithm, termed local reassignment performance impact (LR-PI), consists of an improved task inclusion phase, a novel communication and conflict resolution phase, and a systematic method of reassignment for unallocated tasks. Considering the cooperation in accomplishing tasks that may require multiple UAVs or an individual UAV, the task inclusion phase can build the ordered task list on each UAV with a greedy approach, and the significance value of tasks can be further decreased and conflict-free assignments can be reached eventually. Furthermore, the local reassignment for unallocated tasks focuses on maximizing the number of allocated tasks without conflicts. In particular, the non-ideal communication factors, such as bit error, time delay, and package loss, are integrated with task allocation in the conflict resolution phase, which inevitably exist and can degrade task allocation performance in realistic communication environments. Finally, we show the performance of the proposed algorithm under different communication parameters and verify the superiority in comparison with the PI-MaxAsses and the baseline PI algorithm.

Dynamic Informed Bias RRT*-Connect: Improving Heuristic Guidance by Dynamic Informed Bias Using Hybrid Dual Trees Search

The RRT*-Connect algorithm enhances efficiency through dual tree bias growth, yet this bias can be inherently blind, potentially affecting the algorithm’s heuristic performance. In contrast, the Informed RRT* algorithm narrows the planning problem’s scope by leveraging an informed region, thereby improving convergence efficiency towards optimal solutions. However, this approach relies on the prior establishment of feasible paths. Combining these two algorithms can address the challenges posed by Informed RRT while also accelerating convergence towards optimality, albeit without resolving the issue of blind bias in dual trees.In this paper, we proposed a novel algorithm: Dynamic Informed Bias RRT*-Connect. This algorithm, grounded in potential and explicit informed bias sampling, introduces a dynamical bias points set that guides dual tree growth with precision objectives. Additionally, we enhance the evaluation framework for algorithmic heuristics by introducing two innovative metrics that effectively capture the algorithm’s characteristics. The improvements observed in traditional indicators demonstrate that the proposed algorithm exhibits greater heuristic compared to RRT*-Connect and Informed RRT*-Connect. These findings also suggest the viability of the new metrics introduced in our evaluation framework.

The Impact of PDDL+ Language Features on Planning Performance: An Empirical Analysis on a Real-world Case Study

PDDL+ is an expressive formalism that allows for the use of planning in complex real-world applications. It includes a number of features designed to improve the readability and conciseness of the resulting knowledge models, but that are commonly doubted to have detrimental impact on the performance of domain-independent searches and heuristics. In this paper we empirically assess the impact of such features in a challenging real-world case study.

Efficient retrosynthetic planning with MCTS exploration enhanced A* search

Retrosynthetic planning, which aims to identify synthetic pathways for target molecules from starting materials, is a fundamental problem in synthetic chemistry. Computer-aided retrosynthesis has made significant progress, in which heuristic search algorithms, including Monte Carlo Tree Search (MCTS) and A* search, have played a crucial role. However, unreliable guiding heuristics often cause search failure due to insufficient exploration. Conversely, excessive exploration also prevents the search from reaching the optimal solution. In this paper, MCTS exploration enhanced A* (MEEA*) search is proposed to incorporate the exploratory behavior of MCTS into A* by providing a look-ahead search. Path consistency is adopted as a regularization to improve the generalization performance of heuristics. Extensive experimental results on 10 molecule datasets demonstrate the effectiveness of MEEA*. Especially, on the widely used United States Patent and Trademark Office (USPTO) benchmark, MEEA* achieves a 100.0% success rate. Moreover, for natural products, MEEA* successfully identifies bio-retrosynthetic pathways for 97.68% test compounds.

The Availability Heuristic

Few fields of psychology have undergone such dramatic conceptual changes over the past dozen years as the emerging science of heuristics. Heuristics are effective cognitive processes that consciously or unconsciously ignore part of information. Good heuristics can greatly reduce the time it takes to solve a problem by avoiding some unlikely possibilities, and although heuristic processes tend to find solutions or outcomes that are often valid or correct, they may only be correct, provable, optimal, or accurate sometimes. However, decision making based on heuristics is often sufficient to solve small-scale problems and provide solutions in uncertain situations where complete information is not available. Heuristics rely on shortcuts to provide immediate, efficient and short-term solutions to facilitate timely decision making. People need to understand how cognitive heuristics work in an uncertain world, combined with practical applications or theory, to help analyze the performance and use areas of heuristics, so that they can continue to evolve and improve.

Sampling design methods for making improved lake management decisions

AbstractThe ecological status of lakes is important for understanding an ecosystem's biodiversity as well as for service water quality and policies related to land use and agricultural run‐off. If the status is weak, then decisions about management alternatives need to be made. We assess the value of information of lake monitoring in Finland, where lakes are abundant. With reasonable ecological values and restoration costs, the value of information analysis can be compared with the survey's costs. Data are worth gathering if the expected value from the data exceeds the costs. From existing data, we specify a hierarchical Bayesian spatial logistic regression model for the ecological status of lakes. We then rely on functional approximations and Laplace approximations to get closed‐form expressions for the value of information of a sampling design. The case study contains thousands of lakes. The combinatorially difficult design problem is to wisely pick the right subset of lakes for data gathering. To solve this optimization problem, we study the performance of various heuristics: greedy forward algorithms, exchange algorithms and Bayesian optimization approaches. The value of information increases quickly when adding lakes to a small design but then flattens out. Good designs are usually composed of lakes that are difficult to manage, while also balancing a variety of covariates and geographic coverage. The designs achieved by forward selection are reasonably good, but we can outperform them with the more nuanced search algorithms. Statistical designs clearly outperform other designs selected according to simpler criteria.

Nonsequential Appointment Scheduling With a Random Number of Requests

This article studies an appointment scheduling problem where a service provider dynamically receives appointment requests from a random number of customers. By leveraging the randomness of the number of potential customers, we develop a nonsequential appointment scheduling policy as an alternative to the conventional first-come-first-served (FCFS) policy. This allows for more flexibility in managing appointment scheduling. To calculate the optimal policy, we develop a branch-and-bound algorithm in which the lower bound is estimated using multiple approaches, such as optimality conditions, dynamic programming for calculating FCFS policy, and the shortest path reformulation. Through numerical studies, we observe that nonsequential appointment scheduling is particularly advantageous in systems characterized by highly fluctuating customer numbers or low congestion. In such cases, leaving gaps between appointments for potential future arrivals proves to be a more appropriate strategy. We also evaluate the performance of heuristics proposed in prior literature and provide insights into situations where these heuristics can be effectively applied.

Knacks of Evolutionary Mating Heuristics for Renewable Energy Source–Based Power Systems Signal Harmonics Estimation

Renewable energy sources–based power systems are increasing rapidly every year with higher chances of destabilization and low power quality due to harmonics, subharmonics, and interharmonics. The elimination of the root causes of these harmonics is difficult because harmonics are generated at different levels due to various electronic components. However, the accurate estimation of power signal harmonic can be done and eliminated with a counter signal. Therefore, in this paper, the evolutionary mating heuristic is implied to accurately estimate the parameters of power signal harmonics. The proposed heuristic was tested under multiple scenarios to accurately estimate the phase and amplitude parameters of power signal harmonics at various noise levels. The tuning of the evolutionary mating heuristic is carried out for encountering predator probability and crossover probability Epp <0.8, Cp <0.9. The simulation analysis depicts that the best fitness achieved by the proposed heuristic for noise levels (no) 104, 78, 52, 26, and 13 dB are 2.8169e−11, 9.6969e−09, 3.7703e−06, 1.3501e−03, and 2.8624e−02, respectively, for iteration I = 1249. The comparative analysis shows that the proposed heuristic performance is significantly better than other heuristics.

Dual Bounds from Decision Diagram-Based Route Relaxations: An Application to Truck-Drone Routing

For vehicle routing problems, strong dual bounds on the optimal value are needed to develop scalable exact algorithms as well as to evaluate the performance of heuristics. In this work, we propose an iterative algorithm to compute dual bounds motivated by connections between decision diagrams and dynamic programming models used for pricing in branch-and-cut-and-price algorithms. We apply techniques from the decision diagram literature to generate and strengthen novel route relaxations for obtaining dual bounds without using column generation. Our approach is generic and can be applied to various vehicle routing problems in which corresponding dynamic programming models are available. We apply our framework to the traveling salesman with drone problem and show that it produces dual bounds competitive to those from the state of the art. Applied to larger problem instances in which the state-of-the-art approach does not scale, our method outperforms other bounding techniques from the literature. Funding: This work was supported by the National Science Foundation [Grant 1918102] and the Office of Naval Research [Grants N00014-18-1-2129 and N00014-21-1-2240]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0170 .

Heuristics for online three-dimensional packing problems and algorithm selection framework for semi-online with full look-ahead

In online three-dimensional packing problems (3D-PPs), unlike offline problems, items arrive sequentially and require immediate packing decisions without any information about the quantities and sizes of the items to come. Heuristic methods are of great importance in solving online problems to find good solutions in a reasonable amount of time. However, the literature on heuristics for online problems is sparse. As our first contribution, we developed a pool of heuristics applicable to online 3D-PPs with complementary performance on different sets of instances. Computational results showed that in terms of the number of used bins, in all problem instances, at least one of our heuristics had a better or equal performance compared to existing heuristics in the literature. The developed heuristics are also fully applicable to an intermediate class between offline and online problems, referred to in this paper as a specific type of “semi-online with full look-ahead”, which has several practical applications. In this class, as in offline problems, complete information about all items is known in advance (i.e., full look-ahead); however, due to time or space constraints, as in online problems, items should be packed immediately in the order of their arrival. As our second contribution, we presented an algorithm selection framework, building on developed heuristics and utilizing prior information about items in this specific class of problems. We used supervised machine learning techniques to find the relationship between the features of problem instances and the performance of heuristics and to build a prediction model. The results indicate an 88% accuracy in predicting (identifying) the most promising heuristic(s) for solving any new instance from this class of problems.

Bounds and Heuristics for Multiproduct Pricing

For a large class of demand models that allow for multiple consumer types, we present performance guarantees for simple nonpersonalized pricing heuristics relative to optimal personalized pricing. Our results demonstrate that in a general setting, the effectiveness of pricing along a positive vector depends on how the price vector aligns with optimal personalized price vectors. We propose two positive direction vectors: the “economic” and “robust” directions. The economic direction is a convex combination of the optimal personalized price vectors and aims to do well on average. The robust direction offers the best worst-case performance guarantee. By judiciously selecting pricing directions, our results also provide performance guarantees of simple pricing strategies relative to more sophisticated pricing strategies. In particular, we provide performance guarantees for nonpersonalized, optimal linear pricing relative to optimal nonlinear, personalized pricing. Our research also examines the performance of common heuristics for bundle pricing relative to optimal, personalized, bundle-size pricing. Our experiments show that performance often improves when consumer types are clustered and each cluster is offered a price direction. We compared the performance of the k-means clustering heuristic and the farthest point first clustering heuristic. Our findings indicated that k-means clustering has significantly superior performance on average. This suggests that businesses could potentially benefit from implementing k-means clustering in their pricing strategies. In conclusion, our study offers valuable insights and performance guarantees for various pricing strategies and their relative effectiveness. These findings could inform pricing decisions and potentially lead to improved outcomes for firms. This paper was accepted by Chung-Piaw Teo, optimization. Funding: This work was supported by Collaborative Research Funding (CRF) Hong Kong [Grant (CRF) C6032-21G], RGC Hong Kong [Grant 16502819], and Hong Kong University of Science and Technology. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2021.04173 .

STRUCTURAL INSULATORS AND PROMOTORS IN NETWORKS UNDER GENERIC PROBLEM-SOLVING DYNAMICS

The collective coordination of distributed tasks in a complex system can be represented as decision dynamics on a graph. This abstract representation allows studying the performance of local decision heuristics as a function of task complexity and network architecture. Here, we identify hard-to-solve and easy-to-solve networks in a social differentiation task within the basic model of small-world graphs. We show that, depending on the details of the decision heuristic as well as the length of the added links, shortcuts can serve as structural promotors, which speed up convergence toward a solution, but also as structural insulators, which make the network more difficult to solve. Our findings have implications for situations where, in distributed decision systems, regional solutions emerge, which are globally incompatible as, for example, during the emergence of technological standards.

A powerful heuristic method for generating efficient database systems

Heuristic functions are an integral part of MapReduce software, both in Apache Hadoop and Spark. If the heuristic function performs badly, the load in the reduce part will not be balanced and access times spike. To investigate this problem closer, we run an optimal database program with numerous different heuristic functions on database. We will leverage the Amazon elastic MapReduce framework. The paper investigates on general purpose, implementation, and evaluation of heuristic algorithm for generating optimal database system, checksum, and special heuristic functions. With the analysis, we present the corresponding runtime results. For the coding part, the records counting part is hasty and can only work for local Hadoop part, it can be debugged and optimized for general purpose implement on Hadoop and Spark and turn into an effective performance monitor tool. As mentioned before, there are strange issue, also the performance of BLAKE2s is unexpectedly slow in that it’s widely accepted the performance of BLAKE2s is much better than MD5 and SHA256, we would like to figure out why the common-sense performance of heuristics is deferent from what we got in distributed frameworks.

Incremental accelerated gradient descent and adaptive fine-tuning heuristic performance optimization for robotic motion planning

Robot comprehensive performance optimization is a high dimensional non-convex problem, which is of great significance in the practice of robotic intelligent manufacturing. Existing methods still face many challenges in performance evaluation, local optima, and planning efficiency. In this paper, a novel method based on accelerated gradient descent and directed-mutagenesis genetic algorithm (AGD-DGA) is proposed for efficient robotic motion planning and comprehensive performance optimization. Firstly, the time, energy consumption, and impact (T-E-I) are taken as the optimization objectives. In particular, the power loss sub-model is constructed, which is rarely considered in the previous research on motion optimization, and it is combined with the driving power sub-model to improve the evaluation accuracy. Next, a novel directed-mutagenesis genetic algorithm (DGA) is proposed to address the local optima problem. Compared with existing methods based on random sampling, the DGA further improves the success rate and efficiency by updating the probability model based on the adaptive Gaussian process. Moreover, a hierarchical weight updating mechanism is established to overcome the impact of additional performance indicators on the success rate of collision-free path planning. To improve planning efficiency, an incremental optimization mechanism and an adaptive fine-tuning strategy for population size are proposed to reduce unnecessary sampling, evaluation, and sorting. The simulation and experimental results in different scenarios demonstrate that the proposed AGD-DGA achieves better results than most of the mainstream algorithms in terms of optimization efficiency, success rate, and motion performance. In addition, it is also verified that the modified energy consumption model can achieve an average evaluation accuracy of more than 90% while maintaining the calculation efficiency.

A lamp location problem based on system reliability

ABSTRACT This paper studies one- and two-dimensional lamp location problems considering system reliability. Within the system, several identical lamps are used to cover the system service area, and each lamp can cover a circular area. Lamps could be working or fail, and the system works when working lamps can cover the entire system. System reliability is defined as the probability that the whole area is covered. To check the system state, the Voronoi diagram is used, and the signature method is applied to calculate the system reliability. The primary contribution involves applying a redundancy-based heuristic location strategy to optimize system reliability in lamp system. In method evaluation, the proposed method is compared with other location methods. A genetic algorithm (GA) that employs heuristic solutions as initial input to access the heuristic performance is designed. Finally, numerical experiments suggesting the heuristic method provide a pseudo-optimal solution.

Abstract 12454: Weak Supervision Enables Scalable Post-Market Surveillance on Medical Wearables

Introduction: With the advent of consumer-facing devices that can render atrial fibrillation (AF) pre-diagnosis, medical wearables now have the potential to affect diagnosis rates and medical care. Post-market surveillance is necessary to understand the impact of wearables on patient outcomes and health care utilization, but is hindered by the lack of codified terms in EHR that capture wearable use. Research Questions: Constructing a post-market surveillance system therefore requires a classifier that identifies mentions of AF pre-diagnosis in unstructured EHR data. However, fine-tuning classifiers require large, hand-labeled training sets that can be costly to generate. It is unclear whether a scalable surveillance system could be built for wearables. Methods: Two weakly-supervised approaches were evaluated to reduce clinical note labeling overhead: one using programmatic codes, and the other using prompts to large language models (LLMs). Probabilistically labeled notes were then used to fine-tune a classifier, which identified patients with AF pre-diagnosis mentions. A retrospective cohort study was conducted, where the characteristics and care patterns of patients identified by the classifier were compared against those who did not receive pre-diagnosis. Results: Label model derived from prompt-based labeling heuristics using LLMs (precision = 0.67, recall = 0.83, F1 = 0.74) nearly achieved the performance of code-based heuristics (precision = 0.84, recall = 0.72, F1 = 0.77), while cutting down the cost to create a labeled training set. The classifier learned on the labeled notes accurately identified patients with AF pre-diagnosis (precision = 0.85, recall = 0.81, F1 = 0.83). Those patients who received pre-diagnosis exhibited different demographic and comorbidity characteristics, and were enriched for anticoagulation and eventual diagnosis of AF. At the index diagnosis, existence of pre-diagnosis did not stratify patients on clinical characteristics, but did correlate with anticoagulant prescription. Conclusion: Our work establishes the feasibility of an EHR-based surveillance system for wearable devices that render AF pre-diagnosis. Weakly-supervised approaches can be effective in reducing test set labeling costs.

Transformational Leadership and Heuristic Performance of Teachers Toward Sustainable Professional Development

With the rapid development of society, education plays an increasingly prominent role in promoting social and economic development. Teachers, as the main undertakers of teaching tasks, shoulder the responsibility of training talents. Focusing on teachers' transformational leadership and evaluating their heuristic methods will not only help to promote teachers' role change and professional development but also help to enhance students' academic achievement and promote students' all-around development.

Comprehensive quantity discount model for dynamic green supplier selection and order allocation

We model and solve a deterministic multi-period single-product green supplier selection and order allocation problem in which the considered suppliers’ availability, cost, and green performance change from one period to another in the planning horizon. Moreover, the available suppliers may offer an all-unit or an incremental quantity discount (QD) scheme, resulting in three problem configurations. In one configuration, all suppliers offer all-unit QD. In the second, all suppliers offer incremental QD. In the third, some suppliers offer all-unit QD, and others offer incremental QD. The problem is modeled using a bi-objective integer linear programming formulation that maximizes the total green value of the purchased items from all the suppliers and minimizes their total corresponding cost, including the fixed cost, variable cost, inventory holding cost, and shortage cost. The proposed bi-objective model is scalarized and solved using the branch-and-cut algorithm and a population-based heuristic. A numerical analysis is conducted, which allows first to validate the heuristic approach using small-size instances by comparing its results with those of the exact approach. Moreover, an extensive comparison between the exact and heuristic solution approaches is carried out. The results reveal different findings. First, the economic and environmental solutions of an instance are different, and the environmental solution is independent of the suppliers’ pricing schemes. Second, the maximum difference between the heuristic approach and the exact approach in terms of the bi-objective function value is 4.72%, which makes the proposed heuristic recommended for large-size instances due to its short computation time and good accuracy. Third, there is no difference in terms of the heuristic performance between the combined model and the models with a single type of discount. Fourth, the all-unit discount scheme seems to be generally better in terms of the trade-off between the green value of purchasing and cost.

Redistricting optimization with recombination: A local search case study

In the U.S., states redraw electoral district boundaries every ten years. Given that redistricting affects political representation at both the state and national levels, it is crucial to prevent the manipulation of district boundaries for political gain. Optimization methods can be valuable tools for promoting transparency and fairness in redistricting. Here we examine a novel local search approach for redistricting that transitions between feasible solutions using Recombination (a recently introduced spanning tree iteration). We compare the performance of multiple local search heuristics using both Recombination and more traditional Flip iterations by optimizing congressional plans for Illinois, Missouri, and Tennessee with respect to several common fairness objectives. We evaluate which heuristic produces the best objective value within limited time periods and generate collections of optimized plans. The Recombination heuristics produced excellent objective values, often far superior to the Flip heuristics; they also maintained more compact district shapes when the objective was not compactness. However, the Flip heuristics converged to a local optimum more quickly and occasionally achieved better solutions than the ReCom heuristics within short time periods. Hence, while the use of Recombination within local search frequently improves solution quality, there are some scenarios for which Flip may be preferable.

Machine learning augmented approaches for hub location problems

Hub location problems are widely analyzed in fields of logistic and transportation industry for cost reduction. In this paper, a novel algorithm framework based on machine learning is proposed to improve solution quality of hub location problems for large-scale instances. First, a deep-learning based probabilistic hub-ranker (DLHr) is developed to determine the priority of nodes to be selected as hubs. Next, two node-ranking based approaches DL-CBS and DL-GVNS are developed to augment the DLHr for single allocation hub location problems. DL-CBS is an augment algorithm embedding DLHr-ranking into clustering-based potential hub sets algorithm (CBS) while DL-GVNS embeds DLHr-ranking into general variable neighborhood search (GVNS). The numerical results evidence that DLHr outperforms baselines on the node-ranking task and helps to identify potential hubs. Evaluation on a wide range of experiments shows that DL-CBS and DL-GVNS improve solution quality of single allocation hub location problems compared with vanilla CBS and GVNS, revealing DLHr ranking helps to boost the performance of traditional heuristics.