Multi-Objective Optimization of Multi-Product Parallel Disassembly Line Balancing Problem Considering Multi-Skilled Workers Using a Discrete Chemical Reaction Optimization Algorithm

This work investigates a multi-product parallel disassembly line balancing problem considering multi-skilled workers.A mathematical model for the parallel disassembly line is established to achieve maximized disassembly profit and minimized workstation cycle time.Based on a product’s AND/OR graph,matrices for task-skill,worker-skill,precedence relationships,and disassembly correlations are developed.A multi-objective discrete chemical reaction optimization algorithm is designed.To enhance solution diversity,improvements are made to four reactions:decomposition,synthesis,intermolecular ineffective collision,and wall invalid collision reaction,completing the evolution of molecular individuals.The established model and improved algorithm are applied to ball pen,flashlight,washing machine,and radio combinations,respectively.Introducing a Collaborative Resource Allocation(CRA)strategy based on a Decomposition-Based Multi-Objective Evolutionary Algorithm,the experimental results are compared with four classical algorithms:MOEA/D,MOEAD-CRA,Non-dominated Sorting Genetic Algorithm Ⅱ(NSGA-Ⅱ),and Non-dominated Sorting Genetic Algorithm Ⅲ(NSGA-Ⅲ).This validates the feasibility and superiority of the proposed algorithm in parallel disassembly production lines.

Accelerating Large-Scale Sorting through Parallel Algorithms

This study explores the application of parallel algorithms to enhance large-scale sorting, focusing on the QuickSort method. Implemented in both sequential and parallel forms, the paper provides a detailed comparison of their performance. This study investigates the efficacy of both techniques through the lens of array generation and pivot selection to manage datasets of varying sizes. This study meticulously documents the performance metrics, recording 16,499.2 milliseconds for the serial implementation and 16,339 milliseconds for the parallel implementation when sorting an array by using C++ chrono library. These results suggest that while the performance gains of the parallel approach over its serial counterpart are not immediately pronounced for smaller datasets, the benefits are expected to be more substantial as the dataset size increases.

Improved parallel weighted bit-flipping algorithm

An improved parallel weighted bit-flipping（PWBF） algorithm is presented. To accelerate the information exchanges between check nodes and variable nodes, the bit-flipping step and the check node updating step of the original algorithm are parallelized. The simulation experiments demonstrate that the improved PWBF algorithm provides about 0. 1 to 0. 3 dB coding gain over the original PWBF algorithm. And the improved algorithm achieves a higher convergence rate. The choice of the threshold is also discussed, which is used to determine whether a bit should be flipped during each iteration. The appropriate threshold can ensure that most error bits be flipped, and keep the right ones untouched at the same time. The improvement is particularly effective for decoding quasi-cyclic low-density paritycheck（QC-LDPC） codes.

Multi-objective Optimization of a Parallel Ankle Rehabilitation Robot Using Modified Differential Evolution Algorithm

Dimensional synthesis is one of the most difficult issues in the field of parallel robots with actuation redundancy. To deal with the optimal design of a redundantly actuated parallel robot used for ankle rehabilitation, a methodology of dimensional synthesis based on multi-objective optimization is presented. First, the dimensional synthesis of the redundant parallel robot is formulated as a nonlinear constrained multi-objective optimization problem. Then four objective functions, separately reflecting occupied space, input/output transmission and torque performances, and multi-criteria constraints, such as dimension, interference and kinematics, are defined. In consideration of the passive exercise of plantar/dorsiflexion requiring large output moment, a torque index is proposed. To cope with the actuation redundancy of the parallel robot, a new output transmission index is defined as well. The multi-objective optimization problem is solved by using a modified Differential Evolution（DE） algorithm, which is characterized by new selection and mutation strategies. Meanwhile, a special penalty method is presented to tackle the multi-criteria constraints. Finally, numerical experiments for different optimization algorithms are implemented. The computation results show that the proposed indices of output transmission and torque, and constraint handling are effective for the redundant parallel robot; the modified DE algorithm is superior to the other tested algorithms, in terms of the ability of global search and the number of non-dominated solutions. The proposed methodology of multi-objective optimization can be also applied to the dimensional synthesis of other redundantly actuated parallel robots only with rotational movements.

Improvement and performance of parallel multilevel fast multipole algorithm

The method of establishing data structures plays an important role in the efficiency of parallel multilevel fast multipole algorithm（PMLFMA）.Considering the main complements of multilevel fast multipole algorithm（MLFMA） memory,a new parallelization strategy and a modified data octree construction scheme are proposed to further reduce communication in order to improve parallel efficiency.For far interaction,a new scheme called dynamic memory allocation is developed.To analyze the workload balancing performance of a parallel implementation,the original concept of workload balancing factor is introduced and verified by numerical examples.Numerical results show that the above measures improve the parallel efficiency and are suitable for the analysis of electrical large-scale scattering objects.

A Parallel Algorithm for Adaptive Local Refinement of Tetrahedral Meshes Using Bisection

Local mesh refinement is one of the key steps in the implementations of adaptive finite element methods. This paper presents a parallel algorithm for distributed memory parallel computers for adaptive local refinement of tetrahedral meshes using bisection. This algorithm is used in PHG, Parallel Hierarchical Grid Chttp：//lsec. cc. ac. cn/phg/）, a toolbox under active development for parallel adaptive finite element solutions of partial differential equations. The algorithm proposed is characterized by allowing simukaneous refinement of submeshes to arbitrary levels before synchronization between submeshes and without the need of a central coordinator process for managing new vertices. Using the concept of canonical refinement, a simple proof of the independence of the resulting mesh on the mesh partitioning is given, which is useful in better understanding the behaviour of the biseetioning refinement procedure.

The parallel 3D magnetotelluric forward modeling algorithm

The workload of the 3D magnetotelluric forward modeling algorithm is so large that the traditional serial algorithm costs an extremely large compute time. However, the 3D forward modeling algorithm can process the data in the frequency domain, which is very suitable for parallel computation. With the advantage of MPI and based on an analysis of the flow of the 3D magnetotelluric serial forward algorithm, we suggest the idea of parallel computation and apply it. Three theoretical models are tested and the execution efficiency is compared in different situations. The results indicate that the parallel 3D forward modeling computation is correct and the efficiency is greatly improved. This method is suitable for large size geophysical computations.

Matched-field inversion of sound speed profile in shallow water using a parallel genetic algorithm

A sound speed profile plays an important role in shallow water sound propagation.Concurrent with in-situ measurements,many inversion methods,such as matched-field inversion,have been put forward to invert the sound speed profile from acoustic signals.However,the time cost of matched-field inversion may be very high in replica field calculations.We studied the feasibility and robustness of an acoustic tomography scheme with matched-field processing in shallow water,and described the sound speed profile by empirical orthogonal functions.We analyzed the acoustic signals from a vertical line array in ASIAEX2001 in the East China Sea to invert sound speed profiles with estimated empirical orthogonal functions and a parallel genetic algorithm to speed up the inversion.The results show that the inverted sound speed profiles are in good agreement with conductivity-temperature-depth measurements.Moreover,a posteriori probability analysis is carried out to verify the inversion results.

Adaptive control of parallel manipulators via fuzzy-neural network algorithm

This paper considers adaptive control of parallel manipulators combined with fuzzy-neural network algorithms （FNNA）. With this algorithm, the robustness is guaranteed by the adaptive control law and the parametric uncertainties are eliminated. FNNA is used to handle model uncertainties and external disturbances. In the proposed control scheme, we consider modifying the weight of fuzzy rules and present these rules to a MIMO system of parallel manipulators with more than three degrees-of-freedom （DoF）. The algorithm has the advantage of not requiring the inverse of the Jacobian matrix especially for the low DoF parallel manipulators. The validity of the control scheme is shown through numerical simulations of a 6-RPS parallel manipulator with three DoF.

Forward and backward models for fault diagnosis based on parallel genetic algorithms

In this paper, a mathematical model consisting of forward and backward models is built on parallel genetic algorithms (PGAs) for fault diagnosis in a transmission power system. A new method to reduce the scale of fault sections is developed in the forward model and the message passing interface (MPI) approach is chosen to parallel the genetic algorithms by global sin-gle-population master-slave method (GPGAs). The proposed approach is applied to a sample system consisting of 28 sections, 84 protective relays and 40 circuit breakers. Simulation results show that the new model based on GPGAs can achieve very fast computation in online applications of large-scale power systems.

Lower Bounds and a Nearly Fastest General Parallel Branch-and-Bound Algorithm

In this paper, it is supposed that the B&B algorithm finds the first optimal solution after h nodes have been expanded and m active nodes have been created in the state-space tree. Then the lower bound Ω(m+h log h) of the running time for the general sequential B&B algorithm and the lower bound Ω(m/p+h log p) for the general parallel best-first B&B algorithm in PRAM-CREW are proposed, where p is the number of processors available. Moreover, the lower bound Ω(M/p+H+(H/p) log (H/p)) is presented for the parallel algorithms on distributed memory system, where M and H represent total number of the active nodes and that of the expanded nodes processed by p processors, respectively. In addition, a nearly fastest general parallel best-first B&B algorithm is put forward. The parallel algorithm is the fastest one as p = max{hε, r}, where ε = 1/ rootlogh, and r is the largest branch number of the nodes in the state-space tree.

General and efficient parallel approach of finite element-boundary integral-multilevel fast multipole algorithm

A general and efficient parallel approach is proposed for the first time to parallelize the hybrid finiteelement-boundary-integral-multi-level fast multipole algorithm （FE-BI-MLFMA）. Among many algorithms of FE-BI-MLFMA, the decomposition algorithm （DA） is chosen as a basis for the parallelization of FE-BI-MLFMA because of its distinct numerical characteristics suitable for parallelization. On the basis of the DA, the parallelization of FE-BI-MLFMA is carried out by employing the parallelized multi-frontal method for the matrix from the finiteelement method and the parallelized MLFMA for the matrix from the boundary integral method respectively. The programming and numerical experiments of the proposed parallel approach are carried out in the high perfor- mance computing platform CEMS-Liuhui. Numerical experiments demonstrate that FE-BI-MLFMA is efficiently parallelized and its computational capacity is greatly improved without losing accuracy, efficiency, and generality.

A Class of Parallel Algorithms of Real-TimeNumerical Simulation for Stiff Dynamic System

In this paper a class of real-time parallel modified Rosenbrock methods of numerical simulation is constructed for stiff dynamic systems on a multiprocessor system, and convergence and numerical stability of these methods are discussed. A-stable real-time parallel formula of two-stage third-order and A(α)-stable real-time parallel formula with o ≈ 89.96° of three-stage fourth-order are particularly given. The numerical simulation experiments in parallel environment show that the class of algorithms is efficient and applicable, with greater speedup.

Optimized Parallel Cooperative Spectrum Sensing Strategy Based on Iterative KuhnMunkres Algorithm

Spectrum sensing is the key and premise of cognitive radio( CR). Current parallel cooperative spectrum sensing strategies have some problems,such as large number of cooperative secondary users and lack of consideration for the sensing overhead and the transmission gain. To solve those problems,an optimized parallel cooperative spectrum sensing strategy based on iterative KuhnMunkres( KM) algorithm was proposed. To maximize the total system profit,it considers the tradeoff between the sensing overhead and the transmission gain. Iterative KM algorithm was applied to obtaining the optimal assignment,which indicated when and which channels secondary users should sense. Furthermore,the required detection probability was introduced to avoid unnecessary waste when the accuracy met the system requirement. Monte Carlo simulations show that the proposed strategy can obtain higher total system profit with fewer cooperative secondary users.

PARALLEL IMPLEMENTATION AND OPTIMIZATION OF THE SEBVHOS ALGORITHM

In this paper, a parallel Surface Extraction from Binary Volumes with Higher-Order Smoothness (SEBVHOS) algorithm is proposed to accelerate the SEBVHOS execution. The original SEBVHOS algorithm is parallelized first, and then several performance optimization techniques which are loop optimization, cache optimization, false sharing optimization, synchronization overhead op-timization, and thread affinity optimization, are used to improve the implementation's performance on multi-core systems. The performance of the parallel SEBVHOS algorithm is analyzed on a dual-core system. The experimental results show that the parallel SEBVHOS algorithm achieves an average of 1.86x speedup. More importantly, our method does not come with additional aliasing artifacts, com-paring to the original SEBVHOS algorithm.

Fast Parallel Algorithm for Slicing STL Based on Pipeline

In Additive Manufacturing field, the current researches of data processing mainly focus on a slicing process of large STL files or complicated CAD models. To improve the efficiency and reduce the slicing time, a parallel algorithm has great advantages. However, traditional algorithms can＇t make full use of multi-core CPU hardware resources. In the paper, a fast parallel algorithm is presented to speed up data processing. A pipeline mode is adopted to design the parallel algorithm. And the complexity of the pipeline algorithm is analyzed theoretically. To evaluate the performance of the new algorithm, effects of threads number and layers number are investigated by a serial of experiments. The experimental results show that the threads number and layers number are two remarkable factors to the speedup ratio. The tendency of speedup versus threads number reveals a positive relationship which greatly agrees with the Amdahl＇s law, and the tendency of speedup versus layers number also keeps a positive relationship agreeing with Gustafson＇s law. The new algorithm uses topological information to compute contours with a parallel method of speedup. Another parallel algorithm based on data parallel is used in experiments to show that pipeline parallel mode is more efficient. A case study at last shows a suspending performance of the new parallel algorithm. Compared with the serial slicing algorithm, the new pipeline parallel algorithm can make full use of the multi-core CPU hardware, accelerate the slicing process, and compared with the data parallel slicing algorithm, the new slicing algorithm in this paper adopts a pipeline parallel model, and a much higher speedup ratio and efficiency is achieved.

Concise review of relaxations and approximation algorithms for nonidentical parallel-machine scheduling to minimize total weighted completion times

A class of nonidentical parallel machine scheduling problems are considered in which the goal is to minimize the total weighted completion time. Models and relaxations are collected. Most of these problems are NP-hard, in the strong sense, or open problems, therefore approximation algorithms are studied. The review reveals that there exist some potential areas worthy of further research.

Grid-Based Pseudo-Parallel Genetic Algorithm and Its Application

Aimed at the problems of premature and lower convergence of simple genetic algorithms （SGA）, three ideas --partition the whole search uniformly, multi-genetic operators and multi-populations evolving independently are introduced, and a grid-based pseudo-parallel genetic algorithms （GPPGA） is put forward. Thereafter, the analysis of premature and convergence of GPPGA is made. In the end, GPPGA is tested by both six-peak camel back function, Rosenbrock function and BP network. The result shows the feasibility and effectiveness of GPPGA in overcoming premature and improving convergence speed and accuracy.

Assigning Task by Parallel Genetic Algorithm Based on PVM

Genetic algorithm has been proposed to solve the problem of task assignment. However, it has some drawbacks, e.g., it often takes a long time to find an optimal solution, and the success rate is low. To overcome these problems, a new coarse grained parallel genetic algorithm with the scheme of central migration is presented, which exploits isolated sub populations. The new approach has been implemented in the PVM environment and has been evaluated on a workstation network for solving the task assignment problem. The results show that it not only significantly improves the result quality but also increases the speed for getting best solution.

Multi-objective Collaborative Optimization for Scheduling Aircraft Landing on Closely Spaced Parallel Runways Based on Genetic Algorithms

A scheduling model of closely spaced parallel runways for arrival aircraft was proposed,with multi-objections of the minimum flight delay cost,the maximum airport capacity,the minimum workload of air traffic controller and the maximum fairness of airlines′scheduling.The time interval between two runways and changes of aircraft landing order were taken as the constraints.Genetic algorithm was used to solve the model,and the model constrained unit delay cost of the aircraft with multiple flight tasks to reduce its delay influence range.Each objective function value or the fitness of particle unsatisfied the constrain condition would be punished.Finally,one domestic airport hub was introduced to verify the algorithm and the model.The results showed that the genetic algorithm presented strong convergence and timeliness for solving constraint multi-objective aircraft landing problem on closely spaced parallel runways,and the optimization results were better than that of actual scheduling.