Simulated Annealing-based Approach Research Articles

A Resource Allocation Model for Mixed-Grid Optical Networks

Due to cost and service level agreement, upgrading network infrastructure is always challenging for network operators, particularly for backbone optical networks (ONs). Nowadays, to support several bandwidth-hungry applications, network operators adopt the migration process from fixed-grid nodes to flex-grid in backbone ONs. However, without proper planning, it may lead to inefficient resource utilization. This paper proposes optimization models for resource allocation in mixed-grid ONs, which initially identifies the set of eligible nodes for upgradation from fixed-grid to flex-grid capabilities for a given set of offline traffic and then uses them in the network to maximize the resource utilization. We formulate an integer linear programming (ILP) problem, named Opti-node, to determine the set of nodes selected for fixed-grid to flex-grid conversion in mixed-grid ONs. We then introduce an ILP, named Opti-resource, to enhance the resource utilization based on the determined set of nodes converted from fixed-grid to flex-grid using Opti-node. We prove that the decision problem of resource allocation in mixed-grid ONs is NP-Complete. Heuristic approaches based on the ILP-based approach and simulated annealing are introduced for a large problem size when Opti-resource is not tractable. We evaluate the proposed resource allocation model extensively, considering different networks, and observe that the simulated annealing-based approach performs better in terms of blocking ratio and spectrum utilization than the ILP-based heuristic approach, but it requires additional computation time.

LSTM input timestep optimization using simulated annealing for wind power predictions.

Wind energy is one of the renewable energy sources like solar energy, and accurate wind power prediction can help countries deploy wind farms at particular locations yielding more electricity. For any prediction problem, determining the optimal time step (lookback) information is of primary importance, and using information from previous timesteps can improve the prediction scores. This article uses simulated annealing to find an optimal time step for wind power prediction. Finding an optimal timestep is computationally expensive and may require brute-forcing to evaluate the deep learning model at each time. This article uses simulated annealing to find an optimal time step for wind power prediction. The computation time was reduced from 166 hours to 3 hours to find an optimal time step for wind power prediction with a simulated annealing-based approach. We tested the proposed approach on three different wind farms with a training set of 50%, a validation set of 25%, and a test set of 25%, yielding MSE of 0.0059, 0.0074, and 0.010 for each wind farm. The article presents the results in detail, not just the mean square root error.

An improved thermodynamic simulated annealing-based approach for resource-skewness-aware and power-efficient virtual machine consolidation in cloud datacenters

Cloud computing attracted great attention in both industry and research communities for the sake of its ubiquitous, elasticity and economic services. The first class concern of cloud providers is power management for both reducing their total cost of ownership and green computing objectives. To reach the goal, a system framework is presented which has different modules. The main concentration of the paper is on virtual machine (VM) consolidation module which launches users requested VMs on the minimum number of active servers to reduce datacenter total power consumption (TPC). In this paper, the VMs consolidation is abstracted to two-dimensional bin-packing problem and also is formulated to an integer linear programming. Since the papers in the literature scarcely are aware of skewness in resources of requested VMs and for discrete nature of search space, this paper presents the resource skewness-aware VMs consolidation algorithm based on improved thermodynamic simulated annealing approach because resource skewness potentially compels the algorithm to activate additional servers. The proposed SA-based algorithm is validated in extensive scenarios with different resource skewness in comparison with two heuristics and two meta-heuristics. The average results reported from different scenarios proves superiority of proposed algorithm in comparison with other approaches in terms of the number of used servers, TPC, and total resource wastage of datacenter.

A simulated annealing-based approach for a real case study of vehicle routing problem with a heterogeneous fleet and time windows

We propose a meta-heuristic algorithm for addressing a real case of vehicle routing problem with a heterogeneous fleet and time windows. Our proposal consists of generating an initial feasible solution using the solution of a travelling salesman problem and, subsequently, refining it through simulated annealing. In this work, we evaluate the suitability of our development to planning the distribution of a regional distribution centre of a food company to customers within the urban perimeter of Cali, Colombia. Following the company's customer satisfaction goals, our primary aim is to route to reach a service level of 95% while reducing operational costs. Our results on our real case study show that our meta-heuristic algorithm finds better routes than current ones, in terms of costs and service levels, in a relatively short time.

A simulated annealing-based approach for a real case study of vehicle routing problem with a heterogeneous fleet and time windows

A simulated annealing-based approach for a real case study of vehicle routing problem with a heterogeneous fleet and time windows

Modularity assessment in reconfigurable manufacturing system (RMS) design: an Archived Multi-Objective Simulated Annealing-based approach

Enhancing productivity, reducing inaccuracy and avoiding time waste at changeover are considered major drivers in manufacturing system design. One of the emerging paradigms concerned with these characteristics is reconfigurable manufacturing systems (RMSs). The high responsiveness and performance efficiencies of RMS make it a convenient manufacturing paradigm for and flexible enabler of mass customization. The RMS offers customized flexibility and a variety of alternatives as features thanks to its reconfigurable machine tool (RMT). These machines represent a major component of RMS and are based on an adjustable, modular and reconfigurable structure. Hence, the system modularity is of great importance. This paper outlines a multi-objective approach to optimize the RMS design. Three objectives are considered: the maximization of the system modularity, the minimization of the system completion time and the minimization of the system cost. We developed a modularity-based multi-objective approach that uses an adapted version of the “Archived Multi-Objective Simulated Annealing” (AMOSA) method to solve the optimization problem by selecting from a set of candidate machines the most suitable ones. Implemented, the decision maker can use a multi-objective decision making tool based on the well-known “Technique for Order of Preference by Similarity to Ideal Solution” (TOPSIS) to choose the best solution in the Pareto front according to his preferences. We demonstrated the applicability of the proposed approach through an illustrative example and an analysis of the obtained numerical results.

A simulated annealing-based approach for the joint optimization of production/inventory and preventive maintenance policies

Even if more reliable than the past, the performance of modern manufacturing systems is still affected by machine’s deteriorations and breakdowns. As a consequence, adequate maintenance programs must be implemented to adequately satisfy demands during manufacturing stops due to unexpected failures or preventive maintenance (PM) actions. Despite production and maintenance are closely related issues, their joint optimization has become an important research topic just during the last decade. Therefore, the present paper proposes a model for the combined optimization of production/inventory control and PM policies with the aim of minimizing the total expected cost per unit time. The model is formulated referring to a continuous production system characterized by a random deteriorating behavior so that the presence of a buffer is considered to ensure a continuous products supply during interruptions of service caused by breakdowns or planned maintenance actions on the production system. Unlike the main part of the existing literature, non-restriction on the failure occurrence is here forced, namely that the manufacturing system may fail at any age within the production cycle as well as more than one failure may occur during the same period. A Simulated Annealing-based algorithm combined with a Monte Carlo simulation module is proposed as a resolution approach. The robustness of the developed algorithm is demonstrated by means of repeated runs of different simulated scenarios characterized by diverse sets of cost parameters. Results also confirm the effectiveness of the proposed three-level theoretical inventory profile.

Using simulated annealing for computing cost-aware covering arrays

The configuration spaces of software systems are often too large to test exhaustively. Combinatorial interaction testing approaches, such as covering arrays, systematically sample the configuration space and test only the selected configurations. In an attempt to reduce the cost of testing, standard t-way covering arrays aim to cover all t-way combinations of option settings in a minimum number of configurations. By doing so, they simply assume that every configuration costs the same. When the cost varies from one configuration to another, however, minimizing the number of configurations is not necessarily the same as minimizing the cost. To overcome this issue, we have recently introduced cost-aware covering arrays. In a nutshell, a t-way cost-aware covering array is a standard t-way covering array that “minimizes” a given cost function modeling the actual cost of testing. In this work we develop a simulated annealing-based approach to compute cost-aware covering arrays, which takes as input a configuration space model enhanced with a cost function and computes a cost-aware covering array by using two alternating neighboring state generation strategies together with a fitness function expressed as a weighted sum of two objectives: covering all required t-way option setting combinations and minimizing the cost function. To the best of our knowledge, the proposed approach is the first approach that computes cost-aware covering arrays for general, non-additive linear cost functions with multiplicative interaction effects. We evaluate the approach both by conducting controlled experiments, in which we systematically vary the input models to study the sensitivity of the approach to various factors and by conducting experiments using real cost functions for real software systems. We also compare cost-aware covering arrays to standard covering arrays constructed by well-known algorithms and study how fast the construction costs are compensated by the cost reductions provided. Our empirical results suggest that the proposed approach is more effective and efficient than the existing approaches.

Designing Flexible Stochastic Dynamic Layout: An Integrated Firefly and Chaotic Simulated Annealing-Based Approach

Facility layout problem (FLP) is non-deterministic and a polynomial time problem (NP-hard). In practice, the FLP is affected due to the presence of fluctuating products demand and thus results in increased material handling cost. Such layout problems are known as stochastic dynamic facility layout problem (SDFLP). It is well known that the computational time to solve SDFLP is exponential; therefore, it is hard to solve using exact approaches. Various meta-heuristic algorithms are used for solving such problems. The aim of the paper is to formulate a novel meta-heuristic for solving SDFLP, which minimizes the material handling cost better than the prevailing meta-heuristic. A hybridized methodology of combining firefly algorithm (FA) and chaotic simulated annealing (CSA), i.e. hybrid FA/CSA is proposed and evaluated to find an optimal solution for SDFLP. The performance of the algorithm, in terms of total material handling cost, is compared with simulated annealing, chaotic simulated annealing and hybrid ant colony optimization/simulated annealing using data from the literature.

3D Face Recognition Using Simulated Annealing and the Surface Interpenetration Measure

This paper presents a novel automatic framework to perform 3D face recognition. The proposed method uses a Simulated Annealing-based approach (SA) for range image registration with the Surface Interpenetration Measure (SIM), as similarity measure, in order to match two face images. The authentication score is obtained by combining the SIM values corresponding to the matching of four different face regions: circular and elliptical areas around the nose, forehead, and the entire face region. Then, a modified SA approach is proposed taking advantage of invariant face regions to better handle facial expressions. Comprehensive experiments were performed on the FRGC v2 database, the largest available database of 3D face images composed of 4,007 images with different facial expressions. The experiments simulated both verification and identification systems and the results compared to those reported by state-of-the-art works. By using all of the images in the database, a verification rate of 96.5 percent was achieved at a False Acceptance Rate (FAR) of 0.1 percent. In the identification scenario, a rank-one accuracy of 98.4 percent was achieved. To the best of our knowledge, this is the highest rank-one score ever achieved for the FRGC v2 database when compared to results published in the literature.

Minimising total flow time in permutation flow shop scheduling using a simulated annealing-based approach

This paper presents an efficient simulated annealing algorithm for minimising the total flow time in n-job, m-machine permutation flow shop scheduling problems. Empirical results demonstrate that the proposed approach is competitive with the best three methods in the flow shop literature. Statistical tests of significance are used to validate the improvement in solution quality.

A simulated annealing-based approach to the optimal synthesis of heat-integrated distillation sequences

A simulated annealing-based approach to synthesis of multi-component distillation systems is developed. An encoding procedure that makes use of an integer number series is developed to represent and manipulate the flow sheet structure of the system. With the representation procedure, the overall synthesis problem is formulated as an implicit mixed-integer nonlinear programming (MINLP) problem. A simulated annealing approach suitable for MINLP optimization is adopted and improved to solve the problem. Four example problems, including large-scale ones are solved to illustrate the approach.

Simulated Annealing Approach to the Optimal Synthesis of Distillation Column with Intermediate Heat Exchangers

This article presents a simulated annealing-based approach to the optimal synthesis of distillation column considering intermediate heat exchangers arrangements. The number of intermediate condensers and/or intermediate reboilers, the placement locations, the operating pressure of column, and the heat duties of intermediate heat exchangers are treated as optimization variables. A novel coding procedure making use of an integer number series is proposed to represent and manipulate the structure of system and a stage-to-stage method is used for column design and cost calculation. With the representation procedure, the synthesis problem is formulated as a mixed integer nonlinear programming (MINLP) problem, which can then be solved with an improved simulated annealing algorithm. Two examples are illustrated to show the effectiveness of the suggested approach.

Robust Transmission of Speech LSFs Using Hidden Markov Model-Based Multiple Description Index Assignments

Speech coding techniques capable of generating encoded representations which are robust against channel losses play an important role in enabling reliable voice communication over packet networks and mobile wireless systems. In this paper, we investigate the use of multiple description index assignments (MDIAs) for loss-tolerant transmission of line spectral frequency (LSF) coefficients, typically generated by state-of-the-art speech coders. We propose a simulated annealing-based approach for optimizing MDIAs for Markov-model-based decoders which exploit inter-and intraframe correlations in LSF coefficients to reconstruct the quantized LSFs from coded bit streams corrupted by channel losses. Experimental results are presented which compare the performance of a number of novel LSF transmission schemes. These results clearly demonstrate that Markov-model-based decoders, when used in conjunction with optimized MDIA, can yield average spectral distortion much lower than that produced by methods such as interleaving/interpolation, commonly used to combat the packet losses.

A simulated annealing algorithm for manufacturing cell formation problems

The cell formation problem determines the decomposition of the manufacturing cells of a production system in which machines are assigned to these cells to process one or more part families so that each cell is operated independently and the intercellular movements are minimized or the number of parts flow processed within cells is maximized. In this study, a simple yet effective simulated annealing-based approach, SACF, is proposed to solve the cell formation problem. Considerable efforts are devoted to the design of parts and machine assignment procedures to direct SACF to converge to solutions with good values of grouping efficacy. A set of 25 test problems with various sizes drawn from the literature is used to test the performance of the proposed heuristic algorithm. The corresponding results are compared to several well-known algorithms published. The comparative study shows that the proposed SACF algorithm improves the grouping efficacy for 72% of the test problems. The proposed algorithm should thus be useful to both practitioners and researchers.

A simulated annealing-based optimization approach for integrated process planning and scheduling

A job shop needs to deal with a lot of make-to-order business, in which the orders are usually diverse in types but each one is small in volume. To increase the flexibility and responsiveness of the job shop in the more competitive market, process planning and scheduling modules have been actively developed and deployed. The functions of the two modules are usually complementary. It is ideal to integrate them more tightly to achieve the global optimization of product development and manufacturing. In this paper, a unified representation model and a simulated annealing-based approach have been developed to facilitate the integration and optimization process. In the approach, three strategies, including processing flexibility, operation sequencing flexibility and scheduling flexibility, have been used for exploring the search space to support the optimization process effectively. Performance criteria, such as makespan, the balanced level of machine utilization, job tardiness and manufacturing cost, have been systematically defined to make the algorithm adaptive to meet various practical requirements. Case studies under various working conditions and the comparisons of this approach with two modern evolutionary approaches are given. The merits and characteristics of the approach are thereby highlighted.

Profile-Driven Instruction Mapping for Dataflow Architectures

Dataflow architectures provide an abundance of computing units that can be statically or dynamically configured to match the computing requirements of the given application. Wire delay has a reduced impact in dataflow architectures because only neighboring architectural entities are allowed to communicate within a single clock cycle. In this paper, the authors propose integer linear programming (ILP)-based placement and routing algorithms for mapping dataflow graphs (DFGs) to dataflow machines. The optimization process is guided by profiling information available from the compiler. The goal is to minimize the total execution time of the given application represented by a DFG under architectural constraints. A hierarchical method to handle the complexity of the initial ILP formulation is proposed. The profile-driven ILP algorithm reduces the total execution time of benchmark applications compared to the conventional wirelength-driven ILP approach. In addition, the ILP-based approach outperforms simulated annealing-based approach

A fuzzy goal-programming model of machine-tool selection and operation allocation problem in FMS: a quick converging simulated annealing-based approach

Fuzzy set theory has been widely accepted in modelling of some of the vague phenomena and relationships that are non-stochastic in nature. The problem of machine-tool selection and operation allocations in a flexible manufacturing system usually involves parameters that are non-deterministic and imprecise in nature. This paper adopts a fuzzy goal-programming model having multiple conflicting objectives and constraints pertaining to the machine-tool selection and operation allocation problem, and a new random search optimization methodology termed Quick Converging Simulated Annealing (QCSA) is being used to resolve the underlying issues. The main feature of the proposed QCSA algorithm is that it outperforms genetic algorithm and simulated annealing approaches as far as convergence to the near optimal solution is concerned. Moreover, it is also capable of eluding local optima. Extensive experiments are performed on a problem involving real-life complexities, and some of the computational results are reported to validate the efficacy of the proposed algorithm.

Protocols for negotiating complex contracts

Research to date on negotiation protocols has focused almost exclusively on defining simple contracts consisting of one or a few independent issues and a relatively small number of possible contracts. Many real-world contracts, in contrast, are much more complex, consisting of multiple interdependent issues and intractably large contract spaces. The family of negotiation protocols we've developed make substantial progress toward achieving near-optimal outcomes for negotiations with binary issue dependencies. We propose a simulated annealing-based approach, a refined version based on a parity-maintaining annealing mediator, and an unmediated version of the negotiation protocol.

A Simulated Annealing-Based Approach to Three-Dimensional Component Packing

This paper introduces a simulated annealing-based approach to three-dimensional component packing that employs simulated annealing to generate optimal solutions. Simulated annealing has been used extensively for two-dimensional layout of VLSI circuits; this research extends techniques developed for two-dimensional layout optimization to three-dimensional problems which are more representative of mechanical engineering applications. This research also provides a framework in which to solve general component layout problems.