Showing papers on "Simulated annealing published in 2010"

Benchmark Functions for the CEC'2010 Special Session and Competition on Large-Scale Global Optimization

Abstract: In the past decades, different kinds of metaheuristic optimization algorithms [1, 2] have been developed; Simulated Annealing (SA) [3, 4], Evolutionary Algorithms (EAs) [5–7], Differential Evolution (DE) [8, 9], Particle Swarm Optimization (PSO) [10, 11], Ant Colony Optimization (ACO) [12, 13], and Estimation of Distribution Algorithms (EDAs) [14, 15] are just a few of them. These algorithms have shown excellent search abilities but often lose their efficacy when applied to large and complex problems, e.g., problem instances with high dimensions, such as those with more than one hundred decision variables. Many optimization methods suffer from the “curse of dimensionality” [16, 17], which implies that their performance deteriorates quickly as the dimensionality of the search space increases. The reasons for this phenomenon appear to be two-fold. First, the solution space of a problem often increases exponentially with the problem dimension [16, 17] and more efficient search strategies are required to explore all promising regions within a given time budget. Second, also the characteristics of a problem may change with the scale. Rosenbrock’s function [18] (see also Section 2.6), for instance, is unimodal for two dimension but becomes multimodal for higher ones [19]. Because of such a worsening of the features of an optimization problem resulting from an increase in scale, a previously successful search strategy may no longer be capable of finding the optimal solution. Historically, scaling EAs to large-scale problems has attracted much interest, including both theoretical and practical studies. The earliest practical approach might be parallelizing an existing EA [20–22]. Later, cooperative coevolution appeared as another promising method [23, 24]. However, existing works on this topic are often limited to test problems used in individual studies and a systematic evaluation platform is still not available in literature for comparing the scalability of different EAs. This report aims to contribute to solving this problem. In particular, we provide a suite of benchmark functions for large-scale numerical optimization. Although the difficulty of a problem generally increases with its dimensionality, it is natural that some highdimensional problems are easier than others. For example, if the decision variables involved in a problem are independent of each other, the problem can be easily solved by decomposing it into a number of sub-problems, each of which involving only one decision variable while treating all others as constants. This way, even a line search or greedy method can solve the problem efficiently [25]. This class of problem is known as separable problems, and has been formally defined in [26] as follows:

An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis

Abstract: Clustering is a popular data analysis and data mining technique. A popular technique for clustering is based on k-means such that the data is partitioned into K clusters. However, the k-means algorithm highly depends on the initial state and converges to local optimum solution. This paper presents a new hybrid evolutionary algorithm to solve nonlinear partitional clustering problem. The proposed hybrid evolutionary algorithm is the combination of FAPSO (fuzzy adaptive particle swarm optimization), ACO (ant colony optimization) and k-means algorithms, called FAPSO-ACO-K, which can find better cluster partition. The performance of the proposed algorithm is evaluated through several benchmark data sets. The simulation results show that the performance of the proposed algorithm is better than other algorithms such as PSO, ACO, simulated annealing (SA), combination of PSO and SA (PSO-SA), combination of ACO and SA (ACO-SA), combination of PSO and ACO (PSO-ACO), genetic algorithm (GA), Tabu search (TS), honey bee mating optimization (HBMO) and k-means for partitional clustering problem.

Coupled Simulated Annealing

Abstract: We present a new class of methods for the global optimization of continuous variables based on simulated annealing (SA). The coupled SA (CSA) class is characterized by a set of parallel SA processes coupled by their acceptance probabilities. The coupling is performed by a term in the acceptance probability function, which is a function of the energies of the current states of all SA processes. A particular CSA instance method is distinguished by the form of its coupling term and acceptance probability. In this paper, we present three CSA instance methods and compare them with the uncoupled case, i.e., multistart SA. The primary objective of the coupling in CSA is to create cooperative behavior via information exchange. This aim helps in the decision of whether uphill moves will be accepted. In addition, coupling can provide information that can be used online to steer the overall optimization process toward the global optimum. We present an example where we use the acceptance temperature to control the variance of the acceptance probabilities with a simple control scheme. This approach leads to much better optimization efficiency, because it reduces the sensitivity of the algorithm to initialization parameters while guiding the optimization process to quasioptimal runs. We present the results of extensive experiments and show that the addition of the coupling and the variance control leads to considerable improvements with respect to the uncoupled case and a more recently proposed distributed version of SA.

Optimization and Filtering for Human Motion Capture

Abstract: Local optimization and filtering have been widely applied to model-based 3D human motion capture. Global stochastic optimization has recently been proposed as promising alternative solution for tracking and initialization. In order to benefit from optimization and filtering, we introduce a multi-layer framework that combines stochastic optimization, filtering, and local optimization. While the first layer relies on interacting simulated annealing and some weak prior information on physical constraints, the second layer refines the estimates by filtering and local optimization such that the accuracy is increased and ambiguities are resolved over time without imposing restrictions on the dynamics. In our experimental evaluation, we demonstrate the significant improvements of the multi-layer framework and provide quantitative 3D pose tracking results for the complete HumanEva-II dataset. The paper further comprises a comparison of global stochastic optimization with particle filtering, annealed particle filtering, and local optimization.

Software News and Update Reconstruction of Atomistic Details from Coarse-Grained Structures

Abstract: We present an algorithm to reconstruct atomistic structures from their corresponding coarse-grained (CG) representations and its implementation into the freely available molecular dynamics (MD) program package GROMACS. The central part of the algorithm is a simulated annealing MD simulation in which the CG and atomistic structures are coupled via restraints. A number of examples demonstrate the application of the reconstruction procedure to obtain low-energy atomistic structural ensembles from their CO counterparts. We reconstructed individual molecules in vacuo (NCQ tripeptide, dipalmitoylphosphatidylcholine, and cholesterol), bulk water, and a WALP transmembrane peptide embedded in a solvated lipid bilayer. The first examples serve to optimize the parameters for the reconstruction procedure, whereas the latter examples illustrate the applicability to condensed-phase biomolecular systems. (C) 2010 Wiley Periodicals, Inc. J Comput Chem 31 : 1333-1343, 2010

Reverse logistics network design using simulated annealing

Abstract: Reverse logistics is becoming more important in overall industry area because of the environmental and business factors. Planning and implementing a suitable reverse logistics network could bring more profit, customer satisfaction, and a nice social picture for companies. But, most of logistics networks are not equipped to handle the return products in reverse channels. This paper proposes a mixed integer linear programming model to minimize the transportation and fixed opening costs in a multistage reverse logistics network. Since such network design problems belong to the class of NP-hard problems, we apply a simulated annealing (SA) algorithm with special neighborhood search mechanisms to find the near optimal solution. We also compare the associated numerical results through exact solutions in a set of problems to present the high-quality performance of the applied SA algorithm.

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.

Ant Colony Heuristic for Mapping and Scheduling Tasks and Communications on Heterogeneous Embedded Systems

Abstract: To exploit the power of modern heterogeneous multiprocessor embedded platforms on partitioned applications, the designer usually needs to efficiently map and schedule all the tasks and the communications of the application, respecting the constraints imposed by the target architecture. Since the problem is heavily constrained, common methods used to explore such design space usually fail, obtaining low-quality solutions. In this paper, we propose an ant colony optimization (ACO) heuristic that, given a model of the target architecture and the application, efficiently executes both scheduling and mapping to optimize the application performance. We compare our approach with several other heuristics, including simulated annealing, tabu search, and genetic algorithms, on the performance to reach the optimum value and on the potential to explore the design space. We show that our approach obtains better results than other heuristics by at least 16% on average, despite an overhead in execution time. Finally, we validate the approach by scheduling and mapping a JPEG encoder on a realistic target architecture.

Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms

Abstract: The effective optimization of machining process parameters affects dramatically the cost and production time of machined components as well as the quality of the final products. This paper presents optimization aspects of a multi-pass milling operation. The objective considered is minimization of production time (i.e. maximization of production rate) subjected to various constraints of arbor strength, arbor deflection, and cutting power. Various cutting strategies are considered to determine the optimal process parameters like the number of passes, depth of cut for each pass, cutting speed, and feed. The upper and lower bounds of the process parameters are also considered in the study. The optimization is carried out using three non-traditional optimization algorithms namely, artificial bee colony (ABC), particle swarm optimization (PSO), and simulated annealing (SA). An application example is presented and solved to illustrate the effectiveness of the presented algorithms. The results of the presented algorithms are compared with the previously published results obtained by using other optimization techniques.

An improved ant colony optimization algorithm for solving a complex combinatorial optimization problem

Abstract: This paper presents an improved ant colony optimization algorithm (IACO) for solving mobile agent routing problem. The ants cooperate using an indirect form of communication mediated by pheromone trails of scent and find the best solution to their tasks guided by both information (exploitation) which has been acquired and search (exploration) of the new route. Therefore the premature convergence probability of the system is lower. The IACO can solve successfully the mobile agent routing problem, and this method has some excellent properties of robustness, self-adaptation, parallelism, and positive feedback process owing to introducing the genetic operator into this algorithm and modifying the global updating rules. The experimental results have demonstrated that IACO has much higher convergence speed than that of genetic algorithm (GA), simulated annealing (SA), and basic ant colony algorithm, and can jump over the region of the local minimum, and escape from the trap of a local minimum successfully and achieve the best solutions. Therefore the quality of the solution is improved, and the whole system robustness is enhanced. The algorithm has been successfully integrated into our simulated humanoid robot system which won the fourth place of RoboCup2008 World Competition. The results of the proposed algorithm are found to be satisfactory.

Genetic simulated annealing algorithm for task scheduling based on cloud computing environment

Abstract: Scheduling is a very important part of the cloud computing system. This paper introduces an optimized algorithm for task scheduling based on genetic simulated annealing algorithm in cloud computing and its implementation. Algorithm considers the QOS requirements of different type tasks, the QOS parameters are dealt with dimensionless. The algorithm efficiently completes tasks scheduling in the cloud computing environment computing.

Application of Genetic Algorithms to Solve the Multidepot Vehicle Routing Problem

Abstract: This paper deals with the optimization of vehicle routing problem in which multiple depots, multiple customers, and multiple products are considered. Since the total traveling time is not always restrictive as a time window constraint, the objective regarded in this paper comprises not only the cost due to the total traveling distance, but also the cost due to the total traveling time. We propose to use a stochastic search technique called fuzzy logic guided genetic algorithms (FLGA) to solve the problem. The role of fuzzy logic is to dynamically adjust the crossover rate and mutation rate after ten consecutive generations. In order to demonstrate the effectiveness of FLGA, a number of benchmark problems are used to examine its search performance. Also, several search methods, branch and bound, standard GA (i.e., without the guide of fuzzy logic), simulated annealing, and tabu search, are adopted to compare with FLGA in randomly generated data sets. Simulation results show that FLGA outperforms other search methods in all of three various scenarios.

Electromagnetism-like mechanism and simulated annealing algorithms for flowshop scheduling problems minimizing the total weighted tardiness and makespan

Abstract: This paper presents an efficient meta-heuristic algorithm based on electromagnetism-like mechanism (EM), in which has been successfully implemented in a few combinatorial problems. We propose the EM for scheduling the flow shop problem that minimizes the makespan and total weighted tardiness and considers transportation times between machines and stage skipping (i.e., some jobs may not need to be processed on all the machines). To show the efficiency of this proposed algorithm, we also apply simulated annealing (SA) and some other well-recognized constructive heuristics, such as SPT, NEH, (g/2, g/2) Johnson' rule, EWDD, SLACK, and NEH_EWDD for the given problems. To evaluate the performance and robustness of our proposed EM, we experiment a number of test problems. Our computational results show that our proposed EM in almost all cases outperforms SA and other foregoing heuristics applied to this paper.

Scheduling Trucks In Cross-Docking Systems: A Robust Meta-Heuristics Approach

Abstract: In a cross-docking system, trucks must be scheduled to minimize the total flow time of the system. This problem is NP-hard, and this study proposes two hybrid meta-heuristics—hybrid simulated annealing and hybrid variable neighborhood search—to solve it by achieving the best sequence of truck pairs. The Taguchi method serves to reveal the best robustness of these algorithms. To demonstrate the effectiveness of the proposed methods, especially for large-sized problems, this study solves various test problems, and the computational results clearly reveal that the proposed methods outperform previous approaches.

Robust optimization with simulated annealing

Abstract: Complex systems can be optimized to improve the performance with respect to desired functionalities. An optimized solution, however, can become suboptimal or even infeasible, when errors in implementation or input data are encountered. We report on a robust simulated annealing algorithm that does not require any knowledge of the problems structure. This is necessary in many engineering applications where solutions are often not explicitly known and have to be obtained by numerical simulations. While this nonconvex and global optimization method improves the performance as well as the robustness, it also warrants for a global optimum which is robust against data and implementation uncertainties. We demonstrate it on a polynomial optimization problem and on a high-dimensional and complex nanophotonic engineering problem and show significant improvements in efficiency as well as in actual optimality.

A hybrid immune simulated annealing algorithm for the job shop scheduling problem

Abstract: A hybrid simulated annealing algorithm based on a novel immune mechanism is proposed for the job shop scheduling problem with the objective of minimizing total weighted tardiness. The proposed immune procedure is built on the following fundamental idea: the bottleneck jobs existing in each scheduling instance generally constitute the key factors in the attempt to improve the quality of final schedules, and thus, the sequencing of these jobs needs more intensive optimization. To quantitatively describe the bottleneck job distribution, we design a fuzzy inference system for evaluating the bottleneck level (i.e. the criticality) of each job. By combining the immune procedure with a simulated annealing algorithm, we design a hybrid optimization algorithm which is subsequently tested on a number of job shop instances. Computational results for different-sized instances show that the proposed hybrid algorithm performs effectively and converges fast to satisfactory solutions.

On the impact of the migration topology on the Island Model

Abstract: Parallel Global Optimization Algorithms (PGOA) provide an efficient way of dealing with hard optimization problems. One method of parallelization of GOAs that is frequently applied and commonly found in the contemporary literature is the so-called Island Model (IM). In this paper, we analyze the impact of the migration topology on the performance of a PGOA which uses the Island Model. In particular we consider parallel Differential Evolution and Simulated Annealing with Adaptive Neighborhood and draw first conclusions that emerge from the conducted experiments.