Parallel Evolutionary Algorithm Research Articles

EvOligo: A Novel Software to Design and Group Libraries of Oligonucleotides Applicable for Nucleic Acid-Based Experiments.

Experimental methods based on DNA and RNA hybridization, such as multiplex polymerase chain reaction, multiplex ligation-dependent probe amplification, or microarray analysis, require the use of mixtures of multiple oligonucleotides (primers or probes) in a single test tube. To provide an optimal reaction environment, minimal self- and cross-hybridization must be achieved among these oligonucleotides. To address this problem, we developed EvOligo, which is a software package that provides the means to design and group DNA and RNA molecules with defined lengths. EvOligo combines two modules. The first module performs oligonucleotide design, and the second module performs oligonucleotide grouping. The software applies a nearest-neighbor model of nucleic acid interactions coupled with a parallel evolutionary algorithm to construct individual oligonucleotides, and to group the molecules that are characterized by the weakest possible cross-interactions. To provide optimal solutions, the evolutionary algorithm sorts oligonucleotides into sets, preserves preselected parts of the oligonucleotides, and shapes their remaining parts. In addition, the oligonucleotide sets can be designed and grouped based on their melting temperatures. For the user's convenience, EvOligo is provided with a user-friendly graphical interface. EvOligo was used to design individual oligonucleotides, oligonucleotide pairs, and groups of oligonucleotide pairs that are characterized by the following parameters: (1) weaker cross-interactions between the non-complementary oligonucleotides and (2) more uniform ranges of the oligonucleotide pair melting temperatures than other available software products. In addition, in contrast to other grouping algorithms, EvOligo offers time-efficient sorting of paired and unpaired oligonucleotides based on various parameters defined by the user.

FORM reliability analysis using a parallel evolutionary algorithm

This paper presents a parallel evolutionary algorithm to solve reliability problems with accuracy and repeatability of results. The last characteristic is usually overlooked; however, it is critical to the reliability of the calculation method itself. Note that evolutionary algorithms are stochastic processes and may not always generate identical results. The optimisation problem resulting from the first order reliability method is considered with an implicit state function that can include a call to a finite element analysis (FEA). A strategy to handle failures from the transformation of random variables or from the finite element call during the evolution process is explained in detail. Several benchmark tests are studied, including some involving bounded random variables that introduce strong non-linearities in the mapping to standard Gaussian space. In addition, the solutions of 2D and 3D frame problems using the finite element method illustrate the capabilities of the algorithm including the convenience of the algorithm in handling discrete limit state functions. Finally, the ability to obtain similar results after many runs is demonstrated.

Agent-based Evolutionary and Memetic Black-box Discrete Optimization

Hybridizing agent-based paradigm with evolutionary or memetic computation can enhance the field of meta-heuristics in a significant way, giving to usually passive individuals autonomy and capabilities of perception and interaction with other ones. In the article, an evolutionary multi-agent system (EMAS) is applied to solve difficult discrete benchmark problems without any domain-specific knowledge—thus they may be called “black-box” ones. As a means for comparison, a parallel evolutionary algorithm (constructed along with Michalewicz model) versus evolutionary and memetic versions of EMAS are used. The obtained results point out that EMAS is significantly more efficient than classical evolutionary algorithms and also finds better results in the examined problem instances.

Assessing Energy Consumption and Runtime Efficiency of Master Worker Parallel Evolutionary Algorithms in CPU-GPU Systems

Thanks to parallel processing, it is possible not only to reduce code runtime but also energy consumption once the workload has been adequately distributed among the available cores. The current availability of heterogeneous architectures including GPU and CPU cores with different power-performance characteristics and mechanisms for dynamic voltage and frequency scaling does, in fact, pose a new challenge for developing efficient parallel codes that take into account both the achieved speedup and the energy consumed. This paper analyses the energy consumption and runtime behavior of a parallel master-worker evolutionary algorithm according to the workload distribution between GPU and CPU cores and their operation frequencies. It also proposes a model that has been fitted using multiple linear regression and which enables a workload distribution that considers both runtime and energy consumption by means of a cost function that suitably weights both objectives. Since many useful bioinformatics and data mining applications are tackled by programs with a similar profile to that of the parallel master-worker procedure considered here, the proposed energy-aware approach could be applied in many different situations.

Research of parallel evolutionary algorithms and its application in image processing

Evolutionary computation is the cross-cutting and frontier research field of information science, automation science and computer science. It is also the research direction of computational intelligence. In this paper, based on the evolutionary computation theory, a series model of evolutionary computation is established and the convergence of typical evolutionary algorithms is analyzed. The adaptive evolutionary algorithm based on population information entropy and its application in image is proposed. The paper proposes a decision-making algorithm based on bit-coded discernible matrix and its application in image analysis. The idea of parallel computing is applied to the problem of SAR image change detection and image feature extraction. The corresponding parallel computing framework is designed to improve the running speed of the algorithm. Experimental results showed that the proposed algorithm can improve the accuracy and processing speed of the algorithm, which satisfies the practical application requirements.

A Runtime Analysis of Parallel Evolutionary Algorithms in Dynamic Optimization

A simple island model with lambda islands and migration occurring after every tau iterations is studied on the dynamic fitness function Maze. This model is equivalent to a (1+lambda ) EA if tau =1, i. e., migration occurs during every iteration. It is proved that even for an increased offspring population size up to lambda =O(n^{1-epsilon }), the (1+lambda ) EA is still not able to track the optimum of Maze. If the migration interval is chosen carefully, the algorithm is able to track the optimum even for logarithmic lambda . The relationship of tau , lambda , and the ability of the island model to track the optimum is then investigated more closely. Finally, experiments are performed to supplement the asymptotic results, and investigate the impact of the migration topology.

Multithreaded computing in evolutionary design and in artificial life simulations

This article investigates low-level and high-level multithreaded performance of evolutionary processes that are typically employed in evolutionary design and artificial life. Computations performed in these areas are specific because evaluation of each genotype usually involves time-consuming simulation of virtual environments and physics. Computational experiments have been conducted using the Framsticks simulator running a multithreaded version of a standard evolutionary experiment. Tests carried out on five diverse machines and two operating systems demonstrated how low-level performance depends on the number of physical and logical CPU cores and on the number of threads. Two string implementations have been compared, and their raw performance turned out to fundamentally differ in a multithreading setup. To improve high-level performance of parallel evolutionary algorithms, i.e. the quality of optimized solutions, a new distribution scheme that is especially useful and efficient for complex representations of solutions—the convection distribution—has been introduced. This new distribution scheme has been compared against a random distribution of genotypes among threads that carry out evolutionary processes.

An accelerated parallel algorithm for constructing the nonredundant matrix of implications during the construction of fault-tolerant nonredundant diagnostic tests

In this article the relevance of using test methods of pattern recognition while developing intelligent systems for decision making support for various problem areas is discussed. The advantage of fault-tolerant diagnostic tests used in intelligent systems is shown, namely, a tool for registering and processing different kinds of errors in databases and knowledge bases. The results of testing two algorithms for constructing the nonredundant matrix of implications are compared; the technical particulars of program implementation are discussed such as synchronization means, test environment, test-program structure, and bottlenecks of program implementation; methods of their elimination, and further development of parallel algorithms.

Iterative conic beam tomography based on Bayesian approach to radiation therapy

The key problem in increasing efficiency of radiotherapy of malignant tumors in brain and other dangerous neoplasms is the problem of increasing the quality of 3D positioning of a patient before radiotherapy. We consider the principles of development of fast parallel iterative algorithms based on graphic accelerators and the OpenGL library. The proposed approaches provide simultaneous residual minimization for the sought solution and total variation of the reconstructed 3D image. In this case, the number of required initial data, i.e., conic X-ray projections, can be reduced several times, and therefore, the radiation load on the patient can also be accordingly reduced with preservation of the necessary contrast and spatial resolution of the 3D image of the patient. The new heuristic iterative algorithm can be used as an alternative to the known 3D Feldkamp algorithm.

PARALLEL COMPUTING OF NUMERICAL SCHEMES AND BIG DATA ANALYTIC FOR SOLVING REAL LIFE APPLICATIONS

This paper proposed the several real life applications for big data analytic using parallel computing software. Some parallel computing software under consideration are Parallel Virtual Machine, MATLAB Distributed Computing Server and Compute Unified Device Architecture to simulate the big data problems. The parallel computing is able to overcome the poor performance at the runtime, speedup and efficiency of programming in sequential computing. The mathematical models for the big data analytic are based on partial differential equations and obtained the large sparse matrices from discretization and development of the linear equation system. Iterative numerical schemes are used to solve the problems. Thus, the process of computational problems are summarized in parallel algorithm. Therefore, the parallel algorithm development is based on domain decomposition of problems and the architecture of difference parallel computing software. The parallel performance evaluations for distributed and shared memory architecture are investigated in terms of speedup, efficiency, effectiveness and temporal performance.

General methodology for using Epanet as an optimization element in evolutionary algorithms in a grid computing environment for water distribution network design

In this paper, an evolutionary algorithm, called EA-WDND, is developed to optimize water distribution network design for real instances. The evolutionary algorithm uses the Epanet Solver which, while not an optimizer, helps to evaluate the operational constraints of mass conservation, energy conservation, pressure in nodes (nodal heads) of the network, and velocities of water in network pipes. Epanet is used by the EA-WDND to evaluate whether the looped network is operating properly. Consequently, the EA-WDND obtains feasible configurations of network design. The best configuration, which has the lowest cost and best performance according to defined constraints, is obtained by the EA-WDND. This configuration can be practically implemented in real life. In this paper, a methodology for using Epanet Solver with a parallel evolutionary algorithm is presented.

A large-scale flight multi-objective assignment approach based on multi-island parallel evolution algorithm with cooperative coevolutionary

Due to the rapid increase of air traffic demand, the large-scale flight assignment plays a crucial role in reducing airspace congestion and economic losses via reasonably regulating the air traffic flow of China. In this paper, the large-scale flight assignment problem is formulated as a multi-objective model with consideration of the reduction of airspace congestion and flight delay. However, it is a large-scale combinatorial optimization problem with complex constraints and tightly coupled decision variables, which is difficult to deal with. Hence, an effective multi-objective optimization algorithm is proposed based on the multi-island parallel evolution framework (PEA) with a left-right probability migration topology. Multi-island PEA employs multiple evolution populations for solving the problem simultaneously, and the left-right probability migration topology for exchange individuals among populations to improve the efficiency of the cooperation of populations. Then the cooperative co-evolution (CC) algorithm is introduced for each population to further improve the searching capability. Simulation results using the real traffic data from the China air route network and daily flight plans demonstrate that the proposed approach can improve the solution quality effectively, showing superiority to the existing approaches such as the multi-objective genetic algorithm, the well-known multi-objective evolutionary algorithm based on decomposition, a CC-based multi-objective algorithm as well as other two parallel evolution algorithms with different migration topologies.

Probabilistic Graphical Models on Multi-Core CPUs Using Java 8

In this paper, we discuss software design issues related to the development of parallel computational intelligence algorithms on multi-core CPUs, using the new Java 8 functional programming features. In particular, we focus on probabilistic graphical models (PGMs) and present the parallelisation of a collection of algorithms that deal with inference and learning of PGMs from data. Namely, maximum likelihood estimation, importance sampling, and greedy search for solving combinatorial optimisation problems. Through these concrete examples, we tackle the problem of defining efficient data structures for PGMs and parallel processing of same-size batches of data sets using Java 8 features. We also provide straightforward techniques to code parallel algorithms that seamlessly exploit multi-core processors. The experimental analysis, carried out using our open source AMIDST (Analysis of MassIve Data STreams) Java toolbox, shows the merits of the proposed solutions.

Agent Based Modelling for Water Resource Allocation in the Transboundary Nile River

Water resource allocation is the process of assessing and determining a mechanism on how water should be distributed among different regions, sectors and users. Over the recent decades, the optimal solution for water resource allocation has been explored both in centralised and decentralised mechanisms. Conventional approaches are under central planner suggesting a solution which maximises total welfare to the users. Moving towards the decentralised modelling, the techniques consider individuals as if they act selfishly in their own favour. While central planner provides an efficient solution, it may not be acceptable for some selfish agents. The contrary is true as well in decentralised solution, where the solution lacks efficiency leading to an inefficient usage of provided resources. This paper develops a parallel evolutionary search algorithm to introduce a mechanism in re-distributing the central planner revenue value among the competing agents based on their contribution to the central solution. The result maintains the efficiency and is used as an incentive for calculating a fair revenue for each agent. The framework is demonstrated and discussed to allocate water resources along the Nile river basin, where there exist eleven competing users represented as agents in various sectors with upstream-downstream relationships and different water demands and availability.

Increase in the quality of the prediction of a computational wildfire behavior method through the improvement of the internal metaheuristic

Wildfires cause great losses and harms every year, some of which are often irreparable. Among the different strategies and technologies available to mitigate the effects of fire, wildfire behavior prediction may be a promising strategy. This approach allows for the identification of areas at greatest risk of being burned, thereby permitting to make decisions which in turn will help to reduce losses and damages. In this work we present an Evolutionary-Statistical System with Island Model, a new approach of the uncertainty reduction method Evolutionary-Statistical System. The operation of ESS is based on statistical analysis, parallel computing and Parallel Evolutionary Algorithms (PEA). ESS-IM empowers and broadens the search process and space by incorporating the Island Model in the metaheuristic stage (PEA), which increases the level of parallelism and, in fact, it permits to improve the quality of predictions.

Parallel surrogate-assisted global optimization with expensive functions – a survey

Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in computing power increasingly rely on parallelization rather than faster processors. This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. A key issue focused on in this review is how different algorithms balance exploration and exploitation. Most of the papers surveyed are adaptive samplers that employ Gaussian Process or Kriging surrogates. These allow sophisticated approaches for balancing exploration and exploitation and even allow to develop algorithms with calculable rate of convergence as function of the number of parallel processors. In addition to optimization based on adaptive sampling, surrogate assisted parallel evolutionary algorithms are also surveyed. Beyond a review of the present state of the art, the paper also argues that methods that provide easy parallelization, like multiple parallel runs, or methods that rely on population of designs for diversity deserve more attention.

Comparison of common parallel architectures for the execution of the island model and the global parallelization of evolutionary algorithms

SummaryEvolutionary algorithms are one of the most popular forms of optimization algorithms. They are comparatively easy to use and were successfully employed for a wide variety of practical applications. However, frequently, it is necessary to execute them in parallel in order to reduce the runtime. There are a number of different approaches for the parallelization of evolutionary algorithms, and various hardware platforms can be used for the parallel execution. However, not every platform is equally suitable for any kind of parallelization of evolutionary algorithms. In addition, it also depends on properties of the concrete optimization problem to be solved and on the used evolutionary algorithm, which platform is best suited for the execution. The present work observes this in detail for two common forms of parallelization of evolutionary algorithms – the island model and the global parallelization – and for four widely used parallel computing platforms – multi‐core CPUs, clusters, graphics cards, and grids. Based on empirical and analytical investigations, it is determined, under which circumstances an architecture is better suited for the execution of a parallel evolutionary algorithm than another (and vice versa). Guidelines are derived that support users of parallel evolutionary algorithms with the choice of an appropriate platform. Copyright © 2016 John Wiley & Sons, Ltd.

Efficient parallel evolutionary algorithms for deadline-constrained scheduling in project management

Deadline-constrained scheduling in project management is a NP-hard optimisation problem with major relevance in software engineering and other real-life situations dealing with the planning of activities that must be completed before specific dates. This article introduces efficient parallel versions for two evolutionary algorithms genetic algorithm and hybrid evolutionary algorithm, to solve the deadline-constrained scheduling problem in project management. The proposed algorithms have been engineered to compute accurate solutions in reduced execution times. Specific evolutionary operators, including a parallel local search operator in the hybrid evolutionary algorithm, are proposed for efficiently solving realistic problem instances, and both a master-slave parallel strategy and a distributed subpopulation model are applied to further improve the computational efficiency and the results quality. The experimental analysis performed on both a set of standard problem instances and new large problem instances demonstrate that accurate solutions are computed by the proposed techniques, especially for the distributed subpopulation version of the hybrid evolutionary algorithm. The comparative experimental evaluation demonstrates that the parallel evolutionary algorithms are able to outperform in reduced execution times the results computed using one of the best well-known deterministic techniques for the problem, in particular when solving instances with tight deadline constraints.

A Practical Parallel Programming Course based on Problems of the Spanish Parallel Programming Contest

This paper presents an experience of an introductory course on Parallel Programming. The course is dedicated to parallel programming tools and environments, and in particular to the analysis, development and optimization of parallel algorithms. It has a practical orientation and is guided with the use of problems from the Spanish Parallel Programming Contest. The different units are presented in the traditional lecture format, and a practical session accompanies each unit, with problems to work with in the tools or algorithmic paradigms presented in the previous lecture. The students work in the practical sessions on problems and using the system of the contest, which facilitates online and real time validation of their implementations. The practical approach of the course and the continuous evaluation used led to an important increase in the marks.

High performance computing in multi-scale modeling, graph science and meta-heuristic optimization

One of the main activities within the Group for Scientific Computing at the Faculty of Science are methods for efficiently utilizing real parallel architectures, typically clusters of SMP nodes, shared-memory systems, and GPUs. Focus is on the design, development and implementation of parallel algorithms and data structures for fundamental scientific and engineering problems. Message Passing Interface (MPI) is an important paradigm that still poses interesting design and implementation problems, especially combined with other programming models, like CUDA. In addition to standard HPC (High Performance Computing) technology stack, the Group also utilizes computing stacks like Hadoop and Spark. In this paper we present a short review of the recent research of the Group, focused on large-scale applications in various research fields with references to original articles. The first part considers multi-scale muscle modeling in mixed MPI-CUDA environment. In our approach, a finite element macro model is coupled with the microscopic Huxley kinetics model. The original approach in scheduling tasks within multi-scale simulation ensures good load balance, leading to speed-up of over two orders of magnitude and high scalability. The second part considers application of HPC in graph science for the task of establishing the basic structural features of the minimum-ABC index trees. In order to analyze large amounts of data (all trees of certain order) we utilize grid computing services like storage and computing in order to reduce analysis time up to three orders of magnitude. The last part presents WoBinGO framework for solving optimization problems on HPC resources. It overcomes the shortcomings of earlier static pilot-job frameworks by providing elastic resource provisioning using adaptive allocation of jobs with limited lifetime. The obtained results show that despite WoBinGO's adaptive and frugal allocation of computing resources, it provides significant speed-up when dealing with problems with computationally expensive evaluations, as found in hydro-informatics and market risk management.