Showing papers on "Simulated annealing published in 1996"

Ant system: optimization by a colony of cooperating agents

Abstract: An analogy with the way ant colonies function has suggested the definition of a new computational paradigm, which we call ant system (AS). We propose it as a viable new approach to stochastic combinatorial optimization. The main characteristics of this model are positive feedback, distributed computation, and the use of a constructive greedy heuristic. Positive feedback accounts for rapid discovery of good solutions, distributed computation avoids premature convergence, and the greedy heuristic helps find acceptable solutions in the early stages of the search process. We apply the proposed methodology to the classical traveling salesman problem (TSP), and report simulation results. We also discuss parameter selection and the early setups of the model, and compare it with tabu search and simulated annealing using TSP. To demonstrate the robustness of the approach, we show how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling. Finally we discuss the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.

VLSI module placement based on rectangle-packing by the sequence-pair

Abstract: The earliest and the most critical stage in VLSI layout design is the placement. The background is the rectangle packing problem: given a set of rectangular modules of arbitrary sizes, place them without overlap on a plane within a rectangle of minimum area. Since the variety of the packing is uncountably infinite, the key issue for successful optimization is the introduction of a finite solution space which includes an optimal solution. This paper proposes such a solution space where each packing is represented by a pair of module name sequences, called a sequence-pair. Searching this space by simulated annealing, hundreds of modules have been packed efficiently as demonstrated. For applications to VLSI layout, we attack the biggest MCNC benchmark ami49 with a conventional wiring area estimation method, and obtain a highly promising placement.

Metaheuristics: A bibliography

Abstract: Metaheuristics are the most exciting development in approximate optimization techniques of the last two decades. They have had widespread successes in attacking a variety of difficult combinatorial optimization problems that arise in many practical areas. This bibliography provides a classification of a comprehensive list of 1380 references on the theory and application of metaheuristics. Metaheuristics include but are not limited to constraint logic programming; greedy random adaptive search procedures; natural evolutionary computation; neural networks; non-monotonic search strategies; space-search methods; simulated annealing; tabu search; threshold algorithms and their hybrids. References are presented in alphabetical order under a number of subheadings.

Evolutionary programming based economic dispatch for units with non-smooth fuel cost functions

Abstract: This paper develops an efficient, general economic dispatch (ED) algorithm for generating units with nonsmooth fuel cost functions. Based on the evolutionary programming (EP) technique, the new algorithm is capable of determining the global or near global optimal dispatch solutions in the cases where the classical Lagrangian based algorithms cease to be applicable. Effectiveness of the new algorithm is demonstrated on two example power systems and compared to that of the dynamic programming, simulated annealing, and genetic algorithms. Practical application of the developed algorithm is additionally verified on the Taiwan power (Taipower) system. Numerical results show that the proposed EP based ED algorithm can provide accurate dispatch solutions within reasonable time for any type of fuel cost functions.

Drawing graphs nicely using simulated annealing

Abstract: The paradigm of simulated annealing is applied to the problem of drawing graphs “nicely.” Our algorithm deals with general undirected graphs with straight-line edges, and employs several simple criteria for the aesthetic quality of the result. The algorithm is flexible, in that the relative weights of the criteria can be changed. For graphs of modest size it produces good results, competitive with those produced by other methods, notably, the “spring method” and its variants.

Adaptive simulated annealing (ASA): Lessons learned

Abstract: Adaptive s imulated annealing (ASA) is a global optimization algorithm based on an associated proof that the parameter space can be sampled much more ef ficiently than by using other previous simulated annealing algorithms. The author’ sA SA code has been publicly available for ove rt wo y ears. During this time the author has volunteered to help people via e-mail, and the feedback obtained has been used to further de velop the code. Some lessons learned, in particular some which are rele vant to other simulated annealing algorithms, are described.

Generalized simulated annealing

Abstract: We discuss and illustrate a new stochastic algorithm (generalized simulated annealing) for computationally finding the global minimum of a given (not necessarily convex) energy/cost function defined in a continuous D-dimensional space. This algorithm recovers, as particular cases, the so-called classical (“Boltzmann machine”) and fast (“Cauchy machine”) simulated annealings, and turns out to be quicker than both.

Adaptive simulated annealing (ASA): Lessons learned

Abstract: Adaptive s imulated annealing (ASA) is a global optimization algorithm based on an associated proof that the parameter space can be sampled much more ef ficiently than by using other previous simulated annealing algorithms. The author’ sA SA code has been publicly available for ove rt wo y ears. During this time the author has volunteered to help people via e-mail, and the feedback obtained has been used to further de velop the code. Some lessons learned, in particular some which are rele vant to other simulated annealing algorithms, are described.

Synthesis of unequally spaced arrays by simulated annealing

Abstract: Simulated annealing is applied to the synthesis of arrays in order to reduce the peaks of side lobes by acting on the elements' positions and weight coefficients. In the case considered, the number of array elements and the spatial aperture of an unequally spaced array are a priori fixed. Thanks to the high flexibility of simulated annealing, the results obtained for a 25-element array over an aperture of 50/spl lambda/ improve those reported in the literature.

Simulated annealing metaheuristics for the vehicle routing problem with time windows

Abstract: This paper develops simulated annealing metaheuristics for the vehicle routing and scheduling problem with time window constraints. Two different neighborhood structures, the λ-interchange mechanism of Osman and thek-node interchange process of Christofides and Beasley, are implemented. The enhancement of the annealing process with a short-term memory function via a tabu list is examined as a basis for improving the metaheuristic approach. Computational results on test problems from the literature as well as large-scale real-world problem are reported. The metaheuristics achieve solutions that compare favorably with previously reported results.

Genetic algorithm and graph partitioning

Abstract: Hybrid genetic algorithms (GAs) for the graph partitioning problem are described. The algorithms include a fast local improvement heuristic. One of the novel features of these algorithms is the schema preprocessing phase that improves GAs' space searching capability, which in turn improves the performance of GAs. Experimental tests on graph problems with published solutions showed that the new genetic algorithms performed comparable to or better than the multistart Kernighan-Lin algorithm and the simulated annealing algorithm. Analyses of some special classes of graphs are also provided showing the usefulness of schema preprocessing and supporting the experimental results.

Optimal switching device placement in radial distribution systems

Abstract: This paper presents a new formulation for power system sectionalizing device placement taking into consideration outage, maintenance and investments costs. The formulation of sectionalizing switches is a combinatorial constrained optimization problem with a nonlinear, nondifferentiable objective function. A solution methodology based on the optimization technique of simulated annealing, is proposed to determine: (i) the number of sectionalizing switches; and (ii) the locations of the switches. The proposed solution methodology can offer a global optimal solution for the sectionalizing device placement problem which includes the reliability, investment and maintenance costs.

A genetic local search algorithm for solving symmetric and asymmetric traveling salesman problems

Abstract: The combination of local search heuristics and genetic algorithms is a promising approach for finding near-optimum solutions to the traveling salesman problem (TSP). An approach is presented in which local search techniques are used to find local optima in a given TSP search space, and genetic algorithms are used to search the space of local optima in order to find the global optimum. New genetic operators for realizing the proposed approach are described, and the quality and efficiency of the solutions obtained for a set of symmetric and asymmetric TSP instances are discussed. The results indicate that it is possible to arrive at high quality solutions in reasonable time.

First Step Towards Planning of Syntheses in Solid-State Chemistry: Determination of Promising Structure Candidates by Global Optimization

Abstract: A method is presented here that allows, in principle, the prediction of the existence and structure of (meta)stable solid compounds. It is based on a set of adjustable modules that are applied to the study of the energy function of the chemical system of interest. The main elements are a set of routines for global optimization and local minimization, as well as algorithms for the investigation of the phase space structure near local minima of the potential energy, and the analysis and characterization of the structure candidates. The current implementation focuses on ionic compounds, for which empirical potentials are used for the evaluation of the energy function in the first stage, and a Hartree–Fock algorithm for refinements. The global optimization is performed with a stochastic simulated annealing algorithm, and the local minimization employs stochastic quenches and gradient methods. The neighborhoods of the local minima are studied with the threshold algorithm. The results of this approach are illustrated with a number of examples: compounds of binary noble gases, and binary and ternary ionic compounds. These include several substances that have not been synthesized yet, but should stand a fair chance of being kinetically stable, for example further alkali metal nitrides besides Li3N, as well as Ca3SiBr2 or SrTi2O5.

Solving the capacitor placement problem in a radial distribution system using Tabu Search approach

Abstract: In this paper, the capacitor placement problem in a radial distribution system is formulated and solved by a Tabu Search (TS) based solution algorithm. The capacitor placement problem considers practical operating constraints of capacitors, load growth, capacity of the feeder and the upper and lower bound constraints of voltage at different load levels to minimize the investment cost of capacitors and system energy loss. A sensitivity analysis method is used to select the candidate installation locations of the capacitors to reduce the search space of this problem a priori. Comparison results of the TS method with the simulated annealing (SA) show that the proposed TS method can offer the nearly optimal solution to the capacitor placement problem within reasonable computing time.

Stochastic versions of the em algorithm: an experimental study in the mixture case

Abstract: We compare three different stochastic versions of the EM algorithm: The Stochastic EM algorithm (SEM), the ''Simulated Annealing'' EM algorithm (SAEM) and the Monte Carlo EM algorithm (MCEM). We focus particularly on the mixture of distributions problem. In this context, we investigate the practical behaviour of these algorithms through intensive Monte Carlo numerical simulations and a real data study. We show that, for some particular mixture situations, the SEM algorithm is almost always preferable to the EM and ''simulated annealing'' versions SAEM and MCEM. For some severely overlapping mixtures, however, none of these algorithms can be confidently used. Then, SEM can be used as an efficient data exploratory tool for locating significant maxima of the likelihood function. In the real data case, we show that the SEM stationary distribution provides a contrasted view of the loglikelihood by emphasizing sensible maxima.

Search Heuristics for Resource Constrained Project Scheduling

Abstract: We develop a search procedure for project scheduling problems with multiple resource constraints as well as precedence constraints. The procedure is applied to three popular search heuristics, simulated annealing, tabu search and genetic algorithms. In the heuristics, a solution is represented with a string of numbers each of which denotes priority of each activity. The priorities are used to select an activity for scheduling among competing ones. The search heuristics with this encoding method can always generate feasible neighbourhood solutions for a given solution. Moreover, this encoding method is very flexible in that problems with objective functions of a general functional form (such as a nonlinear function) and complex constraints can be considered without much difficulty. Results of computational tests on the performance of the search heuristics showed that the search heuristics, especially the simulated annealing and tabu search algorithms worked better than existing heuristics.

Applying simulated annealing to location-planning models

Abstract: Simulated annealing is a computational approach that simulates an annealing schedule used in producing glass and metals. Originally developed by Metropolis et al. in 1953, it has since been applied to a number of integer programming problems, including the p-median location-allocation problem. However, previously reported results by Golden and Skiscim in 1986 were less than encouraging. This article addresses the design of a simulated-annealing approach for the p-median and maximal covering location problems. This design has produced very good solutions in modest amounts of computer time. Comparisons with an interchange heuristic demonstrate that simulated annealing has potential as a solution technique for solving location-planning problems and further research should be encouraged.

Resource-constrained project scheduling by simulated annealing

Abstract: This paper presents a new adaptation of the simulated annealing algorithm for solving non-preemptive resource-constrained project scheduling problems in which resources are limited but renewable from period to period. This algorithm is able to handle single-mode and multi-mode problems and to optimize different objective functions. Statistical experiments show the efficiency of the proposed algorithm even in comparison to some Tabu search heuristics.

A new simulated annealing algorithm for the facility layout problem

Abstract: In this paper we present an application of simulated annealing to facility layout problems with single and multiple floors. The facility layout problem is highly combinatorial in nature and generally exhibits many local minima. These properties make it a suitable candidate for simulated annealing. Using a new candidate layout generation routine and spacefilling curves, we develop an improvement-type layout algorithm based on simulated annealing that considers an expanded set of department exchanges. The resulting algorithm achieves low-cost solutions that are much less dependent on the initial layout than other approaches. We compare the performance of the simulated-annealing based algorithm with both steepest-descent and randomized approaches from the literature. Unlike other simulated annealing papers which typically present a statistical experiment to evaluate the effect of numerous control settings, all the experiments presented in this paper were conducted with control settings that are constant or e...

A simulated annealing approach for optimization of multi-pass turning operations

Abstract: In this paper, an optimization algorithm based on the simulated annealing (SA) algorithm and the Hooke-Jeeves pattern search (PS) is developed for optimization of multi-pass turning operations. The cutting process is divided into multi-pass rough machining and finish machining. Machining parameters are determined to optimize the cutting conditions in the sense of the minimum unit production cost under a set of practical machining constraints. Experimental results indicate that the proposed nonlinear constrained optimization algorithm, named SA/PS, is effective for solving complex machining optimization problems. The SA/PS algorithm can be integrated into a CAPP system for generating optimal machining parameters.

Generalized simulated annealing algorithms using Tsallis statistics: Application to conformational optimization of a tetrapeptide

Abstract: A Monte Carlo simulated annealing algorithm based on the generalized entropy of Tsallis is presented. The algorithm obeys detailed balance and reduces to a steepest descent algorithm at low temperatures. Application to the conformational optimization of a tetrapeptide demonstrates that the algorithm is more effective in locating low energy minima than standard simulated annealing based on molecular dynamics or Monte Carlo methods.

Combined genetic algorithm/simulated annealing/fuzzy set approach to short-term generation scheduling with take-or-pay fuel contract

Abstract: This paper first develops a new formulation for short-term generation scheduling with take-or-pay fuel contract. In the formulation, a fuzzy set approach is developed to assist the solution process to find schedules which meet as closely as possible the take-or-pay fuel consumption. The formulation is then extended to also cover the economic dispatch problem when the fuel consumption is higher than the agreed amount in the take-or-pay contract. The extended formulation is combined with the genetic algorithms and simulated-annealing optimization methods for the establishment of new algorithms for the present problem. The new algorithms are demonstrated through a test example, in which the generation loadings of 13 generators in a practical power system are scheduled in a 24-hour schedule horizon.

Variants of simulated annealing for the examination timetabling problem

Abstract: This paper is concerned with the use of simulated annealing in the solution of the multi-objective examination timetabling problem. The solution method proposed optimizes groups of objectives in different phases. Some decisions from earlier phases may be altered later as long as the solution quality with respect to earlier phases does not deteriorate. However, such limitations may disconnect the solution space, thereby causing optimal or near-optimal solutions to be missed. Three variants of our basic simulated annealing implementation which are designed to overcome this problem are proposed and compared using real university data as well as artificial data sets. The underlying principles and conclusions stemming from the use of this method are generally applicable to many other multi-objective type problems.

Genetic and geometric optimization of three-dimensional radiation therapy treatment planning

Abstract: The thesis of this report is that potentially useful treatment beams can be chosen based on geometric heuristics and that a genetic algorithm (GA) can be constructed to find an optimal combination of beams based on a formal objective function. The paper describes the basic principles of a GA and the particular implementation developed. The code represents each plan in the population as two paired lists comprised of beam identifiers and relative weights. Reproduction operators, which mimic sexual reproduction with crossover, mutation, cloning, spontaneous generation, and death, manipulate the lists to grow optimal plans. The necessary gene pool is created by software modules which generate beams, distribute calculation points, obtain clinical constraints, add wedges, and calculate doses. The code has been tested on a set of artificial patients and on four clinical cases: prostate, pancreas, esophagus, and glomus. All demonstrated consistent results, indicating that the code is a reliable optimizer. Additional experiments compared the results for a full set of open beams to the geometrically selected set and the GA code with simulated annealing. Geometric selection of beam directions did not significantly compromise optimization quality. Compared to simulated annealing, the genetic algorithm was equally able to optimize the objective function, and calculations suggest it may be the faster method when the number of beams to be considered exceeds approximately 70.

Scheduling short-term hydrothermal generation using evolutionary programming techniques

Abstract: The authors presents a novel evolutionary programming (EP) based algorithm for the short-term hydrothermal scheduling problem. To more realistically represent the relationship between the generation and amount of water discharge for hydroaggregates, the generation models of the hydro plants as well as thermal plants are often expressed as nonlinear and nonsmooth curves with prohibited areas. The advantage of the proposed algorithm is that it is capable of determining the global or near global optimal solutions to such an optimisation problem with multiple local minima. The developed algorithm is illustrated and tested on two model systems. The test results are compared with those obtained using gradient search, simulated annealing and genetic algorithm methods. Numerical results show that the proposed EP-based algorithm can provide accurate solutions within a reasonable time.