Non-deterministic Polynomial Hard Problem Research Articles

Beyond Single-Reference Fixed-Node Approximation in Ab Initio Diffusion Monte Carlo Using Antisymmetrized Geminal Power Applied to Systems with Hundreds of Electrons.

Diffusion Monte Carlo (DMC) is an exact technique to project out the ground state (GS) of a Hamiltonian. Since the GS is always bosonic, in Fermionic systems, the projection needs to be carried out while imposing antisymmetric constraints, which is a nondeterministic polynomial hard problem. In practice, therefore, the application of DMC on electronic structure problems is made by employing the fixed-node (FN) approximation, consisting of performing DMC with the constraint of having a fixed, predefined nodal surface. How do we get the nodal surface? The typical approach, applied in systems having up to hundreds or even thousands of electrons, is to obtain the nodal surface from a preliminary mean-field approach (typically, a density functional theory calculation) used to obtain a single Slater determinant. This is known as single reference. In this paper, we propose a new approach, applicable to systems as large as the C60 fullerene, which improves the nodes by going beyond the single reference. In practice, we employ an implicitly multireference ansatz (antisymmetrized geminal power wave function constraint with molecular orbitals), initialized on the preliminary mean-field approach, which is relaxed by optimizing a few parameters of the wave function determining the nodal surface by minimizing the FN-DMC energy. We highlight the improvements of the proposed approach over the standard single-reference method on several examples and, where feasible, the computational gain over the standard multireference ansatz, which makes the methods applicable to large systems. We also show that physical properties relying on relative energies, such as binding energies, are affordable and reliable within the proposed scheme.

Improved genetic algorithm based on rule optimization strategy for fibre allocation

ABSTRACT In modern manufacturing industry, in order to adapt to changes in the general environment, the manufacturing industry must improve production efficiency. To this end, this article introduces an improved genetic algorithm based on rule selection to tackle the nondeterministic polynomial hard problem stemming from inventory fibre resources and fibre selection principles in optical cable production. The algorithm aims to maximize inventory score and minimize fibre segmentation rate. It employs a permutation encoding approach to link the genetic algorithm with fibre allocation solutions and applies a self-attention mechanism to determine subset solution weight within each solution. To boost the recombination of favourable gene segments from different chromosomes, a rule optimization strategy is integrated into the crossover operation based on the weights. This operations enhance the algorithm's global search capability and convergence speed. A feasibility repair strategy is then used to inspect and rectify chromosomes, preventing the generation of illegal solutions. The legitimate mutation operation, founded on weight optimization rules, effectively reduces the algorithm's running time by avoiding illegal solutions. By leveraging actual production data from an optical cable manufacturer for simulation, the experimental results confirm the effectiveness of the improved genetic algorithm in addressing the fibre allocation problem. Comparative simulations with the unimproved genetic algorithm and a stepwise greedy algorithm underscore the superiority of the improved genetic algorithm in resolving the fibre allocation problem.

Nanophotonic phased array XY Hamiltonian solver

Solving large-scale computationally hard optimization problems using existing computers has hit a bottleneck. A promising alternative approach uses physics-based phenomena to naturally solve optimization problems, wherein the physical phenomena evolve to their minimum energy. In this regard, photonics devices have shown promise as alternative optimization architectures, benefiting from high-speed, high-bandwidth, and parallelism in the optical domain. Among photonic devices, programmable spatial light modulators (SLMs) have shown promise in solving large scale Ising model problems, to which many computationally hard problems can be mapped. Despite much progress, existing SLMs for solving the Ising model and similar problems suffer from slow update rates and physical bulkiness. Here, we show that using a compact silicon photonic integrated circuit optical phased array (PIC-OPA), we can simulate an XY Hamiltonian, a generalized form of the Ising Hamiltonian, where spins can vary continuously. In this nanophotonic XY Hamiltonian solver, the spins are implemented using analog phase shifters in the optical phased array. The far field intensity pattern of the PIC-OPA represents an all-to-all coupled XY Hamiltonian energy and can be optimized with the tunable phase-shifters, allowing us to solve an all-to-all coupled XY model. Our results show the utility of PIC-OPAs as compact, low power, and high-speed solvers for nondeterministic polynomial-hard problems. The scalability of the silicon PIC-OPA and its compatibility with monolithic integration with CMOS electronics further promise the realization of a powerful hybrid photonic/electronic non-Von Neumann compute engine.

Peak sidelobe level suppression via reconfigurable element pattern selection for beam scanning linear array antenna

Array antenna with reconfigurable elements can further suppress the peak sidelobe level (PSLL) by selecting reconfigurable element pattern (REP) combination according to the variation of the beam directions. However, the pattern selection for N array elements from K possible REPs is a typical non-deterministic polynomial-hard problem, resulting in the REP selection being intractable even with small N and K. To solve this REP selection problem effectively, this paper proposes an improved genetic algorithm to shrink the REP combinations that contain the suboptimal one. As a result, the proposed algorithm can adaptively select the best REP combination for different scanning beams by searching a relatively minor solution space. The alternating direction multiplier method is used to accelerate the computational speed of optimizing the array excitation. Array antennas with two-type REPs and three-type REPs, respectively, are simulated with high-frequency simulation software (HFSS) to validate the superiority of the proposed algorithm. Comparisons with the existing algorithms show that the proposed algorithm can select a superior REP combination to obtain 1 dB to 3 dB lower PSLL for different scanning beams.

Multiobjective Prioritized Workflow Scheduling in Cloud Computing Using Cuckoo Search Algorithm

Effective workflow scheduling in cloud computing is still a challenging problem as incoming workflows to cloud console having variable task processing capacities and dependencies as they will arise from various heterogeneous resources. Ineffective scheduling of workflows to virtual resources in cloud environment leads to violations in service level agreements and high energy consumption, which impacts the quality of service of cloud provider. Many existing authors developed workflow scheduling algorithms addressing operational costs and makespan, but still, there is a provision to improve the scheduling process in cloud paradigm as it is an nondeterministic polynomial-hard problem. Therefore, in this research, a task-prioritized multiobjective workflow scheduling algorithm was developed by using cuckoo search algorithm to precisely map incoming workflows onto corresponding virtual resources. Extensive simulations were carried out on workflowsim using randomly generated workflows from simulator. For evaluating the efficacy of our proposed approach, we compared our proposed scheduling algorithm with existing approaches, i.e., Max–Min, first come first serve, minimum completion time, Min–Min, resource allocation security with efficient task scheduling in cloud computing-hybrid machine learning, and Round Robin. Our proposed approach is outperformed by minimizing energy consumption by 15% and reducing service level agreement violations by 22%.

A Novel Nature-Inspired Routing Scheme for Improving Routing Quality of Service in Power Grid Monitoring Systems

In the process of power grid monitoring, image data are mostly transmitted in segments by wireless image sensor networks (WISNs), and the external attacks or interference on a communication link easily causes the data to be retransmitted. Therefore, high quality of service (QoS) routing is essential to the secure and robust operation of the WISNs. Specifically, the QoS routing optimization in WISN is a nondeterministic polynomial hard problem, which usually cannot be solved by ordinary algorithms. Based on this motivation, a novel heuristic trust scheme (i.e., multi-objective trust routing scheme based on cloned elite slime mould algorithm (CESMA-MTRS)) is proposed to greatly improve the security and QoS of WISN. The scheme designs a novel trust difference test mechanism to enhance the identification of malicious nodes. Furthermore, a new elite archiving strategy and clone optimization strategy are designed to improve the convergence speed of grid monitoring data transmission routing search. The simulation results show that when the network scale is 100 and the attacked node scale is 30%, CESMA-MTRS reduces the delay by 8% and 15% and the packet loss rate by 24% and 27%, respectively, compared with trust-based routing protocol called the On-demand trust-based multicast routing protocol (ODTMRP) and evolutionary selfcooperative trust approach.

Hybrid Differential Evolution and Krill Herd Algorithm for the Optimal Design of Water Distribution Networks

For optimally designing water distribution networks (WDNs), the nondeterministic polynomial hard problem, a novel hybrid model, is introduced with the combined features of evolutionary and swarm intelligence techniques. An evolutionary algorithm with better exploration properties, differential evolution (DE), and the swarm intelligence technique with better exploitation properties, namely the krill herd algorithm (KHA), is considered for this purpose. Because exploration and exploitation are the essential features of the metaheuristic algorithms, the hybrid algorithm with a combination of the DE and KHA features, the DE-KHA, resulted in a balanced search methodology. The results on the application of the proposed model on well-studied benchmark problems have demonstrated its enhanced search behavior, converging faster to the promising results with considerable robustness. Moreover, compared with other competing algorithms reported for optimally designing the WDNs, the DE-KHA outperforms with better computational efficiency. Additionally, considering the few control parameters that have to be calibrated for their optimal values, the computational burden will be less for performing the sensitivity analysis. As a result, considering the solution precision, quick convergence ability, and robustness of DE-KHA, the study suggests the algorithm for efficiently handling real-life case studies.

Advances in parallel and distributed computing and its applications

Advances in parallel and distributed computing and its applications

Optimization of process parameters for preparation of coal water slurry using ant colony optimization and hydrodynamic simulation

ABSTRACT The Ant Colony Optimization algorithm is one of the leading optimization algorithms for nondeterministic polynomial hard problems. The aim of the present work is to find the maximum power of the agitator and selection of the motor power load for agitation equipment by assuming 80% of the maximum power of the agitator for preparation of coal water slurry. The parameters considered for optimization are the particle diameter of solids, jacketed vessel diameter and height, and density of the slurry. They affect the mass ratio and viscosity of the slurry. Sensitivity analysis was conducted with various combinations of ants’ parameters to enhance the result. From the optimization result, the power of the propeller, paddle, and rushton impellers is 13.58 Watts, 54.34 Watts, and 108.67 Watts. The power of the rushton impeller compared with the that of propeller and paddle is around 87.5% and 49%. VisiMix software is used to predict the turbulent shear near the impeller with optimized parameters obtained from ACO. The turbulent shear stress of the rushton impeller of 18.9 N/mm2 is high when compared with the other impellers. The developed algorithm has been justified with standard formulation and found to be satisfactory for implementing maximum power in agitation for uniform suspension to improve heat transfer.

Scheduling the periodic delivery of liquefied petroleum gas tank with time window by using artificial intelligence approaches: An example in Taiwan.

In Taiwan, liquefied petroleum gas tank users have to call a gas company to deliver a full liquefied petroleum gas tank when their tank is out of gas. The calls usually congest in the cooking time and the customers have to wait for a long time for a full liquefied petroleum gas tank. Additionally, allocating manpower is difficult for the gas company. A strategy of periodic delivery for gas companies was presented to deliver liquefied petroleum gas tanks in advance and charge the gas fee based on the weight of returned tanks. Additionally, a new encoding scheme was proposed and embedded into three evolutionary algorithms to solve the nondeterministic polynomial-hard problem. The objective of the problem is to minimize the total traveling distance of the vehicle such that the delivery efficiency of tanks increases and the waiting time of customer decreases. A new encoding scheme was presented to convert any random sequence of integers into a solution of the problem and embedded into three evolutionary algorithms, namely, immune algorithm, genetic algorithm, and particle swarm optimization, to solve the delivery problem. Additionally, the encoding scheme can be used to different frequency types of demand based on customers' requests. Numerical results, including a practical example in Yunlin, Taiwan, were provided to show that the adopted approaches can significantly improve the efficiency of delivery. The periodic delivery strategy and the new encoding scheme can effectively solve the practical problem of liquefied petroleum gas tank in Taiwan.

An Intuitionistic Fuzzy Multi-Objective Goal Programming Approach to Portfolio Selection

Portfolio selection can be regarded as a type of multi-objective decision problem. However, traditional solution methods rarely discussed the decision maker’s nonsatisfaction and hesitation degrees with regard to multiple objectives and they require many extra binary variables, which lead to tedious computational burden. Based on the above, the aim of this paper is to develop a new and unified intuitionistic fuzzy multi-objective linear programming (IFMOLP) model for such portfolio selection problems. The nonmembership functions are constructed by the pessimistic, optimistic, and mixed approaches so as to perfect the traditional intuitionistic fizzy (IF) inequalities and IF theory. The decision maker’s hesitation degrees with regard to multiple objectives are represented by using IF inequalities, and the new IFMOLP model based on IF inequalities is proposed. The IFMOLP problems are solved by the S-shaped membership functions without extra binary variables required by the piecewise-linear method. Finally, the portfolio selection model under IF environments based on IFMOLP is established, and a real example is analyzed to demonstrate its validity and superiority. The developed unified IFMOLP model and method can not only effectively solve multi-objective decision problems with nonsatisfaction and hesitation degrees but also remarkably reduce the complexity of the nondeterministic polynomial-hard problems.

Single-based and Population-Based Metaheuristics Algorithms Performances in Solving NP-hard Problems

Metaheuristics is one of the most well-known field of researches uses to find optimum solution for Non-deterministic polynomial hard problems (NP-Hard), that are difficult to find an optimal solution in a polynomial time. Over time many algorithms have been developed based on the heuristics to solve difficult real-life problems, this paper will introduce Metaheuristic-based algorithms and its classifications, Non-deterministic polynomial hard problems. It also will compare the performance two metaheuristic-based algorithms (Elephant Herding optimization algorithm and Tabu Search) to solve Traveling Salesman Problem (TSP), which is one of the most known problem belongs to Non-deterministic polynomial hard problem and widely used in the performance evaluations for different metaheuristics-based optimization algorithms. the experimental results of the paper compare the results of EHO and TS for solving 10 different problems from the TSPLIB95.

Research of Multi-objective Component Assignment Problem for Lin/con/k/n System Considering Cost

Component assignment problem (CAP) is widely used in engineering systems, which is investigated for enhancing the system performance by assigning switchable components to several positions. However, the traditional CAP does not consider the cost factor and has limitations for improving the system reliability. Therefore, this paper focus on a multi-objective CAP for Lin/con/k/n system to find the cost-efficient maintenance scheme. Firstly, a multi-objective mathematical optimization model is developed with the aim of maximizing the system reliability and minimizing maintenance cost, which has been proved to be a Non-deterministic polynomial hard problem and should be computed by heuristics. Secondly, a comprehensive maintenance method is adopted to improve the system reliability, which integrates improving the system reliability with changing the permutation of components. To evaluate the contribution of the component reliability on both system reliability and maintenance cost, the multi-objective Birnbaum importance (MOBI) is derived. Finally, MOBI is introduced into the non-dominated sorting genetic algorithm-II (NSGA-II) to solve the optimization model. The numerical experiments for several Lin/con/k/n Systems show that MOBI is available to solve CAP considering cost, and MOBI-based-NSGA-II algorithm is more efficient to obtain higher system reliability with lower maintenance cost.

Portfolio Optimization based on Synthetic Fish Congestion Algorithm and Artificial Intelligence: In the Condition of Uncertainty and Half-Variance Risk

The issue of stock portfolio optimization is the most well-known issue in the area of optimization. The purpose of this issue is to create a portfolio among different stocks in such a way that it has the highest returns and the least risk. The problem of optimal selection of stock portfolio is one of the Non-deterministic polynomial hardproblems (NP-hard). For this reason, this article introduces a new hybrid algorithm based on artificial immunity algorithm and swarm of fish algorithm. The computational results show that the proposed combination method has the ability to find the best investment boundary in comparison to other proposed methods.

Application of incremental satisfiability problem solvers for non-deterministic polynomial-time hard problems as illustrated by minimal Boolean formula synthesis problem

Subject of Research. The paper considers a method for solution of the nondeterministic polynomial hard problem (NP-hard problem) of a minimal Boolean formula synthesis from a given truth table. The solution of this problem is proposed based on its reduction to the Boolean satisfiability problem (SAT). The issues of efficient and convenient software implementation are discussed for reducing nondeterministic polynomial hard problems to the satisfiability problem. Method. For a minimal Boolean formula synthesis, the constraint programming approach was used: a SATformula was created for a given truth table, satisfiable if and only if, there exists a Boolean formula of a given size that satisfies the given truth table. The developed method accent is the application of incremental satisfiability problem solvers. Main Results. A method is proposed for synthesis of a Boolean formula, minimal with respect to the number of operators and terminals, for a given truth table. The method is based on reducing to satisfiability problem and provides the usage of incremental satisfiability problem solvers. The kotlin-satlib framework is developed with the possibility to use the Kotlin and Java languages effectively and conveniently for the software implementation of reducing various nondeterministic polynomial-time hard problems to satisfiability problem. Native interaction with satisfiability problem solvers by Java Native Interface (JNI) technology is used. The proposed method for the minimal Boolean formula synthesis is implemented in the Kotlin programming language using the developed kotlin-satlib framework. Practical Relevance. An experimental study on the example of minimal Boolean formula synthesis has shown that the usage of incremental satisfiability problem solvers for nondeterministic polynomial hard problems is reasonable, since it reduces the total solving time in comparison with the non-incremental approach application.

A Novel Multi-Objective Model for the Cold Chain Logistics Considering Multiple Effects

This paper focuses on solving a problem of green location-routing with cold chain logistics (GLRPCCL). Considering the sustainable effects of the economy, environment, society, and cargos, we try to establish a multi-objective model to minimize the total cost, the full set of greenhouse gas (GHG) emissions, the average waiting time, and the total quality degradation. Several practical demands were considered: heterogeneous fleet (HF), time windows (TW), simultaneous pickup and delivery (SPD), and a feature of mixed transportation. To search the optimal Pareto front of such a nondeterministic polynomial hard problem, we proposed an optimization framework that combines three multi-objective evolutionary algorithms (MOEAs) and also developed two search mechanisms for a large composite neighborhood described by 16 operators. Extensive analysis was conducted to empirically assess the impacts of several problem parameters (i.e., distribution strategy, fleet composition, and depots’ time windows and costs) on Pareto solutions in terms of the performance indicators. Based on the experimental results, this provides several managerial insights for the sustainale logistics companies.

RETRACTED ARTICLE: Protein blueprint and their interactions while approachability struggle for amino acids

Understanding the basic source of twenty letter set of proteins is a substantial biophysical and non-deterministic polynomial hard problem. It specifically concentrates on the broader effect of a decreased letter in order to estimate on the collapsing properties. In any case, common letters in order is a bargain among adaptability and enhancement of the accessible assets. In the current work, an extra effect of general accessibility is incorporated in order to show a protein configuration strategy which includes the opposition for assets between a protein and its prospective association accomplice. Moreover, it also allows for exploring the effect caused by the decreased letter set on protein-protein communications. The ideal decreased organization of letters for the plan of protein is distinguished and it was observed that the ordered letters were reduced to 4 letters taking single protein collapsing into consideration. In any case, it is only 6 letters that accomplished ideal collapsing; in this manner recouping investigations are repeated. It was also examined that the observation between the protein and a potential connection accomplice could not be maintained at a strategic distance from which this study reduced the letter sets. In this way, we recommend that accumulation could have been the main incentive for the advancement of substantial protein letters in order.

Container Terminal Logistics Generalized Computing Architecture and Green Initiative Computational Pattern Performance Evaluation

Container terminals are the typical representatives of complex supply chain logistics hubs with multiple compound attributes and multiple coupling constraints, and their operations are provided with the strong characteristics of dynamicity, nonlinearity, coupling, and complexity (DNCC). From the perspective of computational logistics, we propose the container terminal logistics generalized computing architecture (CTL-GCA) by the migration, integration, and fusion of the abstract hierarchy, design philosophy, execution mechanism, and automatic principles of computer organization, computing architecture, and operating system. The CTL-GCA is supposed to provide the problem-oriented exploration and exploitation elementary frameworks for the abstraction, automation, and analysis of green production at container terminals. The CTL-GCA is intended to construct, evaluate, and improve the solution to planning, scheduling, and decision at container terminals, which all are nondeterministic polynomial hard problems. Subsequently, the logistics generalized computational pattern recognition and performance evaluation of a practical container terminal service case study is launched by the qualitative and quantitative approach from the sustainable perspective of green production. The case study demonstrates the application, utilization, exploitation, and exploration of CTL-GCA preliminarily, and finds the unsustainable patterns of production at the container terminal. From the above, we can draw the following conclusions. For one thing, the CTL-GCA makes a definition of the abstract and automatic running architecture of logistics generalized computation for container terminals (LGC-CT), which provides an original framework for the design and implementation of control and decision mechanism and algorithm. For another, the CTL-GCA can help us to investigate the roots of DNCC thoroughly, and then the CTL-GCA makes for conducting the efficient and sustainable running pattern recognition of LGC-CT. It is supposed to provide a favorable guidance and supplement to define, design, and implement the agile, efficient, sustainable, and robust task scheduling and resource allocation for container terminals by computational logistics whether in the strategy level or the tactical one.

Research on predicting 2D-HP protein folding using reinforcement learning with full state space

BackgroundProtein structure prediction has always been an important issue in bioinformatics. Prediction of the two-dimensional structure of proteins based on the hydrophobic polarity model is a typical non-deterministic polynomial hard problem. Currently reported hydrophobic polarity model optimization methods, greedy method, brute-force method, and genetic algorithm usually cannot converge robustly to the lowest energy conformations. Reinforcement learning with the advantages of continuous Markov optimal decision-making and maximizing global cumulative return is especially suitable for solving global optimization problems of biological sequences.ResultsIn this study, we proposed a novel hydrophobic polarity model optimization method derived from reinforcement learning which structured the full state space, and designed an energy-based reward function and a rigid overlap detection rule. To validate the performance, sixteen sequences were selected from the classical data set. The results indicated that reinforcement learning with full states successfully converged to the lowest energy conformations against all sequences, while the reinforcement learning with partial states folded 50% sequences to the lowest energy conformations. Reinforcement learning with full states hits the lowest energy on an average 5 times, which is 40 and 100% higher than the three and zero hit by the greedy algorithm and reinforcement learning with partial states respectively in the last 100 episodes.ConclusionsOur results indicate that reinforcement learning with full states is a powerful method for predicting two-dimensional hydrophobic-polarity protein structure. It has obvious competitive advantages compared with greedy algorithm and reinforcement learning with partial states.

Adapting the elitism on greedy algorithm for variable strength combinatorial test cases generation

A combinatorial testing (CT) is an important technique usually employed in the generation of test cases. The generation of an optimal sized test case is a non-deterministic polynomial hard problem. In recent times, many researchers had developed various strategies based on the search-based approach to address the CT issues. This study presented the most recent variable interaction strength (VS) CT strategy using an enhanced variant in the greedy algorithm. Hence, they are referred to as variable strength modified greedy strategy (VS-MGS). Moreover, the modified strategy supports a VS together with interaction strength up to six. The proposed variant-greedy algorithm employed the elitism mechanism alongside the iteration in order to improve its efficiency. This algorithm is invariably called the modified greedy algorithm (MGA). Furthermore, the efficiency and performance of the VS-MGS using MGA were assessed first by comparing its results with the original greedy algorithm results and thereafter benchmarked with the results of the existing VS CT strategies. The VS-MGS's results ultimately revealed that the adaptation of elitism mechanism with iteration in greedy algorithm resulted in an improved efficiency in the process of generating a near-optimal test case set size.