In the first model, simulated annealing sa algorithm is embedded in woa algorithm, while it is used to improve the best solution found after each iteration of woa algorithm in the second model. Robust optimization is an umbrella term for mathematical modelling method ologies focused on. In order to solve the uncertain scheduling problem with special criterion, a hybrid heuristic algorithm, which integrates the genetic algorithm and the simulated annealing algorithm, is developed in this paper. Function optimization using robust simulated annealing.
To tackle these rapidly changing conditions, a layout must be efficient and effective. Global optimization of statistical functions with simulated annealing william l. Pdf improving the performance of simulated annealing in large. The simulated annealing technique is an example of a global optimization routine. Simulated annealing is a global optimization procedure kirkpatrick et al. Robust optimization with simulated annealing article pdf available in journal of global optimization 482.
It contains a set of multiobjective optimization algorithms such as evolutionary algorithms including spea2 and nsga2, differential evolution, particle swarm optimization, and simulated annealing. The problem consists of finding a spanning tree that minimizes socalled robust deviation, i. Improved modified simulated annealing algorithm 4793 table 2. Sorry, we are unable to provide the full text but you may find it at the following locations. The heuristic algorithm is computationally tractable. Robust optimization with simulated annealing springerlink. A robust simulated annealing based examination timetabling. This book is devoted to robust optimization a speci.
We report on a robust simulated annealing algorithm that does not require any knowledge of the problems structure. An algorithm for exploiting modeling error statistics to. Global search methods require a potentially much larger number of spice evaluations for the global exploration to converge 12, and typically have exponential complexity. In 1953 metropolis created an algorithm to simulate the annealing process. Index termsgeneration scheduling, local search, optimization techniques, simulated. Global optimization and simulated annealing citation for published version apa. Keywords structural optimization discrete optimization. However, global optimum values cannot always be reached by simulated annealing without a logarithmic cooling schedule. Robust optimization is a young and emerging field of research having received. Most approaches, however, assume that the input parameters are precisely known and that the implementation does not suffer any errors. Simulated annealing for convex optimization mathematics of operations research 000, pp.
It is useful in finding the global minimum in the presence of several local minima agostini et al. Minimization using simulated annealing algorithm open live script this example shows how to create and minimize an objective function using the simulated annealing algorithm simulannealbnd function in global optimization toolbox. Specifically, it is a metaheuristic to approximate global optimization in a large search space. Electrical engineering, national university of singapore, 2009 submitted to the school of engineering archives in partial fulfillment of the requirements for the degree of. Listbased simulated annealing algorithm for traveling. Local optimization to understand simulated annealing, one must first understand local optimization. Annealing refers to heating a solid and then cooling it slowly. Application of robust and inverse optimization in transportation s nmassachusetts instite of technology by sepo 2 2010. The simulated annealing algorithm is an optimization method which mimics the slow cooling of metals, which is characterized by a progressive reduction in the atomic movements that reduce the density of lattice defects until a lowestenergy state is reached 143. Journal of optimization theory and applications 104. Simulated annealingbased embedded metaheuristic approach. Simulated annealing tutorial apmonitor optimization suite. The term annealing refers to the thermal process for obtaining low energy states of a solid in a heat bath.
Complex systems can be optimized to improve the performance with respect to desired functionalities. There are many r packages for solving optimization problems see cran task view. A robust hybrid restarted simulated annealing particle swarm optimization technique. Application of robust and inverse optimization in transportation s nmassachusetts instite of technology by sepo 2 2010 thai dung nguyen libraries b. Both vanderbilt and louie 1984 and bohachevsky et al. Isbn 97895330743, pdf isbn 9789535159315, published 20100818. Pdf solving a robust capacitated arc routing problem. In this paper, two hybridization models are used to design different feature selection techniques based on whale optimization algorithm woa. A genetic and simulated annealing combined algorithm is presented and applied to optimize broadband matching networks for antennas. Application of a simulated annealing optimization to a.
Effectiveness and efficiency of the presented combined algorithm are demonstrated by optimization of a wideband matching network for a vhfuhf disconebased. Genetic simulatedannealing algorithm for robust job shop. Optimization using simulated annealing the statistician 44. Optimization by simulated annealing article pdf available in science 2204598. Atoms then assume a nearly globally minimum energy state. Consequently, it does not require the computation of derivatives and so can be used with arrival times of multiple phases, azimuth and slowness information and any type of velocity model, including laterally heterogeneous 3d models, without modification to the. A robust simulated annealing based examination timetabling system a robust simulated annealing based examination timetabling system thompson, jonathan m dowsland, kathryn a. Robust optimization a comprehensive survey sciencedirect. Simulated annealing based robust algorithm for routing.
Solving a robust capacitated arc routing prob lem using a hybrid simulated annealing algorithm journal of industrial engineering and management studie s jiems, vol. Robust optimization model of bus transit network design. Hybrid whale optimization algorithm with simulated annealing. Robust model of discrete competitive facility location. Available formats pdf please select a format to send. Optimal placement of activepassive members in truss. The promise of simulated annealing is demonstrated on the four econometric problems.
This example is using netlogo flocking model wilensky, 1998 to demonstrate parameter fitting with simulated annealing. For the robust network design problem, we propose a decomposition scheme. Therefore, the optimization process is not only driven to uniformity in the audience plane but it is also driven to maximize the number of rays being utilized. This article applies the simulated annealing sa algorithm to the portfolio optimization problem. The benchmarks that are included comprise zdt, dtlz, wfg, and the knapsack problem. For problems where finding an approximate global optimum is more important than. Simulated annealing sa is a probabilistic technique for approximating the global optimum of a given function.
This paper describes the development of a robust algorithm for multiobjective optimization, known as robust multiobjective simulated annealing rmosa. Citeseerx document details isaac councill, lee giles, pradeep teregowda. Global optimization of statistical functions with simulated. A combinatorial opti mization problem can be specified by identifying a set of solutions together with a cost function that assigns a numerical value to each solution. In this study, we propose a new stochastic optimization algorithm, i. Not only can simulated annealing find the global optimum, it is also less likely to fail on difficult functions because it is a very robust algorithm. To deal with the expensive computation required by large networks, we also propose a heuristic robust simulated annealing approach. Simulated annealing is an effective and general form of energy optimization. Nonconvex feasibility robust optimization via scenario.
In local search methods performance is tied to the initial points of the algorithm. Keywords robust optimization simulated annealing global optimization nonconvex optimization 1 introduction optimization has had a distinguished history in engineering and industrial design. A genetic and simulated annealing combined algorithm for. This methodology uses taguchis view of the uncertain world, but replaces his statistical techniques by response surface methodology rsm. Simulated annealing sa algorithm, which was r st independently presented as a search algorithm for combinatorial optimization problems in, is a popular iterative metaheuristic algorithm widely used to address discrete and continuous optimization problems. It is often used when the search space is discrete e. Optimization by simulated annealing martin krzywinski. To solve the problem, a hybrid metaheuristic algorithm is developed based on a simulated annealing algorithm and a heuristic algorithm. Simulated annealing sa is a generic probabilistic and metaheuristic search algorithm which can be used to find acceptable solutions to optimization problems characterized by. One is an exact method, and it is based on directly solving an integer model equivalent to the original robust model. In this and two companion papers, we report on an extended empirical study of the simulated annealing approach to combinatorial optimization proposed by s.
Goffe university of north carolina at wilmington, wilmington, nc 28403. Solving a robust capacitated arc routing problem using a. The primary goal of this preface is to provide the reader with a. A robust optimization model is formulated for the proposed problem, which aims to minimize the sum of the expected value of the operator cost and its variability multiplied by a weighting value. Simulated annealing for earthquake location geophysical. Simulated annealing sa is a generic probabilistic and metaheuristic search algorithm which can be used to find acceptable solutions to optimization problems characterized by a large search space with multiple optima. Algorithm engineering in robust optimization arxiv. To simplify parameters setting, we present a listbased simulated annealing lbsa algorithm to solve traveling salesman problem tsp. Pdf a robust hybrid restarted simulated annealing particle. Specifically, it is a metaheuristic to approximate global optimization in a large. This article, however, develops a robust methodology for uncertain environments. Siam journal on control and optimization siam society for.
Robust design and optimization has even deeper roots in engineering. Hybrid whale optimization algorithm with simulated. We test a new optimization algorithm, simulated annealing, on four econometric problems and compare it to three common conventional algorithms. We report on a robust simulated annealing algorithm that does not require any. As a result, advantages of both the genetic algorithm ga and simulated annealing sa are taken. Goffe university of north carolina at wilmington, wilmington, nc 28403 gary d. While the methods of stochastic linear programming 1 may be regarded as a first approach to deal with uncertainties treating robustness as a side effect only, the notion of robust optimization gained focus in or after the publication of for an introductory paper, see also. The other is a heuristic method, which is a combination of the simulated annealing framework and a sortingbased algorithm. Stochastic optimization is an umbrella set of methods that includes simulated annealing and numerous other approaches. Simulated annealing algorithm an overview sciencedirect. For problems where finding an approximate global optimum is more.
Two methods are proposed to solve the presented robust optimization model. Ferrier southern methodist university, dallas, tx 75275 john rogers north texas state, denton tx 76203 may, 1993. A heuristic solution approach, based on k shortest path algorithm, simulated annealing algorithm, monte carlo simulation, and probittype discrete. Research article listbased simulated annealing algorithm for. Multipletry simulated annealing algorithm for global. This paper addresses the robust spanning tree problem with interval data, i. Improved modified simulated annealing algorithm for global. Simulated annealing algorithm for the robust spanning tree. Optimization of simulated systems is tackled by many methods, but most methods assume known environments. Simulated annealing is a wellstudied local search metaheuristic used to address discrete and, to a lesser extent, continuous optimization problems. Generalized simulated annealing for global optimization.
In a similar way, at each virtual annealing temperature, the. A major risk concerning the calibration of physically based erosion models has been partly attributable to the lack of robust optimization tools. Application of robust and inverse optimization in transportation by thai dung nguyen. Simulated annealing is a widely used algorithm for the computation of global optimization problems in computational chemistry and industrial engineering.
Road congestion is an optimization problem because of the difficulty it poses and its high unpredictable nature. Moreover, due to environmental challenges, use of scarce resources like electrical energy consumption eec is a major issue. Simulated annealing sa algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. Simulated annealing, theory with applications intechopen. Simulated annealing sa and genetic algorithm ga have been applied to. Development of a robust multiobjective simulated annealing. Simulated annealing an overview sciencedirect topics.
Minimization using simulated annealing algorithm matlab. This results in a robust optimization algorithm that conducts an efficient search of the parameter space. Due to the inherent statistical nature of simulated annealing, in principle local minima can be hopped over more easily than for gradient methods. A robust optimization criterion is proposed to balance the average performance and the robustness. That study investigated how best to adapt simulated annealing to particular problems and compared its performance to that of more traditional algorithms. An optimized solution, however, can become suboptimal or even infeasible, when errors in implementation or input data are encountered. Center for connected learning and computerbased modeling, northwestern university, evanston, il. Probabilistic optimization for conceptual rainfallrunoff. In this research, simulated annealing algorithm is applied to solve function optimization. Simulated annealing copies a phenomenon in naturethe annealing of solidsto optimize a complex system.
Application of robust and inverse optimization in tr. Specifically, it is a metaheuristic to approximate global optimization in a large search space for an optimization problem. This paper presents a new feasibility robust optimization approach involving uncertain parameters defined on continuous domains. Io notes for problem set 7 zto read data, use stdio. Generalized simulated annealing classical simulated annealing csa was proposed bykirkpatrick et al. A robust and efficient global optimization algorithm for parameter estimation of biological networks.
Flexible global optimization with simulatedannealing. The proposed approach is based on an integration of two techniques. This paper presents the application and use of statistical analysis method taguchi design method for optimizing the parameters are tuned for the optimum output. The key feature of simulated annealing is that it provides a mechanism to escape local optima by allowing hillclimbing moves i.
1337 35 585 62 275 975 498 519 1486 690 1368 495 548 121 480 166 1578 96 905 1360 168 481 166 572 178 1397 1056