On Parameter Identification for Reaction-Dominated Pore-Scale Reactive Transport Using Modified Bee Colony Algorithm

In this paper, the Gauge–Uzawa method for the time‐dependent incompressible micropolar fluid problem is proposed. Firstly, the first‐order and second‐order Euler semi‐implicit Gauge–Uzawa methods for the micropolar fluid are constructed, and the unconditionally stability analysis and error estimate of the corresponding first‐order scheme are deduced. Secondly, the fully discrete scheme with finite element approximation in space is presented, and the stability result of the corresponding first‐order scheme is derived. Finally, some numerical results are given, which test the theoretical analysis results.

We consider a systematic numerical approximation of a viscoelastic phase separation model that describes the demixing of a polymer-solvent mixture. An unconditionally stable discretisation method is proposed based on a finite element approximation in space and a variational time discretization strategy. The proposed method preserves the energy-dissipation structure of the underlying system exactly and allows us to establish a fully discrete nonlinear stability estimate in natural norms based on the concept of relative energy. These estimates are used to derive order optimal error estimates for the method under minimal smoothness assumptions on the problem data, despite the presence of various strong nonlinearities in the equations. The theoretical results and main properties of the method are illustrated by numerical simulations, which also demonstrate the capability to reproduce the relevant physical effects observed in experiments.

Swarm intelligence is briefly defined as the collective behavior of decentralized and self-organized swarms. Self-organization and labor division are the two key components of swarm intelligence. Artificial Bee Colony (ABC) algorithm is one of the most recent swarm intelligence-based algorithms. The behavior of bees in ABC algorithm satisfies the self-organization features, but there is no specific labor division mechanism in ABC algorithm. In this work, we propose an improved ABC algorithm called labor division artificial bee colony (LDABC) algorithm by incorporating the labor division mechanism into ABC algorithm, which is achieved by individual specialization and role plasticity. We specify three different search methods for employed bees, onlooker bees and scout bees to realize individual specialization, these search methods are related to food source quality, enable bees to maximize exploitation of food source. Role plasticity is achieved by combining with cellular automata, where the roles of bees are not static but vary with their surrounding environment, enable bees not to limit to one search method. The different search modes and the flexibility of the search behaviors make our algorithm achieve a better balance between exploration and exploitation. The experimental results tested on 13 benchmark functions and CEC-2013 test functions demonstrate a competitive performance.

The artificial bee colony (ABC) algorithm is an important branch of evolutionary algorithms and has been shown to be competitive with other algorithms for solving optimisation problems. However, there are always some insufficiencies in ABC algorithm such as lower convergence speed and easily get trapped in the local optima. To increase depth search capabilities of onlooker bees and ensure scout bees do not discard the current optimal solution, we proposed a modified ABC algorithm (denoted as ORABC) based on the optimisation strategy and retained strategy of the best individual in this paper. Compared with ABC algorithm, ABC* (added retained strategy to ABC) and ORABC algorithm, experiments are conducted on a set of numerical benchmark functions. The numerical simulation results demonstrate that ORABC algorithm improves the convergence characteristics of ABC algorithm and provides very remarkable performance in solving complex numerical optimisation problems compared to original algorithm.

The artificial bee colony (ABC) algorithm, which was inspired by the foraging and dance behaviors of real honey bee colonies, was first introduced for solving numerical optimization problems. When the solution space of the optimization problem is binary-structured, the basic ABC algorithm should be modified for solving this class of problems. In this study, we propose XOR-based modification for the solution-updating equation of the ABC algorithm in order to solve binary optimization problems. The proposed method, named binary ABC (binABC), is examined on an uncapacitated facility location problem, which is a pure binary optimization problem, and the results obtained by the binABC are compared with results obtained by binary particle swarm optimization (BPSO), the discrete ABC (DisABC) algorithm, and improved BPSO (IBPSO). The experimental results show that binABC is an alternative tool for solving binary optimization problems and is a competitive algorithm when compared with BPSO, DisABC, and IBPSO in terms of solution quality, robustness, and simplicity.

INTRODUCTION: The artificial bee colony (ABC) algorithm is a nature-inspired technique used for solving different optimization problems. This paper presents a multi-hive ABC algorithm for solving constrained benchmark functions of CEC2006. The CEC2006 data set contains the global benchmark functions with different design variables, number and type of constraints. OBJECTIVES: The objective of the proposed work is to design and apply the GPGPU based multi-hive ABC algorithm to solve constrained optimization problems. METHODS: The proposed approach is a multi-population coarse-grained system implemented using General Purpose Graphics Processing Unit (GPGPU). The performance of the proposed approach is compared with the serial ABC algorithm for eleven benchmark functions and results in the literature. The multi-hive ABC algorithm has multiple hives, each running separate ABC algorithm on different cores of GPGPU. RESULTS: The proposed approach provides global best solutions in significantly reduced time for all benchmark functions. The speed-up obtained is approximately 7X to 9X. The GPGPU device utilization is approximately 57% to 91%. CONCLUSION: The GPGPU based multi-hive ABC algorithm is a found good with respect to best results, speed up factor and GPU utilization to solve constrained optimization problems.

A self adaptive hybrid enhanced artificial bee colony algorithm for continuous optimization problems

The continuous artificial bee colony algorithm for binary optimization

In this paper, we consider the Galerkin finite element method for solving the fractional stochastic diffusion‐wave equations driven by multiplicative noise, which can be used to describe the propagation of mechanical waves in viscoelastic media with random effects. The optimal strong convergence error estimates with respect to the semidiscrete finite element approximation in space are established. Finally, a numerical example is presented to verify the reliability of the theoretical study.

In this work, we study the thermal effects around the oil well cluster on permafrost using numerical modeling. We use the mathematical model of heat transfer with phase transitions. To take into account the arrangement of wells in a cluster, three-dimensional domains with complex geometry are employed, which leads to the use of finite element approximation in space. For time approximation we use fully implicit scheme with linearization of nonlinear coefficients. Numerical implementations are performed using open-source libraries and programs for scientific and engineering computations. To predict the temperature field and formation of thawing area around wells with different sets of input parameters we conduct large-scale computational experiments on the supercomputer of the North-Eastern Federal University.

In this article we consider the iterative solution of the linear system of equations arising from the discretisation of the poly-energetic linear Boltzmann transport equation using a high-order/hp–version discontinuous Galerkin finite element approximation in space, angle, and energy. In particular, we develop preconditioned Richardson iterations which may be understood as generalisations of source iteration in the mono-energetic setting, and derive computable a posteriori bounds for the solver error incurred due to inexact linear algebra, measured in a relevant problem-specific norm. We prove that the convergence of the resulting schemes and a posteriori solver error estimates are independent of the mesh size h and polynomial degree p. We also discuss how the poly-energetic Richardson iteration may be employed as a preconditioner for the generalised minimal residual (GMRES) method. Furthermore, we show that standard implementations of GMRES based on minimising the Euclidean norm of the residual vector can be utilized to yield computable a posteriori solver error estimates at each iteration, through judicious selections of left- and right-preconditioners for the original linear system. The effectiveness of poly-energetic source iteration and preconditioned GMRES, as well as their respective a posteriori solver error estimates, is demonstrated through numerical examples arising in the modelling of photon transport. While the convergence of poly-energetic source iteration is independent of h and p, we observe that the number of iterations required to attain convergence when employing GMRES only depends mildly on h and p. Moreover, this latter approach is highly effective in the low energy regime.

Modeling large-deforming fluid-saturated porous media using an Eulerian incremental formulation

An energy-conserving finite element method for nonlinear fourth-order wave equations

SUMMARYThis paper proposes novel artificial bee colony (ABC) algorithms for solving problems including interdependence among variables. ABC algorithms are one method of solving multivariable real number space optimization problems, in which the search space is a set of vectors constructed of variables. The main search process in the ordinary ABC algorithm creates a new solution vector by changing only one variable of the current solution vector. Therefore, the new solution vector is created along only one coordinate axis. This procedure, however, is not appropriate for solving problems including interdependence among variables. For such problems, a method that is able to change more than one variable of a solution vector at the same time is required. In our proposed methods, the original coordinate axes are transformed to linearly uncorrelated axes by using principal component analysis (PCA) in the searching process. Our ABC algorithms create a new solution vector along one of the axes transformed by PCA. Hence, from the viewpoint of the original coordinate axes, the new algorithms are able to change more than one variable. The proposed algorithms have been compared with the ordinary ABC algorithm by solving five benchmark problems. Through the computer simulation results, our algorithms were shown to have better performance for solving problems including interdependence among variables than the ordinary ABC algorithm.

Archive-based multi-criteria Artificial Bee Colony algorithm for whole test suite generation