Parallel Simulated Annealing 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 pattern 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.

A Correlation-Redundancy Guided Evolutionary Algorithm and Its Application to High-Dimensional Feature Selection in Classification

The processing of high-dimensional datasets has become unavoidable with the development of information technology. Most of the literature on feature selection (FS) of high-dimensional datasets focuses on improvements in search strategies, ignoring the characteristics of the dataset itself such as the correlation and redundancy of each feature. This could degrade the algorithm's search effectiveness. Thus, this paper proposes a correlation-redundancy guided evolutionary algorithm (CRGEA) to address high-dimensional FS with the objectives of optimizing classification accuracy and the number of features simultaneously. A new correlation-redundancy assessment method is designed for selecting features with high relevance and low redundancy to speed up the entire evolutionary process. In CRGEA, a novel initialization strategy combined with a multiple threshold selection mechanism is developed to produce a high-quality initial population. A local acceleration evolution strategy based on a parallel simulated annealing algorithm and a pruning method is developed, which can search in different directions and perform deep searches combing the annealing stage around the best solutions to improve the local search ability. Finally, the comparison experiments on 16 public high-dimensional datasets verify that the designed CRGEA outperforms other state-of-the-art intelligent algorithms. The CRGEA can efficiently reduce redundant features while ensuring high accuracy.

The Fitness Landscapes Exploration Based on the Parallel Simulated Annealing Algorithm

The Fitness Landscapes Exploration Based on the Parallel Simulated Annealing Algorithm

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.

Optimal allocation of enterprise marketing resources based on hybrid parallel genetic algorithm and simulated annealing algorithm

Abstract Aiming at minimizing the use of marketing resources, this article establishes the mathematical model of marketing resources allocation, designs the algorithm of marketing resources allocation, and compares the examples. An improved heuristic algorithm considering tilt angle matching is proposed and used as a local search algorithm for enterprise marketing resources. We design an innovative optimization strategy that incorporates the concept of tilt angle matching to enhance the local search efficiency of enterprise marketing resource allocation. In addition, we have introduced a novel parallel grouping genetic algorithm (PGGA), which utilizes grouping coding and exon crossover to further enhance the search and optimization efficiency of the solution. PGGA is improved by using adaptive parameters to form IPGGA, which improves the efficiency and convergence speed of enterprise marketing resource allocation. The annealing function of the simulated annealing algorithm is improved, and a model is constructed to solve the problem of enterprise marketing resource allocation. Simulated annealing algorithm is introduced to solve the problem of marketing resource allocation, and the framework of simulated annealing algorithm is analyzed. To solve the problem of fast decay rate of simulated annealing algorithm, Doppler effect function is used to optimize the algorithm. This article mainly uses qualitative and quantitative analysis methods to conduct in-depth research on enterprise marketing resource allocation. It focuses on the planning and allocation of enterprise marketing resources. Through IPGGA-ISAA research and analysis of all kinds of data of enterprise marketing resources, the present situation, efficiency, and main problems of enterprise marketing resources allocation are discussed and analyzed more deeply. Compared with other algorithms, IPGGA-ISAA can better analyze the causes of problems and provide better marketing resource allocation schemes for enterprises.

A Pareto-based hybrid genetic simulated annealing algorithm for multi-objective hybrid production line balancing problem considering disassembly and assembly

ABSTRACT Most existing studies about line balancing problems mainly focus on disassembly and assembly separately, which rarely integrate these two modes into a system. However, as critical activities in the remanufacturing field, assembly and disassembly share many similarities, such as working tools and processing sequence. Thus, this paper proposes a multi-objective hybrid production line balancing problem with a fixed number of workstations (HPLBP-FNW) considering disassembly and assembly to optimise cycle time, total cost, and workload smoothness simultaneously. And a novel Pareto-based hybrid genetic simulated annealing algorithm (PB-HGSA) is designed to solve it. In PB-HGSA, the two-point crossover and hybrid mutation operator are proposed to produce potential non-dominated solutions (NDSs). Then, a local search method based on a parallel simulated annealing algorithm is designed for providing a depth search around the NDSs to balance the global and local search ability. Numerical results by comparing PB-HGSA with the well-known algorithms verify the effectiveness of PB-HGSA in solving HPLBP-FNW. Moreover, the managerial insights based on a case study are given to inspire enterprise companies to consider hybrid production line in the remanufacturing process, which is beneficial to reduce the cycle time and total cost and improve the service life of the equipment.

A New Hybrid Algorithm for Vehicle Routing Optimization

To solve the vehicle routing problem with simultaneous pickup–delivery and time windows (VRPSDPTW), a sine cosine and firefly perturbed sparrow search algorithm (SFSSA) is presented. Based on the standard sparrow search algorithm, the initial population uses tent chaotic mapping to change the population diversity; then, the discoverer location is updated using the sine cosine fluctuation range of the random weight factor, and finally the global population location is updated using the firefly perturbation strategy. In this study, SFSSA was compared with a genetic algorithm (GA), parallel simulated annealing algorithm (p-SA), discrete cuckoo search algorithm (DCS), and novel mimetic algorithm with efficient local search and extended neighborhood (MATE) adopting improved Solomon’s benchmark test cases. The computational results showed that the proposed SFSSA was able to achieve the current optimal solutions for 100% of the nine small-to-medium instances. For large-scale instances, SFSSA obtained the current optimal solutions for 25 out of 56 instances. The experimental findings demonstrated that SFSSA was an effective method for solving the VRPSPDTW problem.

A Novel Parallel Simulated Annealing Methodology to Solve the No-Wait Flow Shop Scheduling Problem with Earliness and Tardiness Objectives

In this paper, the no-wait flow shop problem with earliness and tardiness objectives is considered. The problem is proven to be NP-hard. Recent no-wait flow shop problem studies focused on familiar objectives, such as makespan, total flow time, and total completion time. However, the problem has limited studies with solution approaches covering the concomitant use of earliness and tardiness objectives. A novel methodology for the parallel simulated annealing algorithm is proposed to solve this problem in order to overcome the runtime drawback of classical simulated annealing and enhance its robustness. The well-known flow shop problem datasets in the literature are utilized for benchmarking the proposed algorithm, along with the classical simulated annealing, variants of tabu search, and particle swarm optimization algorithms. Statistical analyses were performed to compare the runtime and robustness of the algorithms. The results revealed the enhancement of the classical simulated annealing algorithm in terms of time consumption and solution robustness via parallelization. It is also concluded that the proposed algorithm could outperform the benchmark metaheuristics even when run in parallel. The proposed algorithm has a generic structure that can be easily adapted to many combinatorial optimization problems.

Asynchronous simulated annealing on the placement problem: A beneficial race condition

Race conditions, which occur when compute workers do not synchronise correctly, are considered undesirable in parallel computing, as they introduce often-unintended stochastic behaviour. This study presents an asynchronous parallel algorithm with a race condition, and demonstrates that it reaches a superior solution faster than the equivalent synchronous algorithm without the race condition. Specifically, a parallel simulated annealing algorithm that solves a graph mapping problem (placement) is used to explore this. This paper illustrates how problem size and degree of parallelism affects both the collision rate caused by the race condition, and convergence time. The asynchronous approach reaches a superior solution in half the time of the equivalent synchronous approach. The solver presented here can be applied to application deployment in distributed systems, and the concept can be applied to problems solvable by global optimisation methods, where fitness errors can be tolerated in exchange for faster execution.

Asynchronous MMC PSA inversion of transient electromagnetic data

This paper focuses on low computational efficiency in simulated annealing (SA) inversion of Transient Electromagnetic (TEM) data. Asynchronous multiple Markov chains (MMC) parallel strategy is a very promising SA acceleration method, which can be accelerated almost linearly. However, this method also reduces the accuracy of the solution. To overcome this problem, we added the solution set strategy to the asynchronous MMC parallel simulated annealing (PSA) algorithm for the first time. In this new algorithm, each thread independently searches for direction and exchanges data with the solution set in the shared memory. We used both the synthetic and field data to test the new algorithm. The synthetic data tests showed that the MMC PSA results are better than those of the original MMC PSA. We analyzed the efficiency of the new algorithm. Compared with the sequential VFSA, the maximum speedup of the new algorithm is approximately 10 times. The field data test also showed that the improved MMC PSA algorithm has good practicability. These tests demonstrate that the improved algorithm is effective, showing that its convergence speed is greatly improved without reducing the accuracy.

A Solution for the Full-Load Collection Vehicle Routing Problem With Multiple Trips and Demands: An Application in Beijing

Municipal solid waste (MSW) collection has become a major challenge for clean city management and social sustainable development in developing economies. A new variant of the collection vehicle routing problem (CVRP) is addressed with the characteristics of full loads and multiple trips of the collection vehicles, and multiple demands of the garbage facilities, which is called the collection vehicle routing problem of the garbage facilities (CVRPGF) in this study. Dummy customers are introduced to equivalently transform the CVRPGF problem to the vehicle routing problem with simultaneous pickup-delivery and time windows (VRPSPDTW). A parallel simulated annealing algorithm (Par-SAA) is developed to solve the VRPSPDTW problem. When applied to an international benchmark dataset, the computational results prove the superiority of the proposed algorithm, in which the number of collection vehicles (NV) in four instances is reduced by one. Finally, when the model and algorithm are applied to a real CVRPGF problem in the Xuanwu District of Beijing, the NV needed is reduced by 30%, and the total travel time is decreased by 12%. Thus, the effectiveness of the Par-SAA is demonstrated, and the proposed solution has practical value in China.

An experimental evaluation of a parallel simulated annealing approach for the 0–1 multidimensional knapsack problem

The recent progresses in the development and improvement of multicore/manycore architectures instigate the design of algorithms capable of exploring the functionalities provided by these architectures to solve more efficiently hard problems. The NP-hard class of problems contains several problems which demand for efficient alternative solutions, since there is a wide range or real-world problems which can be modeled as one of them and it is unknown if they can be exactly solved in feasible time. The 0–1 multidimensional knapsack problem (0–1 MKP) is one of the most emblematic NP-hard problems and this work focuses on the proposal of a parallel simulated annealing algorithm using GPGPU. The results achieved by the parallel SA were compared to other reference works and showed that GPGPU is effective on the task of obtaining better quality solutions in reduced execution time when compared to sequential programs. Our proposed approach can be adapted to other parallel platforms.

A speculative parallel simulated annealing algorithm based on Apache Spark

SummarySimulated annealing (SA) is an effective method for solving unconstrained optimization problems and has been widely used in machine learning and neural network. Nowadays, in order to optimize complex problems with big data, the SA algorithm has been implemented on big data platform and obtains a certain speedup. However, the efficiency for such implementation is still limited because the conventional SA algorithm still runs with low parallelism on new platforms and the computing resource cannot be fully utilized. For these problems, this paper raised a speculative parallel SA algorithm based on Apache Spark to expand the algorithm's parallelism and enhance its efficiency. In this paper, first, the inner dependencies, which stop conventional algorithm, run in parallel, are analyzed. Then, based on the analysis, the Software Thread‐Level Speculation technique is employed to help the conventional algorithm overcome the dependencies and make it run concurrently. Finally, a new parallel SA algorithm with speculation mechanism is proposed and implemented on Apache Spark. The experiments show that, for big data problems, the proposed algorithm could achieve an optimal parallelism when comparing the traditional algorithm without speculation on Apache Spark. Moreover, the execution efficiency of simulated annealing process can be markedly enhanced by the proposed algorithm.

Theoretical analysis of simultaneously improving the light coupling efficiency and bandwidth between two separated grating couplers using integrated distributed Bragg reflectors

The performance of free space optical (FSO) communication system is limited by atmospheric turbulent extremely. Adaptive optics (AO) is the significant method to overcome the atmosphere disturbance. Especially, for the strong scintillation effect, the sensor-less AO system plays a major role for compensation. In this paper, a modified artificial fish school (MAFS) algorithm is proposed to compensate the aberrations in the sensor-less AO system. Both the static and dynamic aberrations compensations are analyzed and the performance of FSO communication before and after aberrations compensations is compared. In addition, MAFS algorithm is compared with artificial fish school (AFS) algorithm, stochastic parallel gradient descent (SPGD) algorithm and simulated annealing (SA) algorithm. It is shown that the MAFS algorithm has a higher convergence speed than SPGD algorithm and SA algorithm, and reaches the better convergence value than AFS algorithm, SPGD algorithm and SA algorithm. The sensor-less AO system with MAFS algorithm effectively increases the coupling efficiency at the receiving terminal with fewer numbers of iterations. In conclusion, the MAFS algorithm has great significance for sensor-less AO system to compensate atmospheric turbulence in FSO communication system.

A hybrid meta-heuristic algorithm to minimize the number of tardy jobs in a dynamic two-machine flow shop problem

In this paper, first, the problem of dynamic two-machine flow shop scheduling with the objective of minimizing the number of tardy jobs is investigated. Second, a hybrid algorithm based on genetic algorithm and parallel simulated annealing algorithm is presented. In order to solve large scale instances of the problem and to generate the initial population for the hybrid approach, a heuristic algorithm is also presented. To evaluate the efficiency of the proposed algorithms, they are compared with an optimal branch-and-bound algorithm which has been already developed in the literature. Computational experiments demonstrate that the proposed hybrid algorithm can solve the entire small-sized problems and more than 95% of medium-sized problems optimally.

A new hybridized algorithm based on Population-Based Simulated Annealing with an experimental study of phase transition in 3-SAT

This paper is about experiments for satisfiability problem using a new algorithm (GR-MM-PBSA) that improves the algorithm Population-Based Simulated Annealing (PBSA). GR-MM-PBSA runs in a parallel way Simulated Annealing (SA) and Threshold Annealing (TA) algorithms with a Golden Ratio space search strategy and Markovian Model to select initial and final temperature. In this paper we execute differents hybridized Simulated Annealing (or Threshold Accepting) algorithms and compares the efficiency of these, using a metric based on transition phase effect. Simulated Annealing Algorithms (SAA) theoretically can reach the optimum if the control parameters and cooling scheme are chosen correctly. All algorithms are compared with a metric based on transition phase obtained for 3-SAT instances. This paper shows the results of SAA hybridizations are more efficient than the original algorithm, without increasing their computational complexity. We also show the experimental data about runs with 820 3-SAT instances with ratio clauses-variables between 2.0 to 6.0.

A Parallel Simulated Annealing Algorithm for Weapon-Target Assignment Problem

Weapon-target assignment (WTA) is a combinatorial optimization problem and is known to be NP-complete. The WTA aims to best assignment of weapons to targets to minimize the total expected value of the surviving targets. Exact methods can solve only small-size problems in a reasonable time. Although many heuristic methods have been studied for the WTA in the literature, a few parallel methods have been proposed. This paper presents parallel simulated algorithm (PSA) to solve the WTA. The PSA runs on GPU using CUDA platform. Multi-start technique is used in PSA to improve quality of solutions. 12 problem instances (up to 200 weapons and 200 targets) generated randomly are used to test the effectiveness of the PSA. Computational experiments show that the PSA outperforms SA on average and runs up to 250x faster than a single-core CPU.

Energy-aware framework with Markov chain-based parallel simulated annealing algorithm for dynamic management of virtual machines in cloud data centers

Significant savings in the energy consumption, without sacrificing service level agreement (SLA), are an excellent economic incentive for cloud providers. By applying efficient virtual Machine placement and consolidation algorithms, they are able to achieve these goals. In this paper, we propose a comprehensive technique for optimum energy consumption and SLA violation reduction. In the proposed approach, the issues of allocation and management of virtual machines are divided into smaller parts. In each part, new algorithms are proposed or existing algorithms have been improved. The proposed method performs all steps in distributed mode and acts in centralized mode only in the placement of virtual machines that require a global vision. For this purpose, the population-based or parallel simulated annealing (SA) algorithm is used in the Markov chain model for virtual machines placement policy. Simulation of algorithms in different scenarios in the CloudSim confirms better performance of the proposed comprehensive algorithm.

Orthogonal parallel MCMC methods for sampling and optimization

Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. A well-known class of MC methods are Markov Chain Monte Carlo (MCMC) algorithms. In order to foster better exploration of the state space, specially in high-dimensional applications, several schemes employing multiple parallel MCMC chains have been recently introduced. In this work, we describe a novel parallel interacting MCMC scheme, called orthogonal MCMC (O-MCMC), where a set of “vertical” parallel MCMC chains share information using some “horizontal” MCMC techniques working on the entire population of current states. More specifically, the vertical chains are led by random-walk proposals, whereas the horizontal MCMC techniques employ independent proposals, thus allowing an efficient combination of global exploration and local approximation. The interaction is contained in these horizontal iterations. Within the analysis of different implementations of O-MCMC, novel schemes in order to reduce the overall computational cost of parallel Multiple Try Metropolis (MTM) chains are also presented. Furthermore, a modified version of O-MCMC for optimization is provided by considering parallel Simulated Annealing (SA) algorithms. Numerical results show the advantages of the proposed sampling scheme in terms of efficiency in the estimation, as well as robustness in terms of independence with respect to initial values and the choice of the parameters.

A heuristic and parallel simulated annealing algorithm for variable selection in near‐infrared spectroscopy analysis

A new heuristic and parallel simulated annealing algorithm was proposed for variable selection in near‐infrared spectroscopy analysis. The algorithm employs a parallel mechanism to enhance the search efficiency, a heuristic mechanism to generate high‐quality candidate solutions, and the concept of Metropolis criterion to estimate accuracy of the candidate solutions. Several near‐infrared datasets have been evaluated under the proposed new algorithm, with partial least squares leading to improved analytical figures of merit upon wavelength selection. Improved robust and predictive regression models were obtained by the new algorithm. The method could also be helpful in other chemometric activities such as classification or quantitative structure‐activity relationship problems.