Parallel Evolutionary Algorithm Research Articles

Development and evaluation of parallel simulated annealing algorithm for reactor core optimization problems

Background The optimization of core loading patterns in nuclear reactors is one of the most studied optimization problems in nuclear engineering due to the enormous economical and safety benefits. Various algorithms such as Genetic Algorithms (GA), Simulated Annealing (SA), and Parallel Simulated Annealing (PSA) have been used in the past for such problems. Methods In this work, a PSA algorithm was developed and integrated into the Modularly Implemented Design Assistance Suite (MIDAS), a framework developed at North Carolina State University to solve nuclear engineering problems. The effectiveness of PSA was compared against the GA and SA algorithms available in MIDAS for a Pressurized Water Reactor first cycle core loading pattern optimization problem. Results PSA consistently generates more optimal solutions than SA and GA by having the higher average fitness, and showing less variance in its performance and thus being more robust. This provides confidence in the PSA implementation within MIDAS. The obtained loading pattern positions high reactive fuel in peripheral locations and low reactive fuel towards the centers in a strategy resembling both Out-In-Checkboard and L3P loading patter approaches. Conclusions Future studies will involve applying the PSA algorithm to other optimization studies in larger combinatorial spaces, such as in multi-cycle optimization problems.

Parallel cooperative multiobjective coevolutionary algorithm for constrained multiobjective optimization problems

The existing parallel multiobjective evolutionary computation does not perform well for constrained multiobjective optimization problems with discontinuous Pareto fronts or narrow feasible regions. This study parallelizes the state-of-the-art cooperative multiobjective coevolutionary algorithm and proposes an effective parallel evolutionary algorithm for constrained multiobjective optimization problems that are difficult to optimize. Two parallelization methods are compared: a global parallel model in which solution evaluations are performed in parallel, and a hybrid model that treats the cooperative populations in a distributed manner while performing each solution evaluation in parallel. The first model is a straightforward parallelization, while the second one capitalizes on the characteristics of the coevolutionary framework. To investigate the efficacy of the proposed models, experiments are conducted on constrained multiobjective optimization problems, including complex characteristics, while varying the number of parallel cores up to 64. The experiments compare the two proposed methods from the viewpoint of search performance and execution time. The experimental results reveal that the latter hybrid model shows better computational efficiency and scalability against an increasing number of cores without adversely affecting the search performance compared to the former straightforward parallelization.

Parallel evolutionary algorithms for the reconfigurable transfer line balancing problem

This paper deals with an industrial problem of machining line design, which consists in partitioning a given set of operations into several subsets corresponding to workstations and sequencing the operations to satisfy the technical requirements and achieve the best performance of the line. The problem has a complex set of constraints that include partial order on operations, part positioning, inclusion, exclusion, cycle time, and installation of parallel machines on a workstation. The problem is NP-hard and even finding a feasible solution can be a difficult task from the practical point of view. A parallel evolutionary algorithm (EA) is proposed and implemented for execution on a Graphics Processing Unit (GPU). The parallelization in the EA is done by working on several parents in one iteration and in multiple application of mutation operator to the same parent to produce the best offspring. The proposed approach is evaluated on large scale instances and demonstrated superior performance compared to the algorithms from the literature in terms of running time and ability to obtain feasible solutions. It is shown that in comparison to the traditional populational EA scheme the newly proposed algorithm is more suitable for advanced GPUs with a large number of cores.

McRPL: a general purpose parallel raster processing library on distributed heterogeneous architectures

Parallel computing on distributed heterogeneous architectures (e.g. computing clusters with multiple CPUs and GPUs) can significantly improve the computational efficiency and scalability of complicated algorithms, but it is theoretically and technically complex. Parallel raster processing libraries reduce the development complexity of parallel raster algorithms by hiding parallel computing details; however, no existing library sufficiently utilizes distributed heterogeneous computing resources. A general-purpose raster processing library (mcRPL) combining multi-process parallelism and multi-thread parallelism is proposed to enable parallel raster processing on distributed heterogeneous architectures with multiple CPUs and GPUs. Additionally, an adaptive hardware assignment strategy is proposed to fully utilize available processors in various hardware environments. A series of task-processing strategies are adopted to aim toward maximizing the utilization of the computing capacity of involved processors. Experiments revealed that two raster algorithms parallelized using mcRPL for spatiotemporal data fusion and land-use change simulation were 170.7- and 143.2-fold faster than original serial algorithms using 8 and 16 GPUs, respectively. While hiding the details of mixed parallelism and reducing the development complexity, mcRPL provides user-friendly interfaces for the development of parallel raster algorithms to enhance computational performance and enable large-scale raster computing tasks with extensive data volumes.

An adaptive parallel evolutionary algorithm for solving the uncapacitated facility location problem

An adaptive parallel evolutionary algorithm for solving the uncapacitated facility location problem

Development of Monte Carlo hybrid parallel algorithm and application in high resolution spatial-energy flux distribution calculation

Monte Carlo transport calculations for fusion reactors are very challenging due to factors such as large radiation decay gradients, complex geometric structures, and strong neutron anisotropy. For such deep penetration shielding calculation problems, in order to obtain accurate global flux distribution, variance reduction methods and parallel computing are necessary means. When using the global weight window variance reduction technique, the huge deviation of the particle arrival weight and the weight window can lead to an extreme number of splits, which is the long history problem. If a process encounters the long history problem in parallel computing and takes a lot of time to complete, other processes will stop computing because the histories allocated in the computing cycle have been completed, seriously degrading computing efficiency. In addition, the calculation of the shutdown dose rate, a key issue in fusion neutronics calculations, requires a high “space-energy” resolution, which brings huge challenges to computational memory, especially in parallel computing. In order to overcome the degradation of parallel efficiency caused by the long history problem and the memory limitation in large-scale shutdown dose rate calculation, a hybrid parallel algorithm based on shared memory was researched. Finally, the hybrid parallel architecture was built in the Monte Carlo code cosRMC to realize dynamic load balancing between threads. Through the memory sharing among all threads of a single node, the pressure of memory consumption can be effectively relieved. In order to verify the effectiveness of the hybrid parallel algorithm developed in this paper, it was applied to the calculation of CFETR. The results showed that the hybrid parallel algorithm can effectively alleviate the impact of the long history problem on the parallel efficiency. In terms of saving memory, the hybrid parallel algorithm also showed a significant effect, which provides a guarantee for large-scale shutdown dose rate calculation.

An edge-aided parallel evolutionary privacy-preserving algorithm for Internet of Things

Data sanitization in the context of Internet of Things (IoT) privacy refers to the process of permanently and irreversibly hiding all sensitive information from vast amounts of streaming data. Taking into account the dynamic and real-time characteristics of streaming IoT data, we propose a parallel evolutionary Privacy-Preserving Data Mining (PPDM), called High-performance Evolutionary Data Sanitization for IoT (HEDS4IoT), and implement two mechanisms on a Graphics Processing Units (GPU)-aided parallelized platform to achieve real-time streaming protected data transmission. The first mechanism, the Parallel Indexing Engine (PIE), generates retrieval index lists from the dataset using GPU blocks. These lists are used in place of the dataset during the PPDM process. The second mechanism, called Parallel Fitness Function Engine (PF2E), parallelizes the index lists on the GPU threads to speed up the computation of the quality of solutions generated by the evolutionary algorithm, in which deferential evolution is adopted as the evolutionary algorithm. In addition to the ability for Big data, the HEDS4IoT can be adaptively adjusted for dynamic nature of IoT where new streaming data is considered for data sanitization. Our experimental results with extensive benchmarks show that, at the kernel level, the PIE and PF2E mechanisms are averagely 33.5x and 53.7x faster than their CPU-implemented version, respectively. At the application level, our findings demonstrate that the HEDS4IoT can perform the PPDM process 47.7x faster than some of the state-of-art methods.

A massively parallel evolutionary algorithm for the partial Latin square extension problem

The partial Latin square extension problem is to fill as many as possible empty cells of a partially filled Latin square. This problem is a useful model for a wide range of applications in diverse domains. This paper presents the first massively parallel evolutionary algorithm for this computationally challenging problem based on a transformation of the problem to partial graph coloring. The algorithm features the following original elements. Based on a very large population (with more than 104 individuals) and modern graphical processing units, the algorithm performs many local searches in parallel to ensure an intensive exploitation of the search space. The algorithm employs a dedicated crossover with a specific parent matching strategy to create a large number of diversified and information-preserving offspring at each generation. Extensive experiments on 1800 benchmark instances show a high competitiveness of the algorithm compared to the current best performing methods. Competitive results are also reported on the related Latin square completion problem. Analyses are performed to shed lights on the roles of the main algorithmic components.

Scalable Parallel Nonlinear Optimization with PyNumero and Parapint

We describe PyNumero, an open-source, object-oriented programming framework in Python that supports rapid development of performant parallel algorithms for structured nonlinear programming problems (NLP’s) using the Message Passing Interface (MPI). PyNumero provides three fundamental building blocks for developing NLP algorithms: a fast interface for calculating first and second derivatives with the AMPL Solver Library (ASL), a number of interfaces to efficient linear solvers, and block-structured vectors and matrices based on NumPy, SciPy, and MPI that support distributed parallel storage and computation. PyNumero’s design enables efficient, parallel algorithm development using high-level Python syntax while keeping expensive numerical calculations in fast, compiled implementations based on languages like C and Fortran. To demonstrate the utility of PyNumero, we also present Parapint, a Python package built on PyNumero for parallel solution of dynamic optimization problems. Parapint includes a parallel interior-point solver based on Schur-Complement decomposition. We illustrate the effectiveness of PyNumero for developing parallel algorithms with both code examples and scalability analyses for parallel matrix-vector dot products, parallel solution of structured systems of linear equations using Schur-Complement decomposition, and the parallel solution of a two-dimensional PDE optimal control problem. Our numerical results show nearly perfect scaling to more than 1,000 cores for large matrix-vector dot products and structured linear systems. Moreover, we obtain more than 354 times speedup for the optimal control example. History: Accepted by Alice Smith, EIC/Ted Ralphs, Area Editor/Software Tools. Funding: This work was funded in part by the Institute for the Design of Advanced Energy Systems (IDAES) with funding from the Office of Fossil Energy, Cross-Cutting Research, U.S. Department of Energy. This work was also funded by Sandia National Laboratories Laboratory Directed Research and Development (LDRD) program. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.1272 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2021.0285 ) at ( http://dx.doi.org/10.5281/zenodo.7192328 ).

Parallel evolutionary algorithm for Water Distribution Network Design, using the Masters–Students model in distributed environment

The Water Distribution Networks Design Problem study is widespread in the scientific community due to its practical applicability. In this work, a sequential evolutionary algorithm has been designed, developed, and successfully applied to solve small and medium instances; it has been called the Evolutionary Algorithm for Water Distribution Network Design Problem. This algorithm is executed in centralized environments and can perfectly solve these instances in less than minutes. For large-scale real-world instances, the Evolutionary Algorithm has been adapted to work in distributed environments by using a novel parallel model, also proposed in this work, called the Masters–Students​ model. This model has been used for designing, developing, and implementing the resulting parallel evolutionary algorithm, named PEA-WDND. The Evolutionary Algorithm would last for days in the case of real-world instances, but the parallel algorithm solves them in seconds or, at maximum, in minutes. This study shows that the parallel algorithm yields an execution time lower than the execution time obtained from the evolutionary algorithm for different theoretical and practical instances.

Comparative analysis of the implementation of parallel algorithms on the central processors of automation systems in agriculture

The article presents a comparative analysis of the implementation of parallel algorithms on the central processors of automation systems in agriculture. Modern automation systems impose increased requirements on the reliability of the implementation of parallel algorithms in real time. It is proposed to use models for the development, analysis and comparison of parallel algorithms on GPUs. The proposed model of parallel computing on GPUs is designed to simplify the development of parallel algorithms for a heterogeneous CPU-GPU environment. Those, with this model, you can: develop parallel algorithms that use data parallelism, while applying the existing experience in creating parallel algorithms for the PRAM machine; evaluate the running time of the parallel algorithm and analyze which part of it is the most resource-intensive and requires optimization; compare parallel algorithms according to some parameters of the model, and, if required, select the best one according to these parameters in real time for automation in agriculture.

Топологічні аспекти моделювання паралельних обчислень при розв’язанні динамічних задач на базі інтерфейсу передачі повідомлень

The article deals with the development, substantiation and research of analytical models of data transmission when solving complex multidimensional dynamic problems. Models of interprocessor exchange are obtained for computing systems with distributed memory and topological architectures: ring, 2D-torus, hypercube, etc. The application of double arithmetic based on the GMP-library and its effect on the temporal characteristics of parallel applications are considered. The dependence of the parallelism characteristics of numerical algorithms when using the streaming SIMD-extension is analyzed. The modern stage of development of computer and information technologies has one of its directions - the use of the idea of parallelism in order to reduce the time of solving multidimensional problems that have a high temporal complexity or are even NP-complete. Despite the significant increase in the productivity of existing parallel systems, reducing the cost of components for their construction, development and implementation of parallel methods remains the most difficult problem due to the lack of study of the internal structure of algorithms and their properties. Thus, the introduction of parallel computing requires not so much the mastering of parallel data processing as the development of parallel algorithms based on the parallelization of sequential ones or the construction of fundamentally new, more efficient parallel methods. That is why the analysis of existing parallel algorithms, studying the possibilities of improving their characteristics is an important, promising and practically demanded task. The purpose of the work is to increase the efficiency of solving complex multidimensional dynamic problems implemented on parallel architectures of distributed memory using the message passing interface (MPI) due to the reduction of communication time. Tasks of the work: firstly, it is the development of analytical models of multiple data transfer operations for different topologies of connection of processors, substantiation of the models and research of the quality of parallel calculations of various classes of numerical methods for cluster systems. The possibility and effectiveness of using double arithmetic (GMP-library) and its influence on the temporal characteristics of parallel implementation are separately investigated. Additionally, the paper analyzes the dependence of the parallelism characteristics of the methods on the use of Streaming SIMD-Extension (SSE). The scientific novelty of the field lies in the development and development of analytical models for processes of interprocessor exchange given by cluster systems, which allow to increase the efficiency of parallel implementations of numerical methods in rich dynamic tasks for a short period of time on a communication warehouse. The practical significance of the field is in the development of split models for the analysis of the efficiency of collective operations of multiple data transfer within the MPI interface, which is the current standard for software implementation of robots with shared memory. Possibility of arranging floating arithmetic based on the GMP library was added, which is especially important for the development of folding problems of dynamic problems with singularities.

A frequency-based parent selection for reducing the effect of evaluation time bias in asynchronous parallel multi-objective evolutionary algorithms

Parallel evolutionary algorithms (PEAs) have been studied for reducing the execution time of evolutionary algorithms by utilizing parallel computing. An asynchronous PEA (APEA) is a scheme of PEAs that increases computational efficiency by generating a new solution immediately after a solution evaluation completes without the idling time of computing nodes. However, because APEA gives more search opportunities to solutions with shorter evaluation times, the evaluation time bias of solutions negatively affects the search performance. To overcome this drawback, this paper proposes a new parent selection method to reduce the effect of evaluation time bias in APEAs. The proposed method considers the search frequency of solutions and selects the parent solutions so that the search progress in the population is uniform regardless of the evaluation time bias. This paper conducts experiments on multi-objective optimization problems that simulate the evaluation time bias. The experiments use NSGA-III, a well-known multi-objective evolutionary algorithm, and compare the proposed method with the conventional synchronous/asynchronous parallelization. The experimental results reveal that the proposed method can reduce the effect of the evaluation time bias while reducing the computing time of the parallel NSGA-III.

QUANTUM DIGITAL-ANALOGUE COMPUTING

Context. Nature is the relation among processes and phenomena. Nothing exists in the universe without relations. Computer is transactions of relations between data with the help of control and execution mechanisms. Quantum relations are a superposition of particles and their states. Superposition and entanglement are equivalent concepts. Entanglement is a non-local superposition of deterministic states. A quantum computer is unconditional transactions of relations between qubit data. Quantum computer is an analog device for parallel solution of combinatorial problems. Practically oriented definitions of the quantum computer concepts are the path to development of scalable quantum parallel algorithms for combinatorial problems solving. Any algorithm can be reduced to a sequence of operations without conditions, because any truth table is a collection of a complete system of conditions-states. Any sequence of actions can always be reduced to one parallel operation. Conditions and sequences arise only when the developer wants to use previously created primitive constructs to build an always non-optimal computing unit. The paradigm of quantum computer creation is determined through the use of photonic transactions on the electrons of an atom may exclude the use of quantum logic. The evolutionary path of a quantum computer from the classical one: “memory-address-transaction” (MAT) → “electron-addresstransaction” → “electron-address-quantaction” (EAQ) → state-superposition-logic. The meeting point of classical and quantum computers is photon transactions on the structure of electrons. Everything that is calculated on a quantum computer can be calculated in parallel on a classical one on account of memory redundancy. The given example is a memory-driven algorithm for modeling digital products based on qubit-vector forms of functionality description for significant performance boost of computing processes by parallel execution of logical operations.
 Objective. Simulation of the correct SoC-component behavior based on vector representation of the logic. Formation of the triggering development of a computing based on the superposition of the classical, quantum and analog computing process, which in its development should be based on technological qubit, tabular and vector data structures for the parallel solution of combinatorial problems.
 Method. MAT-computing implements any algorithms on account of transactions (read-write) in memory. Qubit-vector models for describing functionalities, which differ from known truth tables in compactness of description and manufacturability for the implementation of parallel algorithms of the synthesis and analysis of digital devices and SoC-components.
 Results. 1) The metric of the technological data structures, focused on parallel troubleshooting in digital systems based on the usage of two logical vector operations, was proposed for the first time. 2) The metric of relations between the individual components of QC, allowing organizing a quantum deterministic computer, has been further developed. 3) Quantum architectural solutions, that allow solving coverage problems in a quasi-parallel mode, were proposed for the first time. 4) Architectural solutions based on an analog-to-digital computing, which can be used to solve the problems of the digital systems parallel analysis, have been further developed. 5) Vector-qubit structures of the logic data, that allow a quasi-parallel simulation of digital circuits, were proposed.
 Conclusions. Qubit models, quantum methods and combinatorial algorithms for technical diagnostics of digital devices have been implemented, which can significantly (up to 25%) reduce the time of test synthesis, deductive modeling of faulty and correct behavior, search for defective states by introducing an innovative idea of using qubit-vector data structures for describing logical components. Comparative assessments of qubit models and methods usage show an increase in the efficiency of algorithms for modeling digital devices compared to tabular ones. The superposition of a classical, quantum and analog computer is integrally represented, which allows to find the best solutions for recognition and decision making.

Improving the efficiency of graph algorithm executions on high‐performance computing

SummaryThe growing need for extracting information from large graphs has been pushing the development of parallel graph algorithms. However, the highly irregular structure of the real‐world graphs limits the performance and energy improvements of graph applications. In this paper, we show that, in most cases, using all the available cores of the multiprocessor is not the best option in terms of the aforementioned non‐functional requirements. Based on that, we propose GraphKat, a framework that enables the simultaneous processing of several algorithms/graphs instead of executing them serially (i.e., one after another), increasing efficiency in terms of performance and energy. GraphKat works in two steps: (i) it characterizes the graph applications with a specific number of threads based on their efficiency levels; and (ii) it defines the execution order of all graph applications in the target system. Experimental results on three multicore processors (Intel and AMD) show that GraphKat improves the overall system's efficiency related to performance (up to ) and energy‐saving (up to 245.21), and reduces the graph applications' execution time (up to ) and energy consumption (up to 6.64) compared to the default execution of parallel applications on HPC systems.

Development of Parallel Algorithms for Probabilistic Models Learning for Web Application Testing

In the current conditions of testing methods and algorithms development to combine individual test components in the form of a hierarchical model reflecting the connections and states between these components, as well as allowing to evaluate the probability of a transition between model states in the event of information about the successful implementation of a certain test to detect a program error is of particular importance. Existing approaches do not allow optimal adjustment of model parameters, as well as qualitative calculation and establishment of directions of connections between individual components of this model. The scientific study considered the possibility of using effective numerical methods to solve the problem of training probabilistic models built on the basis of Bayesian networks to solve the main problems of testing web applications. The main approaches to create parallel algorithms implementing the main functional capabilities underlying the implementation of the procedure for training test models are considered. Analysis of the developed training algorithms effectiveness for testing certain groups of program errors allowing the most optimal parameters of the DBS model was made, as well as a justification for their use in distributed data processing systems. We have developed algorithmic solutions for Jacobi and Hesse matrices calculation optimization based on Cannon and Fox algorithms. The process of testing errors of web application is simulated and presented in the form of a dynamic Bayesian network obtained from the results of the structure and parameters learning procedure. The validity of all theoretical results is confirmed by a large number of experimental results proving the validity of the put forward assumptions, testing methods and models presented in the form of dynamic Bayesian networks.

Scaling and Benchmarking an Evolutionary Algorithm for Constructing Biophysical Neuronal Models

Single neuron models are fundamental for computational modeling of the brain's neuronal networks, and understanding how ion channel dynamics mediate neural function. A challenge in defining such models is determining biophysically realistic channel distributions. Here, we present an efficient, highly parallel evolutionary algorithm for developing such models, named NeuroGPU-EA. NeuroGPU-EA uses CPUs and GPUs concurrently to simulate and evaluate neuron membrane potentials with respect to multiple stimuli. We demonstrate a logarithmic cost for scaling the stimuli used in the fitting procedure. NeuroGPU-EA outperforms the typically used CPU based evolutionary algorithm by a factor of 10 on a series of scaling benchmarks. We report observed performance bottlenecks and propose mitigation strategies. Finally, we also discuss the potential of this method for efficient simulation and evaluation of electrophysiological waveforms.

Parallel Genetic Algorithm Based on Construction of Gene Pool in the Ordinary Network for TSP

Though using parallel evolutionary algorithm to solve large-scale TSP problems is efficient, the parallel computer costs too much and the algorithm is not easy to expand. To address this issue, I propose a parallel genetic algorithm based on a gene pool under the existing network. To replace the group-genes in the evolutionary algorithm with the genes from the gene pool, the algorithm conducts greedy algorithm. The host process conducts greedy algorithm and improved evolutionary algorithm of Inver-over operator while the child process performs the improved hybrid genetic algorithms. Simulation results demonstrate that this algorithm achieves a better solution.

On the numerical approximation of some inverse problems governed by nonlinear delay differential equation

The paper deals with the approximate solving of an inverse problem for the nonlinear delay differential equation, which consists of finding the initial moment and delay parameter based on some observed data. The inverse problem is considered as a nonlinear optimal control problem for which the necessary conditions of optimality are formulated and proved. The obtained optimal control problem is solved by a method based on an improved parallel evolutionary algorithm. The efficiency of the proposed approach is demonstrated through various numerical experiments.

Development of Parallel Algorithms for Intelligent Transportation Systems

This paper deals with the creation of parallel algorithms implementing macro-and microscopic traffic flow models on modern supercomputers. High-performance computing contributes to the development of intelligent transportation systems based on information technologies and aimed at the effective regulation of traffic in large cities. As a macroscopic approach, the quasi-gas-dynamic traffic model approximated by explicit finite-difference schemes is proposed. One- and two-dimensional variants of the system are considered, and the concept of lateral velocity and different equations for obtaining it are discussed. The microscopic approach is represented by the multilane cellular automata model. The previously developed model is extended to reproduce synchronized flow in accordance with Kerner’s three-phase theory. The new version starts from the Kerner–Klenov–Schreckenberg–Wolf model and operates with the concept of the synchronization gap. Macroscopic models are relevant for determining the common characteristics of road traffic, while microscopic models are useful for a detailed description of cars’ movement. Both approaches possess inner parallelism. The parallel algorithms are based on the geometrical parallelism principle with different boundary conditions at interfaces of the subdomains. Sufficiently high speedups were reached when up to 100 processors were involved in calculations. The proposed algorithms can serve as the core of ITS.