Showing papers on "Simulated annealing published in 2012"

Finding Low-Energy Conformations of Lattice Protein Models by Quantum Annealing

Abstract: Lattice protein folding models are a cornerstone of computational biophysics. Although these models are a coarse grained representation, they provide useful insight into the energy landscape of natural proteins. Finding low-energy threedimensional structures is an intractable problem even in the simplest model, the Hydrophobic-Polar (HP) model. Description of protein-like properties are more accurately described by generalized models, such as the one proposed by Miyazawa and Jernigan (MJ), which explicitly take into account the unique interactions among all 20 amino acids. There is theoretical and experimental evidence of the advantage of solving classical optimization problems using quantum annealing over its classical analogue (simulated annealing). In this report, we present a benchmark implementation of quantum annealing for lattice protein folding problems (six different experiments up to 81 superconducting quantum bits). This first implementation of a biophysical problem paves the way towards studying optimization problems in biophysics and statistical mechanics using quantum devices.

Adaptive simulated annealing (asa)

Abstract: ADAPTIVE SIMULATED ANNEALING (ASA) ©Lester Ingberingber@ingber.comingber@alumni.caltech.eduAdaptive Simulated Annealing (ASA) is a C-language code developed to statistically find the bestglobal fit of a nonlinear constrained non-convex cost-function overaD-dimensional space. Thisalgorithm permits an annealing schedule for “temperature”T decreasing exponentially in annealing-timek, T = T

Hybrid Simulated Annealing–Tabu Search Method for Optimal Sizing of Autonomous Power Systems With Renewables

Abstract: Small autonomous power systems (SAPS) that include renewable energy sources are a promising option for isolated power generation at remote locations. The optimal sizing problem of SAPS is a challenging combinatorial optimization problem, and its solution may prove a very time-consuming process. This paper initially investigates the performance of two popular metaheuristic methods, namely, simulated annealing (SA) and tabu search (TS), for the solution of SAPS optimal sizing problem. Moreover, this paper proposes a hybrid SA-TS method that combines the advantages of each one of the above-mentioned metaheuristic methods. The proposed method has been successfully applied to design an SAPS in Chania region, Greece. In the study, the objective function is the minimization of SAPS cost of energy (€/kWh), and the design variables are: 1) wind turbines size, 2) photovoltaics size, 3) diesel generator size, 4) biodiesel generator size, 5) fuel cells size, 6) batteries size, 7) converter size, and 8) dispatch strategy. The performance of the proposed hybrid optimization methodology is studied for a large number of alternative scenarios via sensitivity analysis, and the conclusion is that the proposed hybrid SA-TS improves the obtained solutions, in terms of quality and convergence, compared to the solutions provided by individual SA or individual TS methods.

Intelligent Energy Resource Management Considering Vehicle-to-Grid: A Simulated Annealing Approach

Abstract: This paper proposes a simulated annealing (SA) approach to address energy resources management from the point of view of a virtual power player (VPP) operating in a smart grid. Distributed generation, demand response, and gridable vehicles are intelligently managed on a multiperiod basis according to V2G users' profiles and requirements. Apart from using the aggregated resources, the VPP can also purchase additional energy from a set of external suppliers. The paper includes a case study for a 33 bus distribution network with 66 generators, 32 loads, and 1000 gridable vehicles. The results of the SA approach are compared with a methodology based on mixed-integer nonlinear programming. A variation of this method, using ac load flow, is also used and the results are compared with the SA solution using network simulation. The proposed SA approach proved to be able to obtain good solutions in low execution times, providing VPPs with suitable decision support for the management of a large number of distributed resources.

Black-Box Search by Unbiased Variation

Abstract: The complexity theory for black-box algorithms, introduced by Droste, Jansen, and Wegener (Theory Comput Syst 39:525---544, 2006), describes common limits on the efficiency of a broad class of randomised search heuristics There is an obvious trade-off between the generality of the black-box model and the strength of the bounds that can be proven in such a model In particular, the original black-box model provides for well-known benchmark problems relatively small lower bounds, which seem unrealistic in certain cases and are typically not met by popular search heuristics In this paper, we introduce a more restricted black-box model for optimisation of pseudo-Boolean functions which we claim captures the working principles of many randomised search heuristics including simulated annealing, evolutionary algorithms, randomised local search, and others The key concept worked out is an unbiased variation operator Considering this class of algorithms, significantly better lower bounds on the black-box complexity are proved, amongst them an Ω(nlogn) bound for functions with unique optimum Moreover, a simple unimodal function and plateau functions are considered We show that a simple (1+1) EA is able to match the runtime bounds in several cases

Finding low-energy conformations of lattice protein models by quantum annealing

Abstract: Lattice protein folding models are a cornerstone of computational biophysics. Although these models are a coarse grained representation, they provide useful insight into the energy landscape of natural proteins. Finding low-energy three-dimensional structures is an intractable problem even in the simplest model, the Hydrophobic-Polar (HP) model. Exhaustive search of all possible global minima is limited to sequences in the tens of amino acids. Description of protein-like properties are more accurately described by generalized models, such as the one proposed by Miyazawa and Jernigan (MJ), which explicitly take into account the unique interactions among all 20 amino acids. There is theoretical and experimental evidence of the advantage of solving classical optimization problems using quantum annealing over its classical analogue (simulated annealing). In this report, we present a benchmark implementation of quantum annealing for a biophysical problem (six different experiments up to 81 superconducting quantum bits). Although the cases presented here can be solved in a classical computer, we present the first implementation of lattice protein folding on a quantum device under the Miyazawa-Jernigan model. This paves the way towards studying optimization problems in biophysics and statistical mechanics using quantum devices.

Reconfiguration and Capacitor Placement Simultaneously for Energy Loss Reduction Based on an Improved Reconfiguration Method

Abstract: Network reconfiguration and capacitor placement have been widely employed to reduce power losses and maintain voltage profiles within permissible limits in distribution systems. Reconfiguration method proposed in this paper is based on a simple branch exchange method of single loop. In this simple method of branch exchange, loops selection sequence affects the optimal configuration and the network loss. Therefore, this method has been improved by optimizing the sequence of loops selection for minimizing the energy losses in this paper. Also, a joint optimization algorithm is proposed for combining this improved method of reconfiguration and capacitor placement and therefore maximum loss reduction. For more practical application of the proposed method, different load patterns are considered and a fast method of total energy loss calculation is employed for the economic optimization of energy losses during the planning horizon. Discrete genetic algorithm (GA) is used to optimize the location and size of capacitors and the sequence of loops selection. In fact, the capacitor sizes have been considered as discrete variables. Simulated annealing (SA) is also applied to compare the performance of convergence. The proposed algorithm is effectively tested on a real life 77-bus distribution system with four different kinds of load patterns.

Motion-guided mechanical toy modeling

Abstract: We introduce a new method to synthesize mechanical toys solely from the motion of their features. The designer specifies the geometry and a time-varying rotation and translation of each rigid feature component. Our algorithm automatically generates a mechanism assembly located in a box below the feature base that produces the specified motion. Parts in the assembly are selected from a parameterized set including belt-pulleys, gears, crank-sliders, quick-returns, and various cams (snail, ellipse, and double-ellipse). Positions and parameters for these parts are optimized to generate the specified motion, minimize a simple measure of complexity, and yield a well-distributed layout of parts over the driving axes. Our solution uses a special initialization procedure followed by simulated annealing to efficiently search the complex configuration space for an optimal assembly.

Review: A particle swarm optimization approach to clustering

Abstract: The clustering problem has been studied by many researchers using various approaches, including tabu searching, genetic algorithms, simulated annealing, ant colonies, a hybridized approach, and artificial bee colonies. However, almost none of these approaches have employed the pure particle swarm optimization (PSO) technique. This study presents a new PSO approach to the clustering problem that is effective, robust, comparatively efficient, easy-to-tune and applicable when the number of clusters is either known or unknown. The algorithm was tested using two artificial and five real data sets. The results show that the algorithm can successfully solve both clustering problems with both known and unknown numbers of clusters.

The location-routing problem with simultaneous pickup and delivery: Formulations and a heuristic approach

Abstract: In this paper, we consider a variant of the Location-Routing Problem (LRP), namely the LRP with simultaneous pickup and delivery (LRPSPD). The LRPSPD seeks to minimize total cost by simultaneously locating the depots and designing the vehicle routes that satisfy pickup and delivery demand of each customer at the same time. We propose two polynomial-size mixed integer linear programming formulations for the problem and a family of valid inequalities to strengthen the formulations. While the first formulation is a node-based formulation, the second one is a flow-based formulation. Furthermore, we propose a two-phase heuristic approach based on simulated annealing, tp_SA, to solve the large-size LRPSPD and two initialization heuristics to generate an initial solution for the tp_SA. We then empirically evaluate the strengths of the proposed formulations with respect to their ability to find optimal solutions or strong lower bounds, and investigate the performance of the proposed heuristic approach. Computational results show that the flow-based formulation performs better than the node-based formulation in terms of the solution quality and the computation time on small-size problems. However, the node-based formulation can yield competitive lower bounds in a reasonable amount of time on medium-size problems. Meantime, the proposed heuristic approach is computationally efficient in finding good quality solutions for the LRPSPD.

A hybrid genetic algorithm for the discrete time-cost trade-off problem

Abstract: In this paper we present a hybrid strategy developed using genetic algorithms (GAs), simulated annealing (SA), and quantum simulated annealing techniques (QSA) for the discrete time-cost trade-off problem (DTCTP). In the hybrid algorithm (HA), SA is used to improve hill-climbing ability of GA. In addition to SA, the hybrid strategy includes QSA to achieve enhanced local search capability. The HA and a sole GA have been coded in Visual C++ on a personal computer. Ten benchmark test problems with a range of 18 to 630 activities are used to evaluate performance of the HA. The benchmark problems are solved to optimality using mixed integer programming technique. The results of the performance analysis indicate that the hybrid strategy improves convergence of GA significantly and HA provides a powerful alternative for the DTCTP.

Creating Realistic Synthetic Populations at Varying Spatial Scales: A Comparative Critique of Population Synthesis Techniques

Abstract: There are several established methodologies for generating synthetic populations. These include deterministic reweighting, conditional probability (Monte Carlo simulation) and simulated annealing. However, each of these approaches is limited by, for example, the level of geography to which it can be applied, or number of characteristics of the real population that can be replicated. The research examines and critiques the performance of each of these methods over varying spatial scales. Results show that the most consistent and accurate populations generated over all the spatial scales are produced from the simulated annealing algorithm. The relative merits and limitations of each method are evaluated in the discussion.

Markowitz-based portfolio selection with cardinality constraints using improved particle swarm optimization

Abstract: This work presents particle swarm optimization (PSO), a collaborative population-based meta-heuristic algorithm for solving the Cardinality Constraints Markowitz Portfolio Optimization problem (CCMPO problem). To our knowledge, an efficient algorithmic solution for this nonlinear mixed quadratic programming problem has not been proposed until now. Using heuristic algorithms in this case is imperative. To solve the CCMPO problem, the proposed improved PSO increases exploration in the initial search steps and improves convergence speed in the final search steps. Numerical solutions are obtained for five analyses of weekly price data for the following indices for the period March, 1992 to September, 1997: Hang Seng 31 in Hong Kong, DAX 100 in Germany, FTSE 100 in UK, S&P 100 in USA and Nikkei 225 in Japan. The test results indicate that the proposed PSO is much more robust and effective than existing PSO algorithms, especially for low-risk investment portfolios. In most cases, the PSO outperformed genetic algorithm (GA), simulated annealing (SA), and tabu search (TS).

Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm

Abstract: The accuracy of annual electric load forecasting plays an important role in the economic and social benefits of electric power systems. The least squares support vector machine (LSSVM) has been proven to offer strong potential in forecasting issues, particularly by employing an appropriate meta-heuristic algorithm to determine the values of its two parameters. However, these meta-heuristic algorithms have the drawbacks of being hard to understand and reaching the global optimal solution slowly. As a novel meta-heuristic and evolutionary algorithm, the fruit fly optimization algorithm (FOA) has the advantages of being easy to understand and fast convergence to the global optimal solution. Therefore, to improve the forecasting performance, this paper proposes a LSSVM-based annual electric load forecasting model that uses FOA to automatically determine the appropriate values of the two parameters for the LSSVM model. By taking the annual electricity consumption of China as an instance, the computational result shows that the LSSVM combined with FOA (LSSVM-FOA) outperforms other alternative methods, namely single LSSVM, LSSVM combined with coupled simulated annealing algorithm (LSSVM-CSA), generalized regression neural network (GRNN) and regression model.

Improved PSO-based Task Scheduling Algorithm in Cloud Computing

Abstract: Job scheduling system problem is a core and challenging issue in cloud computing. How to use cloud computing resources efficiently and gain the maximum profits with job scheduling system is one of the cloud computing service providers’ ultimate goals. For characteristics of particle swarm optimization algorithm in solving the large-scale combination optimization problem easy to fall into the search speed slowly and partially the most superior, the global fast convergence of simulated annealing algorithm is utilized to combine particle swarm optimization algorithm in each iteration, which enhances the convergence rate and improves the efficiency. This paper proposed the improve particle swarm optimization algorithm in resources scheduling strategy of the cloud computing. Through experiments, the results show that this method can reduce the task average running time, and raises the rate availability of resources.

A multi-type ant colony optimization MACO method for optimal land use allocation in large areas

Abstract: Optimizing land use allocation is a challenging task, as it involves multiple stakeholders with conflicting objectives. In addition, the solution space of the optimization grows exponentially as the size of the region and the resolution increase. This article presents a new ant colony optimization algorithm by incorporating multiple types of ants for solving complex multiple land use allocation problems. A spatial exchange mechanism is used to deal with competition between different types of land use allocation. This multi-type ant colony optimization optimal multiple land allocation MACO-MLA model was successfully applied to a case study in Panyu, Guangdong, China, a large region with an area of 1,454,285 cells. The proposed model took only about 25 minutes to find near-optimal solution in terms of overall suitability, compactness, and cost. Comparison indicates that MACO-MLA can yield better performances than the simulated annealing SA and the genetic algorithm GA methods. It is found that MACO-MLA has an improvement of the total utility value over SA and GA methods by 4.5% and 1.3%, respectively. The computation time of this proposed model amounts to only 2.6% and 12.3%, respectively, of that of the SA and GA methods. The experiments have demonstrated that the proposed model was an efficient and effective optimization technique for generating optimal land use patterns.

A Hybrid Estimation of Distribution Algorithm with Decomposition for Solving the Multiobjective Multiple Traveling Salesman Problem

Abstract: Evolutionary multiobjective optimization with decomposition, in which the algorithm is not required to differentiate between the dominated and nondominated solutions, is one of the promising approaches in dealing with multiple conflicting objectives. In this paper, the estimation of distribution algorithm (EDA) is integrated into the decomposition framework. The search behavior of the algorithm is further enhanced by hybridizing local search metaheuristic approaches with the decomposition EDA. Three local search techniques, including hill climbing, simulated annealing, and evolutionary gradient search, are considered. A novel multiobjective formulation of the multiple traveling salesman problem is proposed. The hybrid algorithms are used to solve the formulated problem with different number of objective functions, salesmen, and problem sizes. The effectiveness and efficiency of the algorithms are tested and benchmarked against several state-of-the-art multiobjective evolutionary paradigms.

An approach using particle swarm optimization and bottleneck heuristic to solve hybrid flow shop scheduling problem

Abstract: Hybrid flow shops (HFS) are common manufacturing environments in many industries, such as the glass, steel, paper and textile industries. In this paper, we present a particle swarm optimization (PSO) algorithm for the HFS scheduling problem with minimum makespan objective. The main contribution of this paper is to develop a new approach hybridizing PSO with bottleneck heuristic to fully exploit the bottleneck stage, and with simulated annealing to help escape from local optima. The proposed PSO algorithm is tested on the benchmark problems provided by Carlier and Neron. Experimental results show that the proposed algorithm outperforms all the compared algorithms in solving the HFS problem.

Selection of wire electrical discharge machining process parameters using non-traditional optimization algorithms

Abstract: Selection of the optimal values of different process parameters, such as pulse duration, pulse frequency, duty factor, peak current, dielectric flow rate, wire speed, wire tension, effective wire offset of wire electrical discharge machining (WEDM) process is of utmost importance for enhanced process performance The major performance measures of WEDM process generally include material removal rate, cutting width (kerf), surface roughness and dimensional shift Although different mathematical techniques, like artificial neural network, gray relational analysis, simulated annealing, desirability function, Pareto optimality approach, etc have already been applied for searching out the optimal parametric combinations of WEDM processes, but in most of the cases, sub-optimal or near-optimal solutions have been arrived at In this paper, an attempt is made to apply six most popular population-based non-traditional optimization algorithms, ie genetic algorithm, particle swarm optimization, sheep flock algorithm, ant colony optimization, artificial bee colony and biogeography-based optimization for single and multi-objective optimization of two WEDM processes The performance of these algorithms is also compared and it is observed that biogeography-based optimization algorithm outperforms the others

A Simulated Annealing Hyper-heuristic Methodology for Flexible Decision Support

Abstract: Most of the current search techniques represent approaches that are largely adapted for specific search problems. There are many real-world scenarios where the development of such bespoke systems is entirely appropriate. However, there are other situations where it would be beneficial to have methodologies which are generally applicable to more problems. One of our motivating goals for investigating hyper-heuristic methodologies is to provide a more general search framework that can be easily and automatically employed on a broader range of problems than is currently possible. In this paper, we investigate a simulated annealing hyper-heuristic methodology which operates on a search space of heuristics and which employs a stochastic heuristic selection strategy and a short-term memory. The generality and performance of the proposed algorithm is demonstrated over a large number of benchmark datasets drawn from two very different and difficult problems, namely; course timetabling and bin packing. The contribution of this paper is to present a method which can be readily (and automatically) applied to different problems whilst still being able to produce results on benchmark problems which are competitive with bespoke human designed tailor made algorithms for those problems.

Particle Swarm Optimization Inspired Probability Algorithm for Optimal Camera Network Placement

Abstract: In this paper, a novel method based on binary Particle Swarm Optimization (BPSO) inspired probability is proposed to solve the camera network placement problem. Ensuring accurate visual coverage of the monitoring space with a minimum number of cameras is sought. The visual coverage is defined by realistic and consistent assumptions taking into account camera characteristics. In total, nine evolutionary-like algorithms based on BPSO, Simulated Annealing (SA), Tabu Search (TS) and genetic techniques are adapted to solve this visual coverage based camera network placement problem. All these techniques are introduced in a new and effective framework dealing with constrained optimizations. The performance of BPSO inspired probability technique is compared with the performances of the stochastic variants (e.g., genetic algorithms-based or immune systems-based) of optimization based particle swarm algorithms. Simulation results for 2-D and 3-D scenarios show the efficiency of the proposed technique. Indeed, for a large-scale dimension case, BPSO inspired probability gives better results than the ones obtained by adapting all other variants of BPSO, SA, TS, and genetic techniques.