Hard Problem Research Articles

Identifying the cardinality-constrained critical nodes with a hybrid evolutionary algorithm

Identifying the critical nodes in a network is crucial for understanding its characteristics, controlling its structure, and determining its functionality. Cardinality-constrained CNP (CC-CNP) is a nondeterministic polynomial-time (NP)-hard combinatorial optimization problem that refers to minimizing a set of nodes such that after deletion, the size of the largest connected component in the residual subgraph is smaller than a prescribed value. CC-CNP is applicable to a variety of fields, such as epidemic and infectious disease control, electric power network construction and maintenance, and traffic network control.In this work, we present a multistage local search (MSLS) algorithm for generating high-quality initial solutions for CC-CNP, where two strategies, circular node deletion and node change and the tabu search-based first in, first out (FIFO) principle, are utilized to prevent search detours. Then, a population-based strategy is incorporated, resulting in a genetic algorithm-based multistage local search algorithm (GAMSLS) that adopts a genetic algorithm framework, refines the initial solutions in the crossover process, and utilizes a new population update strategy to ensure the diversity and individual quality of the population. The proposed algorithm is evaluated on 75 network instances and is shown to outperform state-of-the-art algorithms for CC-CNP.

A self-sustaining serpentinization mega-engine feeds the fougerite nanoengines implicated in the emergence of guided metabolism.

The demonstration by Ivan Barnes et al. that the serpentinization of fresh Alpine-type ultramafic rocks results in the exhalation of hot alkaline fluids is foundational to the submarine alkaline vent theory (AVT) for life's emergence to its 'improbable' thermodynamic state. In AVT, such alkaline fluids ≤ 150°C, bearing H2 > CH4 > HS--generated and driven convectively by a serpentinizing exothermic mega-engine operating in the ultramafic crust-exhale into the iron-rich, CO2> > > NO3--bearing Hadean ocean to result in hydrothermal precipitate mounds comprising macromolecular ferroferric-carbonate oxyhydroxide and minor sulfide. As the nanocrystalline minerals fougerite/green rust and mackinawite (FeS), they compose the spontaneously precipitated inorganic membranes that keep the highly contrasting solutions apart, thereby maintaining redox and pH disequilibria. They do so in the form of fine chimneys and chemical gardens. The same disequilibria drive the reduction of CO2 to HCOO- or CO, and the oxidation of CH4 to a methyl group-the two products reacting to form acetate in a sequence antedating the 'energy-producing' acetyl coenzyme-A pathway. Fougerite is a 2D-layered mineral in which the hydrous interlayers themselves harbor 2D solutions, in effect constricted to ~ 1D by preferentially directed electron hopping/tunneling, and proton Gröthuss 'bucket-brigading' when subject to charge. As a redox-driven nanoengine or peristaltic pump, fougerite forces the ordered reduction of nitrate to ammonium, the amination of pyruvate and oxalate to alanine and glycine, and their condensation to short peptides. In turn, these peptides have the flexibility to sequester the founding inorganic iron oxyhydroxide, sulfide, and pyrophosphate clusters, to produce metal- and phosphate-dosed organic films and cells. As the feed to the hydrothermal mound fails, the only equivalent sustenance on offer to the first autotrophs is the still mildly serpentinizing upper crust beneath. While the conditions here are very much less bountiful, they do offer the similar feed and disequilibria the survivors are accustomed to. Sometime during this transition, a replicating non-ribosomal guidance system is discovered to provide the rules to take on the incrementally changing surroundings. The details of how these replicating apparatuses emerged are the hard problem, but by doing so the progenote archaea and bacteria could begin to colonize what would become the deep biosphere. Indeed, that the anaerobic nitrate-respiring methanotrophic archaea and the deep-branching Acetothermia presently comprise a portion of that microbiome occupying serpentinizing rocks offers circumstantial support for this notion. However, the inescapable, if jarring conclusion is drawn that, absent fougerite/green rust, there would be no structured channelway to life.

Sleep-wakeup scheduling algorithm for lifespan maximization of directional sensor networks: a discrete cuckoo search optimization algorithm

Directional sensor networks (DSNs) are ad-hoc networks which are utilized in different industrial applications. Their usual engagements are to monitor and to perform the coverage of all specific targets in the observing fields permanently. These kinds of networks include numerous configurable directional sensors in which they can be utilized in one of the possible directions along with the one of their adjustable ranges. Although the energy harvesting methodology is being applied for these battery-hungry applications, the battery management and network lifetime maximization are still prominent challenges. In this paper, the network lifetime extension is formulated to a discrete optimization problem which is a famous non-deterministic polynomial time hardness (NP-Hard) problem. To solve this combinatorial problem, a discrete cuckoo search algorithm (D-CSA) is designed and is called in several rounds. A cover is a sub set of configured sensors capable of monitoring all targets in the observing field. In each round, the most efficient cover is constituted along with its activation time. In the determined activation time, the sensors in the cover are scheduled in wakeup mode whereas others are set in sleep mode to save energy. Despite other meta-heuristic algorithms, this proposed algorithm utilizes the novel defined discrete walking around procedures that makes to reach a good balance between exploration and exploitation in this complex search space. The proposed algorithm has been tested in different scenarios to be evaluated. The simulation results in the variety circumstances prove the superiority of the proposed algorithm is about 20.29%, 19.55%, 14.40%, 14.51%, 7.70% and 8.03% in term of average lifespan improvement against H-MNLAR, Hm-LifMax-BC, GA, ACOSC, H-GATS, and HDPSO algorithms, respectively. The results also show the high potential scalability of the proposed algorithm.

MIP-based heuristics for combinatorial design of reconfigurable rotary transfer machines for production of multiple parts

This paper deals with a problem of the optimal configuration of a rotary transfer machine with turrets for machining multiple parts. This is a hard combinatorial optimization problem appearing at the preliminary machine design stage. Such machines are multi-positional, i.e. parts are sequentially machined on several working positions. At each working position, several machining modules (spindle heads) can be installed to process the tasks assigned to this position. Machining modules are activated sequentially or simultaneously. Sequential activation is realized by the use of turrets. Simultaneous activation is possible if machining modules are related to the different sides of the part, and if they can work in parallel. There are horizontal and vertical spindle heads, and turrets to access different sides of the parts on a working position. At the preliminary design stage, the following decisions must be made: the choice of orientations of the parts on the rotary table; the partitioning of the given set of tasks into positions and their assignment to machining modules (selection or design of machining modules to use), and the choice of cutting modes for each spindle head and turret. The objective is to minimize the total cost of equipment used. The number of possible solutions for this combinatorial design problem increases exponentially with the number of part types to be produced, and this represents a computational burden for decision-makers (usually process engineers). In this paper, in order to help decision makers deal efficiently with the manufacturing of multiple batches of parts, we develop a powerful heuristic framework which can be used in real life industrial cases. We test the developed methodology on the real-life cases provided by one of our industrial partners and demonstrate its efficiency. The proposed model and algorithms allow to minimize the cost of designed machines.

Logical qubit implementation for quantum annealing: augmented Lagrangian approach

Solving optimization problems on quantum annealers (QA) usually requires each variable of the problem to be represented by a connected set of qubits called a logical qubit or a chain. Chain weights, in the form of ferromagnetic coupling between the chain qubits, are applied so that the physical qubits in a chain favor taking the same value in low energy samples. Assigning a good chain-strength value is crucial for the ability of QA to solve hard problems, but there are no general methods for computing such a value and, even if an optimal value is found, it may still not be suitable by being too large for accurate annealing results. In this paper, we propose an optimization-based approach for producing suitable logical qubits representations that results in smaller chain weights and show that the resulting optimization problem can be successfully solved using the augmented Lagrangian method. Experiments on the D-Wave Advantage system and the maximum clique problem on random graphs show that our approach outperforms both the default D-Wave method for chain-strength assignment as well as the quadratic penalty method.

Mathematical modeling of resource allocation for cognitive radio sensor health monitoring system using coevolutionary quantum-behaved particle swarm optimization

Independent interactive medical applications cooperate closely with user systems through independent information and data exchange, which realizes digital and intelligent communication for health monitoring system. Due to extensive connectivity and application services, heavy data traffic and limited energy consumption reduce the quality of application services. In this paper, a mathematical model for efficient energy harvesting resource allocation based on cognitive radio sensor network (CRSN) infrastructure is constructed to address energy limitation issues in health monitoring system applications. Because of the highly dynamic nature of available network resources, energy efficiency and communication reliability can be improved. Since the proposed optimization problem is a complex and NP hard problem, coevolutionary theory is adopted, and then a stochastic optimization algorithm based on polynomial approximation and quantum particle swarm optimization is proposed. Simulation results show that the proposed CQPSO algorithm has better performance in terms of re-transmission probability, system throughput, and energy consumption compared to other similar algorithms. Efficient resource allocation improves the performance of the health monitoring system, more effectively and real-time monitoring of human physiological and motion information, providing testers with high-precision quantitative information, and achieving early monitoring and intervention of common diseases.

A Comparative Study on Genetic algorithm and Reinforcement Learning to Solve the Traveling Salesman Problem

Machine Learning (ML) and Evolutionary Computing (EC) are the two most popular computational methodologies in computer science to solve learning and optimization problems around us, respectively. It is of research interest in the literature, for exploring these two methodologies and to formulate algorithmic frameworks with 'EA for ML' and 'ML for EA' where EA stands for Evolutionary Algorithm. The objective of this paper is on exploring this dimension of research. The Traveling Salesman Problem (TSP) is one of the NP-hard (nondeterministic polynomial time hard) problems in combinatorial optimization problems. The solution for a TSP is the shortest path covering all the nodes in a given city. This paper compares two algorithms, "Genetic Algorithm (GA)" of the EC domain and "Epsilon-Greedy Q-Learning Algorithm (EQLA)" of the ML domain on solving TSP. The detailed design methodology involved in both these algorithms is discussed in this paper. The experiments are carried out on two different data sets (random and ATT48) to compare the speed and accuracy of the algorithms. The comparative results reveal that the GA could solve the TSP more effectively than EQLA. The obtained inferences along with the limitations are presented in this paper.

Torsion Geometry 5-Fold Symmetry, Anholonomic Phases, Klein Bottle Logophysics, Chaos, Resonance: Applications Towards a Novel Paradigm for the Neurosciences and Consciousness

Torsion Geometry 5-Fold Symmetry, Anholonomic Phases, Klein Bottle Logophysics, Chaos, Resonance: Applications Towards a Novel Paradigm for the Neurosciences and Consciousness

The effect of RRA treatment on mechanical properties and wear behavior in vanadium micro-alloyed Hadfield's steel

To solve the ongoing problems of micro-alloyed Hadfield's steel, insufficient hardness and excessive deformation in the early working period, expensive alloying elements like Ti, Nb, and V have been utilized. However, traditional long-time, single-stage aging exacer-bates vanadium to assemble at the edge of initial precipitates, which diminishes estimated effects. For this, here we report the tremendous impact of retrogression and re-aging process (RRA) in this steel by booming the V (C, N) mean sized in 11 nm. The procedure enhances the micro-hardness, yielding strength, tensile strength, and impact-wear resistance for more than 27.4%, 29.5%, 22.2%, and 51.5%, respectively. Moreover, the wear mechanism of RRA samples has been investigated in that micro-sized precipitates surrounded by numerous nano V (C, N) stick in the matrix and hinder the abrasive embedding consistently, thus decreasing the micro-cutting and micro-plowing. This new process maximizes the utilization of costly and nonrenewable vanadium and magnifies the impact-wear resistance under either mild or medium-intensity working conditions.

Numerical simulation and correlation research of multi-track overlapping laser quenching process for 40Cr steel

Laser quenching has a short process cycle and high production efficiency, and it plays an important role in automobile, ship, machinery manufacturing, and other fields. The surface hardness of 40Cr steel laser quenching parts is uneven by unreasonable set on the quenching overlap zone. This will affect the microstructure of the quenched layer, resulting in cracking, corrosion during service, and other hazards that ultimately reduce reliability. Numerical simulations provide an effective way to quantitatively reveal the transient evolution of the multi-field coupling between temperature field, stress field, and phase transition field in quenching, which directly determines the extent of the overlap zone and quenching properties. The quenched phase transition layer profile is predicted to effectively determine the extent of the secondary tempering softening zone and solve the bottleneck problem of uneven surface hardness in quenching. The innovation of this paper is to establish a multi-field coupled numerical model of the 40Cr steel multi-track laser quenching process. The transient quenching temperature, phase transition hardening, and stress distribution were numerically calculated, and the size of the tempering zone under different overlapping rates was evaluated, revealing the internal coupling mechanism and correlation between multi-fields during the laser quenching. The quenching temperature, microstructure, and hardness distribution of 40Cr steel were tested by an infrared thermometer, Axio Vert A1 Zeiss microscope, Thermo ScientificTM Apreo scanning electron microscope, and Q10M microhardness tester, which verified the effectiveness of numerical simulation. The research can provide an important theoretical basis for optimizing quenching process parameters in production.

Unsupervised Maritime Vessel Re-Identification With Multi-Level Contrastive Learning

Re-identification (re-ID) of maritime vessels plays an important role in marine surveillance, but remains highly unexplored due to the lack of large-scale annotated datasets. In vessel re-ID, contrastive methods are supposed to learn discriminative representation from unlabeled vessel images in an unsupervised manner. However, directly introducing classical instance-level contrastive methods to maritime vessel re-ID suffers from the difficulty of finding vessel images with the same pseudo label as positive images, which potentially leads to inefficient training and unsatisfactory performance. This paper proposes a simple but effective method to solve such a hard positive problem. Our method takes all images in an intra-batch cluster as positives and excludes them from the set of negative samples when computing instance-level contrastive loss. Based on this strategy, we construct a multi-level contrastive learning (MCL) framework for vessel re-ID trained with the specifically designed intra-batch cluster-level contrastive loss along with the instance-level one. Experiments on a newly proposed dataset consisting of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1,248$</tex-math> </inline-formula> vessel identities show that MCL achieves the state-of-the-art performance compared with other unsupervised methods.

Stochastic Project Scheduling Optimization for Multi-stage Prefabricated Building Construction with Reliability Application

This study investigates a project scheduling problem of prefabricated building (PB) construction in an uncertain environment. Different from the traditional scheduling models in PB construction, we consider a complex multi-stage cooperation system including the production, transportation and assembly (PTA) phases. In this system, both activity durations and resource amounts are stochastic variables. By applying the reliability theory to the stochastic scheduling model innovatively, we formulate a duration reliability model to maximize the probability of non-delayed project completion, within the resource constraints. As the proposed model is a non-deterministic polynomial hard (NP-hard) problem, a hybrid meta-heuristic differential evolution particle swarm optimization (DEPSO) algorithm is developed, which is utilized the mutation factor of the differential evolution (DE) algorithm in the framework of the particle swarm optimization (PSO). Finally, a real-life example of a PB construction project is used to explore the performance of the proposed DEPSO algorithm. The result shows that the hybrid algorithm DEPSO can better find the global optimal solution in the multi-dimensional optimization problem.

A bivariate polynomial-based cryptographic hard problem and its applications

A bivariate polynomial-based cryptographic hard problem and its applications

How effective are current population-based metaheuristic algorithms for variance-based multi-level image thresholding?

Multi-level image thresholding is a common approach to image segmentation where an image is divided into several regions based on its histogram. Otsu’s method is the most popular method for this purpose, and is based on seeking for threshold values that maximise the between-class variance. This requires an exhaustive search to find the optimal set of threshold values, making image thresholding a time-consuming process. This is especially the case with increasing numbers of thresholds since, due to the curse of dimensionality, the search space enlarges exponentially with the number of thresholds.Population-based metaheuristic algorithms are efficient and effective problem-independent methods to tackle hard optimisation problems. Over the years, a variety of such algorithms, often based on bio-inspired paradigms, have been proposed. In this paper, we formulate multi-level image thresholding as an optimisation problem and perform an extensive evaluation of 23 population-based metaheuristics, including both state-of-the-art and recently introduced algorithms, for this purpose. We benchmark the algorithms on a set of commonly used images and based on various measures, including objective function value, peak signal-to-noise ratio, feature similarity index, and structural similarity index. In addition, we carry out a stability analysis as well as a statistical analysis to judge if there are significant differences between algorithms. Our experimental results indicate that recently introduced algorithms do not necessarily achieve acceptable performance in multi-level image thresholding, while some established algorithms are demonstrated to work better.

Adversarial discriminative domain adaptation for modulation classification based on Ulam stability

AbstractDomain adaptation modulation classification aims to improve the cross‐domain robustness of modulation classification, with the basic idea of mitigating the domain shift between the label‐rich source domain and label‐poor target domain in the latent common feature subspace. Due to the complex and heterogeneous wireless propagation conditions, how to construct the optimal latent common feature subspace is a hard problem. In this letter, the authors introduce Ulam stability to character the boundary of the latent common feature space. These theorems can provide guidance for cross‐domain feature extraction and eliminate the domain shift. Based on the proposed stable theorems, the authors define the Ulam loss and present a new method named Adversarial Discriminative Domain Adaptation with Ulam stability (U‐ADDA). Experiments on public datasets prove the effectiveness and generality of the method.

О связи трудной проблемы сознания и комбинаторной проблемы панпсихизма

The paper compares two problems in the analytical philosophy of mind: the hard problem of consciousness and the combination problem for panpsychism. The former arises for a wide range of materialist approaches, including physicalism. Panpsychism is asserted by its proponents as the best solution to the combination problem. However, panpsychism itself is faced with the combination problem. A comparison of these problems demonstrates that constitutive panpsychism is fundamentally incapable of solving the hard problem of consciousness. This is demonstrated by the structural and substantive similarity of the arguments in favour of each problem. The author shows that constitutive panpsychism is an extension of physicalism specifically designed to solve the hard problem. Further, the arguments from conceivability in favour of the hard and combination problems are compared, demonstrating their structural similarity. In addition, the author points out the substantive similarity of these problems by comparing individual types of argumentations. This allows us to accept the hypothesis that one of the reasons for the problems of panpsychism and physicalism may lie in mereological atomism, which unites these approaches. Thus, the association of the hard and combination problems can serve as an argument against mereological atomism in both panpsychism and physicalism. Based on the analysis of both problems, general conclusions are drawn about the prospects for the approaches in question and a direction of research is proposed in which the general problems of constitutive panpsychism and physicalism could be solved. It is shown that there is at least one acceptable naturalistic alternative to atomism: priority monism, which can be a strategy for solving the considered problems for both physicalism and panpsychism.

Features for the 0-1 knapsack problem based on inclusionwise maximal solutions

Decades of research on the 0-1 knapsack problem led to very efficient algorithms that are able to quickly solve large problem instances to optimality. This prompted researchers to also investigate the structure of problem instances that are hard for existing solvers. In the current paper we are interested in investigating which features make 0-1 knapsack problem instances hard to solve to optimality for the state-of-the-art 0-1 knapsack solver. We propose a set of 14 features based on previous work by the authors in which so-called inclusionwise maximal solutions (IMSs) play a central role. Calculating these features is computationally expensive and requires one to solve hard combinatorial problems. Based on new structural results about IMSs, we formulate polynomial and pseudopolynomial time algorithms for calculating these features. These algorithms were executed for two large datasets on a supercomputer in approximately 540 CPU-hours. We show that the proposed features contain important information related to the empirical hardness of a problem instance that was missing in earlier features from the literature by training machine learning models that can accurately predict the empirical hardness of a wide variety of 0-1 knapsack problem instances. Moreover, we show that these features can be cheaply approximated at the cost of less accurate hardness predictions. Using the instance space analysis methodology, we show that hard 0-1 knapsack problem instances are clustered together around a relatively dense region of the instance space and several features behave differently in the easy and hard parts of the instance space.

Artificial intelligence for sustainable development of smart cities and urban land-use management

ABSTRACT The urban land-use allocation problem is a spatial optimization problem that allocates optimum land-uses to specific land units in urban areas. This problem is an NP (nondeterministic polynomial time)-hard problem because of involving many objective functions, many constraints, and complex search space. Moreover, this subject is an important issue in smart cities and newly developed areas of cities to achieve a sustainable arrangement of land-uses. Different types ofMulti-Objective Optimization Algorithms (MOOAs) based on Artificial Intelligence (AI) have been frequently employed, but their ability and performance have not been evaluated and compared properly. This paper aims to employ and compare three commonly used MOOAs i.e. NSGA-II, MOPSO, and MOEA/D in urban land-use allocation problems. Selected algorithms belong to different categories of MOOAs family to investigate their advantage and disadvantages. The objective functions of this study are compatibility, dependency, suitability, and compactness of land-uses and the constraint is compensating of Per-Capita demand in the urban environment. Evaluation of results is based on the dispersion of the solutions, diversity of the solutions’ space, and comparing the number of dominant solutions in Pareto-Fronts. The results showed that all three algorithms improved the objective functions related to the current arrangement of the land-uses. However, the run time of NSGA-II is the worst, related to the Diversity Metric (DM) which represents the regularity of the distance between solutions at the highest degree. Moreover, MOPSO provides the best Scattering Diversity Metric (SDM) which shows the diversity of solutions in the solution space. Furthermore, In terms of algorithm execution time, MOEA/D performed better than the other two. So, Decision-makers should consider different aspects in choosing the appropriate MOOA for land-use management problems.

Superpolynomial quantum-classical separation for density modeling

Density modelling is the task of learning an unknown probability density function from samples, and is one of the central problems of unsupervised machine learning. In this work, we show that there exists a density modelling problem for which fault-tolerant quantum computers can offer a super-polynomial advantage over classical learning algorithms, given standard cryptographic assumptions. Along the way, we provide a variety of additional results and insights, of potential interest for proving future distribution learning separations between quantum and classical learning algorithms. Specifically, we (a) provide an overview of the relationships between hardness results in supervised learning and distribution learning, and (b) show that any weak pseudo-random function can be used to construct a classically hard density modelling problem. The latter result opens up the possibility of proving quantum-classical separations for density modelling based on weaker assumptions than those necessary for pseudo-random functions.

Complexity of stability in trading networks

Efficient computability is an important property of solution concepts. We consider the computational complexity of finding and verifying various solution concepts in trading networks—multi-sided matching markets with bilateral contracts and without transferable utility—under the assumption of full substitutability of agents’ preferences. It is known that outcomes that satisfy trail stability always exist and can be found in linear time. However, we show that the existence of stable outcomes—immune to deviations by arbitrary sets of agents—is an {{textsf{N}}}{{textsf{P}}}-hard problem in trading networks. We also show that even verifying whether a given outcome is stable is {{textsf{N}}}{{textsf{P}}}-hard in trading networks.