Heuristic Method Research Articles

An inspired chaos-based estimation-theory optimization for low-density parity-check (LDPC) code decoding

Optimization techniques have emerged as powerful tools for solving complex engineering problems in real-time applications, surpassing traditional heuristic methods. This paper introduces a novel chaos optimization algorithm that integrates the bisection search method to enhance the convergence ability, solution accuracy, and search speed of conventional optimization problems. The proposed algorithm is applied to the design and analysis of a reduced complexity decoding scheme using Low-Density Parity-Check (LDPC) codes. By leveraging the Linear Minimum Mean Square Error (LMMSE) estimation criterion, the proposed method approximates the estimated parameters acquired during the message computation process. This combined approach effectively mitigates error propagation and premature convergence issues. The presented research offers a low-complexity decoding algorithm for LDPC codes, leveraging an optimized chaos bisection search method. The adoption of the LMMSE criterion overcomes data dependency and error propagation concerns during the estimation process. The modified chaos optimization technique, integrated with the bisection search, efficiently determines optimal values from perturbed observations, leading to improved convergence rates and data processing precision by reducing the observation dimension (search space). Simulation results validate the proposed approach, showcasing its ability to mitigate error propagation and premature convergence while maintaining coding gain accuracy. Experimental validation on an FPGA RF-SoC device (model XCZU27DR) demonstrates that the proposed method achieves a maximum throughput of 8.7 Gbps at a clock frequency of 386.72 MHz, with reduced hardware complexity. The experimental verification on an FPGA RF-SoC device highlights the algorithm's minimal hardware complexity, superior decoding throughput, and Bit Error Rate (BER) performance.

Maximizing the net present value of a project under uncertainty: Activity delays and dynamic policies

We study a project with stochastic activity durations and cash flows; we model the uncertainty using discrete scenarios. The project entails precedence-related activities, each of which incurs a cash flow that may be positive (inflow) or negative (outflow). The problem is to find a scheduling policy that maximizes the expected net present value of the project. A scheduling policy decides the starting time of each activity under every possible realization of the unknown parameters. Ideally, one wants to expedite the inflows (e.g., incoming payments), while delaying the outflows (e.g., costs) as much as possible, without violating the project deadline. In this article, we devise an exact and a heuristic method to define policies within two new classes of scheduling policies. The first policy class generalizes all existing static policies in the literature and further illustrates the importance of intentional activity delays from both a theoretical as well as an empirical point of view. Whereas the literature on project scheduling has mainly focused on static policies, we also propose a second class of dynamic policies. We show that dynamic policies outperform static policies by means of extensive computational experiments.

Navigating legal pathways for accelerating urban energy transition: A comprehensive deep analysis of photovoltaic power prediction and policy instruments

This research proposes a navigation method that explores how to accelerate urban energy transformation through legal means from the perspective of energy law, in order to improve the accuracy of photovoltaic output power prediction in sustainable energy transformation. Firstly, a deep learning model based on adversarial autoencoder (AAE) architecture was introduced to predict photovoltaic output power according to clean environment policies. Secondly, in order to effectively fine tune the parameters of AAE, a heuristic optimization method based on the Imperial Competition Algorithm was proposed to optimize the AAE configuration to adapt to the unique characteristics of photovoltaic systems, thereby improving prediction accuracy. In addition, the study analyzed the challenges and opportunities faced by current urban energy transformation, and then proposed sustainable ways to accelerate urban energy transformation from the aspects of formulating transformation plans, promoting technological innovation, promoting green transportation, promoting building energy conservation, and developing circular economy. The synergistic effect of deep learning and heuristic optimization, combined with strict stability and sensitivity assessment, puts this research at the forefront of advancing photovoltaic output power prediction methods, which is of great significance for optimizing renewable energy utilization and grid integration.

Enhancing information freshness in multi-class mobile edge computing systems using a hybrid discipline

Timely status updating in mobile edge computing (MEC) systems has recently gained the utmost interest in internet of things (IoT) networks, where status updates may need higher computations to be interpreted. Moreover, in real-life situations, the status update streams may also be of different priority classes according to their importance and timeliness constraints. The classical disciplines used for priority service differentiation, preemptive and non-preemptive disciplines, pose a dilemma of information freshness dissatisfaction for the whole priority network. This work proposes a hybrid preemptive/non-preemptive discipline under an M/M/1/2 priority queueing model to regulate the priority-based contention of the status update streams in MEC systems. For this hybrid discipline, a probabilistic discretionary rule for preemption is deployed to govern the server and buffer access independently, introducing distinct probability parameters to control the system performance. The stochastic hybrid system approach is utilized to analyze the average age of information (AoI) along with its higher moments for any number of classes. Then, a numerical study on a three-class network is conducted by evaluating the average AoI performance and the corresponding dispersion. The numerical observations underpin the significance of the hybrid-discipline parameters in ensuring the reliability of the whole priority network. Hence, four different approaches are introduced to demonstrate the setting of these parameters. Under these approaches, some outstanding features are manifested: exploiting the buffering resources efficiently, conserving the aggregate sensing power, and optimizing the whole network satisfaction. For this last feature, a near-optimal low-complex heuristic method is proposed.

MODUL KEMAHIRAN BERFIKIR ALGEBRA UNTUK PELAJAR TINGKATAN SATU

Algebraic Thinking Skills (KBA) is one aspect of skills that students need to master in order to solve non-routine problems. These skills are also needed as a basis for preparing students to enter university studies and job fields that require logical and analytical thinking. However, the performance of Malaysian students in solving algebraic problems has not yet reached a satisfactory level according to the TIMSS 2019 and PISA 2018 reports. Thus, the Algebraic Thinking Skills Module (MBA) was developed to foster KBA through three constructs namely i) arithmetic generalization, ii) functions, and iii) modeling. MBA is developed based on heuristic methods through Polya absorption Model and digital bar. A bar model is a rectangular illustration constructed to represent known and unknown quantities and relationships between quantities. The digital bar model refers to the free application of the bar model at the link https://mathsbot.com/manipulatives/bar. The KBA test was constructed and administered as a pre- and post-test to 120 Form One students from a rural school in Sabah. The results of the paired sample t-test showed that there was a significant difference in the mean score between the pre-test and the post-test (t(119) = -17.553, p < .05) after the intervention using MBA. This shows that MBA can improve KBA through heuristic methods. This innovation is expected to be developed in rural schools to form students who think algebraically and are literate in digital technology.

The last-mile delivery vehicle routing problem with handling cost in the front warehouse mode

This paper aims to investigate the vehicle routing problem faced by a logistics service provider that performs the last-mile delivery of large household appliances under the front warehouse mode for multiple enterprises. We formulate the problem as a new variant of the pickup and delivery problem and present a mixed integer programming model for it. To solve this problem heuristically, the neighborhood structures with newly proposed move operators are explored within a variable neighborhood search (VNS) and a randomized tabu thresholding (RTT). Experiments on small instances clearly indicate the effectiveness of the two heuristic methods. The comparative results on large-scale instances show that RTT performs relatively better than VNS. In addition, through comparison with computational results of algorithms from the literature, RTT proves good ability to solve a related problem. Furthermore, we introduce a sample case to explore the benefit of FWM for last-mile delivery and to discuss the impact of inventory in front warehouses, handling cost, and split delivery on the solution.

Electrochemical aging model of lithium-ion battery with impedance output and its parameter sensitivity analysis and identification

The electrochemical model of the battery has gained widespread recognition due to its clear physical interpretability. However, achieving an accurate electrochemical model heavily relies on precise parameter settings. To address this challenge, various model-based parameter optimization methods have been developed. Currently, most research relies on model reduction techniques and heuristic random search methods for parameter identification. These approaches often lead to time-consuming processes and a loss of physical interpretability in the electrochemical model. To overcome these limitations, we propose a novel technical framework that directly parametrizes the electrochemical model. First, we establish a battery electrochemical model that accounts for lithium plating and SEI film thickening, serving as the main focus of this study. Secondly, in order to expedite parameter identification without compromising the model's physical interpretability, we innovate by performing comprehensive sensitivity analysis using voltage and impedance characteristics. This allows us to identify the most influential parameters, which become the key for subsequent research. Finally, to ensure reasonable initialization of finite element computations and to avoid convergence issues in the identification algorithm, we combine machine learning and optimization algorithms into a two-step parameter identification method. By utilizing the proposed method to identify the model parameters, the simulations exhibit high accuracy.

Self-healing radial distribution network reconfiguration based on deep reinforcement learning

Distribution network reconfiguration (DNR) has long been used to minimize losses in normal conditions and restore out-of-service areas in fault situations. However, the integration of renewable resources into power systems has made operating strategies more challenging, requiring fast and responsive approaches to handle abrupt interruptions. Traditional DNR methods rely on mathematical programming, heuristic and meta-heuristic methods that cannot generalize unseen generation and load profiles, and may require significant computational time to converge, posing an overrun risk for real-time execution. Recently, deep reinforcement learning (DRL) has been shown to be effective in optimizing DNR in normal conditions with calculation times of only a few milliseconds. However, previously proposed dynamic DNR approaches are not able to self-heal and restore in fault conditions, where timely restoration is critical for customer satisfaction. To address this gap, we propose a restorative dynamic distribution network reconfiguration (RDDNR) framework that enables continuous reconfiguration after faults, enabling the fast restoration of out-of-service areas. Our proposed framework was tested on two balanced test feeders and demonstrated competitiveness regardless of operating states. By leveraging RDDNR, distribution system operators can effectively restore service in a timely manner, improving overall grid resilience and customer satisfaction.

Advancing signal integrity and application analysis through data-flow mapping

Data-flow mapping is a crucial method in signal processing and optimization, managing data flow within systems. Its essential in signal compensation, particularly in telecommunications, audio processing, and biomedical signal processing. Four main algorithm categories underpin data-flow mapping: heuristics, meta-heuristics, Integer Linear Programming (ILP), and Constraint Satisfaction Problems (CSP). Heuristic and meta-heuristic methods like Genetic Algorithms (GA) and Ant Colony Optimization (ACO) provide approximate solutions, crucial for complex problems. ILP and Branch and Bound (B&B) methods offer precise solutions by exhaustive searches under constraints. CSP focuses on satisfying imposed conditions. These methodologies have practical applications, such as signal compensation in communication systems and improving medical imaging like MRI and ultrasound. Theyre also integrated with machine learning, quantum computing, and specialized hardware for 5G/6G communications and IoT. Real-time processing and noise reduction advancements enhance consumer audio and diverse sectors. In summary, data-flow mapping and its algorithms drive signal processing innovations across domains, with evolving technology integration ensuring their lasting importance.

A branch-and-bound algorithm for the proactive resource-constrained project scheduling problem with a robustness maximization objective

This paper presents a branch-and-bound (B&B) algorithm for the proactive scheduling problem, which incorporates a degree of anticipated variability in practical projects. First, the resource-constrained proactive scheduling model is formulated. Second, solving the resource-unconstrained version of the problem with an adapted Fulkerson algorithm provides an upper bound solution. Then, the subproblems of the model are derived by introducing additional precedence relationships to resolve the resource conflict in the upper bound solution. Third, we solve the resource-constrained proactive scheduling problem with the proposed B&B algorithm. The resource conflicts found in the upper bound solution are resolved by adding additional precedence relationships. According to the problem analysis, two dominance rules are proposed for additional node fathoming with the aim of improving the algorithm. Fourth, a genetic algorithm is designed to provide an effective heuristic method for solving the research problem. Finally, we conduct an extensive computational experiment on 6,000 randomly generated instances. The obtained results show that the B&B algorithm has advantages over CPLEX and the genetic algorithm. In addition, the impacts of the dominance rules and the three key parameters on the computational time and project robustness are analysed.

Compaction elastoplastic damage constitutive model of rock under hydro-mechanical coupling: A heuristic modeling method based on internal state variables theory

Based on the internal state variables (ISV) theory, this paper employs a heuristic modeling approach to investigate the compaction elastoplastic damage constitutive model of rocks under hydro-mechanical coupling. The heuristic modeling method demonstrated herein can seamlessly integrate with existing constitutive theories, facilitating a broader spectrum of research on hydro-mechanical coupling constitutive models. Furthermore, a numerical solution was conducted in the principal axes via stress spectrum decomposition, which can well reflect the distinct characteristics of compaction deformation and the influence of water pressure on rock stress distribution. Utilizing the classic Drucker-Prager (D-P) criterion, the modeling method and numerical algorithm were implemented to derive a hydro-mechanical coupling compaction elastic–plastic damage constitutive model. This contribution expands the application domain of the classic D-P criterion within the context of hydro-mechanical coupling environments. The rationality of the model was confirmed through compression test data under various water pressures and confining pressures. Water pressure increases nonlinear compaction deformation by changing the opening degree of microcracks under low confining pressure, which has not been paid enough attention in existing research. This study thoroughly considers the influence of water pressure on compaction deformation. The developed process of “heuristic modeling method-numerical method-model application” is helpful for enriching the constitutive theory of rock under hydro-mechanical coupling.

Water renewal and stratification modelling in small estuaries

Water renewal and flushing in small, intermittently open or closed estuaries is receiving increasing attention particularly in light of the climate change induced alterations in run-off, wave and sediment transport conditions along coasts. The challenges of predicting the stratification-circulation state and the balance between tidal or freshwater flushing in response to the mouth dynamics of small, wave-dominated estuaries is the focus of the paper. Such predictions are required for determining estuary freshwater requirements or establishing an estuary's capacity to maintain sound water quality under pollutant discharges. Advances in simulating changes in stratification-circulation over long time scales are limited. Instead attention has focused on generating indices of stratification or water quality state using heuristic methods. In this paper, systems dynamics modelling is applied to simulate the non-linear response of the estuary to changes in river and marine water fluxes. The estuary is modelled as a basin with a specified water volume to water level relationship, connected to the sea by a channel with variable sill height, but fixed width. The direction and magnitude of the flow through the mouth determines whether the sill height erodes or accretes and hence the mouth dynamics (see Slinger, 2017). The tidal flux through the mouth co-determines the volumetric exchange of salt, influencing both the stratification state of the estuary and the degree of tidal or freshwater flushing. This is also influenced by run-off. The resulting dynamic balance is captured in two bulk indices, the Estuarine Richardson number and the bulk densimetric Froude number. Using measured data from the Great Brak Estuary, South Africa, the model is calibrated. Model simulations demonstrate the importance of tidal flushing and concomitant mouth breaching for water renewal as freshwater flushing declines under scenarios of increased water abstraction. Although the estuary remains partially mixed, there is increased average salinity and a more uniform the water column. Water releases and mouth breaching bring about a more natural stratification-circulation state, but these effects are short-lived.

Relative Keys: Putting Feature Explanation into Context

Formal feature explanations strictly maintain perfect conformity but are intractable to compute, while heuristic methods are much faster but can lead to problematic explanations due to lack of conformity guarantees. We propose relative keys that have the best of both worlds. Relative keys associate feature explanations with a set of instances as context, and warrant perfect conformity over the context as formal explanations do, whilst being orders of magnitudes faster and working for complex blackbox models. Based on it, we develop CCE, a prototype that computes explanations with provably bounded conformity and succinctness, without accessing the models. We show that computing the most succinct relative keys is NP-complete and develop various algorithms for it under the batch and online models. Using 9 real-life datasets and 7 state-of-the-art explanation methods, we demonstrate that CCE explains cases where existing methods cannot, and provides more succinct explanations with perfect conformity for cases they can; moreover, it is 2 orders of magnitude faster.

Heuristic optimal transport in branching networks

Optimal transport aims to learn a mapping of sources to targets by minimizing the cost, which is typically defined as a function of distance. The solution to this problem consists of straight line segments optimally connecting sources to targets, and it does not exhibit branching. These optimal solutions are in stark contrast with both natural, and man-made transportation networks, where branching structures are prevalent. Here, we discuss a fast heuristic branching method for optimal transport in networks. We also provide several numerical applications to synthetic examples, a simplified cardiovascular network, and the “Santa Claus” distribution network which includes 141[Formula: see text]182 cities around the world, with known location and population.

Optimizing Placement and Scheduling for VNF by a Multi-objective Optimization Genetic Algorithm

Virtual network functions (VNFs) have gradually replaced the implementation of traditional network functions. Through efficient placement, the VNF placement technology strives to operate VNFs consistently to the greatest extent possible within restricted resources. Thus, VNF mapping and scheduling tasks can be framed as an optimization problem. Existing research efforts focus only on optimizing the VNFs scheduling or mapping. Besides, the existing methods focus only on one or two objectives. In this work, we proposed addressing the problem of VNFs scheduling and mapping. This work proposed framing the problem of VNFs scheduling and mapping as a multi-objective optimization problem on three objectives, namely (1) minimizing line latency of network link, (2) reducing the processing capacity of each virtual machine, and (3) reducing the processing latency of virtual machines. Then, the proposed VNF-NSGA-III algorithm, an adapted variation of the NSGA-III algorithm, was used to solve this multi-objective problem. Our proposed algorithm has been thoroughly evaluated through a series of experiments on homogeneous and heterogeneous data center environments. The proposed method was compared to several heuristic and recent meta-heuristic methods. The results reveal that the VNF-NSGA-III outperformed the comparison methods.

Kode Semiotika Roland Barthes pada Antologi Puisi Duka-Mu Abadi Karya Sapardi Djoko Damono

This research is motivated by the use of semiotic codes in poetry that presents ambiguous meanings that are difficult to interpret as a whole. To solve the problem, this research uses semiotic code reading according to Roland Barthes. The material object of this research is a poem entitled Prologue and Saat Sebelum Berangkat contained in the anthology Duka-Mu Abadi by Sapardi Djoko Damono. Methods and techniques of data collection by reading, listening, and recording. While heuristic, hermeneutic, and retroactive methods are used to analyze the data. The results of the analysis show that there are five semiotic codes consisting of prerioactive, hermeneutic, cultural, semantic, and symbolic. The prerioactive code in the Prologue poem is in the form of contemplation and meaning of life, and the realization that a loved one has passed away who then has to carry out a funeral for the corpse in the poem Saat Sebelum Berangkat (SSB). The cultural code of the Prologue poem shows the existence of Christian and Jewish religious culture, as well as the upper middle class in the SSB poem. The symbolic code in the Prologue poem is the use of the pronoun ‘Mu’ which refers to God, the Qain field, and the Gologota hill, while in the SBB poem it is a bouquet of flowers, the full world. The hermeneutic and semantic codes of both poems show the grief and loneliness of being left behind by a loved one. Keywords: Duka-Mu Abadi, Code, Roland Barthes, Semiotics

Grado de usabilidad de plataformas e-learning a través delmétodo de evaluación heurística en estudiantes de universidad pública de Trujillo 2021-2022

The National University of Trujillo offers high quality educational services in its various programmes; due to the health crisis, the institution underwent a major change in the mode of delivery of classes, which became from face-to-face to virtual. This change forced the university population to make use of technological tools such as virtual platforms, but the process of adaptation was very difficult for everyone. Barriers such as the lack of previous culture and the complex manipulation of these systems prevented many students from continuing their studies. In order to address this problem, a study was carried out to determine the degrees of usability of virtual platforms through a heuristic evaluation method with the aim of identifying attributes that could be improved with the support of experts. In order to achieve this, the components that make the correct and simple use of these platforms possible were determined, where the student has the possibility of developing their subjects and being able to understand the use of the platform, which has intuitive characteristics. This research concluded with the evaluation of the grades for each component giving as a result a very significant level in the use of the virtual classroom E-learning.

A novel model for transfer synchronization in transit networks and a Lagrangian-based heuristic solution method

To realize the benefits of network connectivity in transfer-based transit networks, it is critical to minimize transfer disutility for passengers by synchronizing timetables of intersecting routes. We propose a mixed-integer linear programming timetable synchronization model that incorporates new features, such as dwell time determination and vehicle capacity consideration, which have been largely overlooked in the literature at the scheduling stage. We introduce a new concept of pre-planned holding time, called transfer buffer time, to reduce the transfer waiting time, particularly for transfers to low-frequency routes, while taking into account the penalty of extra in-vehicle time for onboard passengers and the possible consequences on headway regularity of a route. We develop a Lagrangian relaxation-based heuristic to obtain high-quality solutions efficiently for large instances. Our experiments on instances with up to 12 transfer nodes in the City of Toronto, with a mixture of low- and high-frequency routes, illustrate the potential benefits of the proposed model over the state of the art. The results indicate that incorporating transfer buffer time, dwell time determination, and vehicle capacity consideration improves model outcomes considerably, also demonstrating the computational efficiency of our Lagrangian-based solution method.

Stochastic ambulance dispatching and routing in mass casualty incident under road vulnerability

This paper investigates the stochastic ambulance dispatching and routing (SADR) problem with multiple casualty collection points in the mass casualty incidents (MCI) under uncertainty of road vulnerability and traffic congestion caused by an earthquake. A two-stage stochastic mixed-integer nonlinear programming model is formulated to derive a reliable and high-quality ambulance scheduling, dispatching and routing solution for maximizing expected number of survivors of all casualties. An integrated simulation optimization approach combining a data-driven travel time scenario generation algorithm, sample average approximation, and a column-generation-based heuristic method is developed to efficiently solve this complicated two-stage SADR model with a nonlinear objective function and continuous travel time distributions. Collaborating with the National Science and Technology Center for Disaster Reduction (NCDR), an empirical study using a potential real-world earthquake scenario occurring in Taiwan is conducted to demonstrate the usefulness and effectiveness of our proposed model and solution approach compared to the deterministic model and two current-practice heuristics.

MRI-CE: Minimal rare itemset discovery using the cross-entropy method

Rare itemsets have been studied less extensively than frequent itemsets, but have important potential applications in black swan events, like detecting anomalies. Mining rare itemsets poses two challenges: too many results may be obtained, and the process may incur a high computational overhead. To overcome these two challenges, we can attempt to mine minimal rare itemsets (MRIs) and use heuristic methods to mine approximate results instead of exact results. This paper describes a novel algorithm for mining MRIs using cross-entropy (CE). We present the modeling method for MRI-CE and introduce a progressive checking strategy that enables more MRIs to be discovered in each iteration. The discovered MRIs are then used to update a probability vector. We design two optimization strategies to improve the algorithm's performance. The adaptive sample size strategy narrows the search space as the number of iterations increases, and the crossover-based individual generation strategy improves the diversity of the samples. To evaluate the performance of MRI-CE, we select six competitive algorithms and conduct extensive experiments on publicly available datasets. The results show that the proposed algorithm is not only efficient, but also highly accurate. Furthermore, we verify the effectiveness of the two optimization strategies experimentally.