Showing papers on "Simulated annealing published in 2000"

Ant colony optimization for resource-constrained project scheduling

Abstract: An ant colony optimization approach (ACO) for the resource-constrained project scheduling problem (RCPSP) is presented. Combinations of two pheromone evaluation methods are used by the ants to find new solutions. We tested our ACO algorithm on a set of large benchmark problems from the PSPLIB. Compared to several other heuristics for the RCPSP including genetic algorithms, simulated annealing, tabu search, and different sampling methods our algorithm performed best on the average. For some test instances the algorithm was able to find new best solutions.

A new protocol for the determination of magnetic structures using simulated annealing and representational analysis (SARAh)

Abstract: The determination of magnetic structures from neutron diffraction data is often carried out by trial and error. Much time is wasted in the examination of structures that are in fact symmetry forbidden. The technique of representation analysis (RA) uses simple matrix calculations to provide model magnetic structures that can arise from a second-order phase transition, but has fallen into misuse because of its tedious nature. New Windows-based code performs these calculations automatically. Integration with refinement packages based on simulated annealing (SA) algorithms allows these models to be fitted against diffraction data. Combination of simulated annealing and representation analysis creates a powerful new protocol for the determination of magnetic structures.

Whole-profile structure solution from powder diffraction data using simulated annealing

Abstract: Techniques and methods to facilitate the solution of structures by simulated annealing have been developed from the starting point of a space group and lattice parameters. The simulated-annealing control parameters have been systematically investigated and optimum values characterized and determined. Most significant is the inclusion of electrostatic-potential penalty functions in a non-linear least-squares Rietveld refinement procedure. The long-range electrostatic potentials are calculated using a general real-space summation which can be used for all space groups. In addition, a general weighting scheme for penalty functions negates the need to determine weighting schemes experimentally. Also investigated and improved is the non-linear least-squares minimization procedure used in the refinement of structural parameters. The behaviour and success of the techniques have been tested on X-ray diffraction powder data against the known structures of AlVO4 in P1 with 18 atoms in the asymmetric unit, K2HCr2AsO10 in P31 with 15 atoms in the asymmetric unit excluding hydrogen, and [Co(NH3)5CO3]NO3.H2O in P121 with 15 atoms in the asymmetric unit excluding hydrogen.

Nature's heuristics for scheduling jobs on Computational Grids

Abstract: Computational Grid (Grid Computing) is a new paradigm that will drive the computing arena in the new millennium. Unification of globally remote and diverse resources, coupled with the increasing computational needs for Grand Challenge Applications (GCA) and accelerated growth of the Internet and communication technology will further fuel the development of global computational power grids. In this paper, we attempt to address the scheduling of jobs to the geographically distributed computing resources. Conventional wisdom in the field of scheduling is that scheduling problems exhibit such richness and variety that no single scheduling method is sufficient. Heuristics derived from the nature has demonstrated a surprising degree of effectiveness and generality for handling combinatorial optimization problems. This paper begins with an introduction of computational grids followed by a brief description of the three nature's heuristics namely Genetic Algorithm (GA), Simulated Annealing (SA) and Tabu Search (TS). Experimental results using GA are included. We further demonstrate the hybridized usage of the above algorithms that can be applied in a computational grid environment for job scheduling.

Corner block list: an effective and efficient topological representation of non-slicing floorplan

Abstract: In this paper, a corner block list -- a new efficient topological representation for non-slicing floorplan is proposed with applications to VLSI floorplan and building block placement. Given a corner block list, it takes only linear time to construct the floorplan. Unlike the O-tree structure, which determines the exact floorplan based on given block sizes, corner block list defines the floorplan independent of the block sizes. Thus, the structure is better suited for floorplan optimization with various size configurations of each block. Based on this new structure and the simulated annealing technique, an efficient floorplan algorithm is given. Soft blocks and the aspect ratio of the chip are taken into account in the simulated annealing process. The experimental results demonstrate the algorithm is quite promising.

A Continuous Genetic Algorithm Designed for the Global Optimization of Multimodal Functions

Abstract: Genetic algorithms are stochastic search approaches based on randomized operators, such as selection, crossover and mutation, inspired by the natural reproduction and evolution of the living creatures. However, few published works deal with their application to the global optimization of functions depending on continuous variables. A new algorithm called Continuous Genetic Algorithm (CGA) is proposed for the global optimization of multiminima functions. In order to cover a wide domain of possible solutions, our algorithm first takes care over the choice of the initial population. Then it locates the most promising area of the solution space, and continues the search through an “intensification” inside this area. The selection, the crossover and the mutation are performed by using the decimal code. The efficiency of CGA is tested in detail through a set of benchmark multimodal functions, of which global and local minima are known. CGA is compared to Tabu Search and Simulated Annealing, as alternative algorithms.

Robust design of multimachine power system stabilizers using simulated annealing

Abstract: Robust design of multimachine power system stabilizers (PSSs) using simulated annealing (SA) optimization technique is presented in this paper. The proposed approach employs SA to search for optimal parameter settings of a widely used conventional fixed-structure lead-lag PSS (CPSS). The parameters of the proposed simulated annealing based power system stabilizer (SAPSS) are optimized in order to shift the system electromechanical modes at different loading conditions and system configurations simultaneously to the left in the s-plane. Incorporation of SA as a derivative-free optimization technique in PSS design significantly reduces the computational burden. One of the main advantages of the proposed approach is its robustness to the initial parameter settings. In addition, the quality of the optimal solution does not rely on the initial guess. The performance of the proposed SAPSS under different disturbances and loading conditions is investigated for two multimachine power systems. The eigenvalue analysis and the nonlinear simulation results show the effectiveness of the proposed SAPSS's to damp out the local as well as the interarea modes and enhance greatly the system stability over a wide range of loading conditions and system configurations.

Timing-driven placement for FPGAs

Abstract: In this paper we introduce a new Simulated Annealing-based timing-driven placement algorithm for FPGAs. This paper has three main contributions. First, our algorithm employs a novel method of determining source-sink connection delays during placement. Second, we introduce a new cost function that trades off between wire-use and critical path delay, resulting in significant reductions in critical path delay without significant increases in wire-use. Finally, we combine connection-based and path-based timing-analysis to obtain an algorithm that has the low time-complexity of connection-based timing-driven placement, while obtaining the quality of path-based timing-driven placement.A comparison of our new algorithm to a well known non-timing-driven placement algorithm demonstrates that our algorithm is able to increase the post-place-and-route speed (using a full path-based timing-driven router and a realistic routing architecture) of 20 MCNC benchmark circuits by an average of 42%, while only increasing the minimum wiring requirements by an average of 5%.

Simulation and assessment of power control strategies for a parallel hybrid car

Abstract: The aim of this paper is to propose a power control strategy for hybrid electrical vehicles. This strategy uses a fuel consumption criterion with battery charge sustaining. It is based on an instantaneous minimization of the equivalent fuel flow. Two comparisons are performed to evaluate the proposed strategy. The first one uses the loss minimization strategy of Seiler and Schroder [1], which appears to be realistic and efficient for real-time control. This strategy is also based on an instantaneous optimization and allows the battery state of charge to be taken into account. The second comparison is made with an optimal solution found for a given driving schedule. Although not realistic for real-time control, this solution is derived through a global optimization algorithm, the well-known simulated annealing method.

Tabu search algorithm for network synthesis

Abstract: Large scale combinatorial problems such as the network expansion problem present an amazingly high number of alternative configurations with practically the same investment, but with substantially different structures (configurations obtained with different sets of circuit/transformer additions). The proposed parallel tabu search algorithm has shown to be effective in exploring this type of optimization landscape. The algorithm is a third generation tabu search procedure with several advanced features. This is the most comprehensive combinatorial optimization technique available for treating difficult problems such as the transmission expansion planning. The method includes features of a variety of other approaches such as heuristic search, simulated annealing and genetic algorithms. In all test cases studied there are new generation, load sites which can be connected to an existing main network: such connections may require more than one line, transformer addition, which makes the problem harder in the sense that more combinations have to be considered.

A simulated annealing algorithm for multiobjective optimization

Abstract: This paper describes a novel implementation of the Simulated Annealing algorithm designed to explore the trade-off between multiple objectives in optimization problems. During search, the algorithm maintains and updates an archive of non-dominated solutions between each of the competing objectives. At the end of search, the final archive corresponds to a number of optimal solutions from which the designer may choose a particular configuration. A new acceptance probability formulation based on an annealing schedule with multiple temperatures (one for each objective) is proposed along with a novel restart strategy. The performance of the algorithm is demonstrated on three examples. It is concluded that the proposed algorithm offers an effective and easily implemented method for exploring the trade-off in multiobjective optimization problems.

Optimum positioning of base stations for cellular radio networks

Abstract: Finding optimum base station locations for a cellular radio network is considered as a mathematical optimization problem. Dependent on the channel assignment policy, the minimization of interferences or the number of blocked channels, respectively, may be more favourable. In this paper, a variety of according analytical optimization problems are introduced. Each is formalized as an integer linear program, and in most cases optimum solutions can be given. Whenever by the complexity of the problem an exact solution is out of reach, simulated annealing is used as an approximate optimization technique. The performance of the different approaches is compared by extensive numerical tests.

Solving the generalized indirect Fourier transformation (GIFT) by Boltzmann simplex simulated annealing (BSSA)

Abstract: The structure of colloidal particles can be studied with small-angle X-ray and neutron scattering (SAXS and SANS). In the case of randomly oriented systems, the indirect Fourier transformation (IFT) is a well established technique for the calculation of model-free real-space information. Interaction leads to an overlap of inter- and intraparticle scattering effects, preventing most detailed interpretations. The recently developed generalized indirect Fourier transformation (GIFT) technique allows these effects to be separated by assuming various models for the interaction, i.e. the so-called structure factors. The different analytical behaviour of these structure factors from that of the form factors, describing the intraparticle scattering, allows this separation. The mean-deviation surface is defined by the quality of the fit for different parameter sets of the structure factor. Its global minimum represents the solution. The former non-linear least-squares approach has proved to be inefficient and not very reliable. In this paper, the incorporation of the completely different Boltzmann simplex simulated annealing (BSSA) algorithm for finding the global minimum of the hypersurface is presented. This new method increases not only the calculation speed but also the reliability of the evaluation.

The Metropolis Algorithm

Abstract: The Metropolis Algorithm has been the most successful and influential of all the members of the computational species that used to be called the "Monte Carlo method". Today, topics related to this algorithm constitute an entire field of computational science supported by a deep theory and having applications ranging from physical simulations to the foundations of computational complexity. Since the rejection method invention (J. von Neumann), it has been developed extensively and applied in a wide variety of settings. The Metropolis Algorithm can be formulated as an instance of the rejection method used for generating steps in a Markov chain.

Assembly line balancing using genetic algorithms

Abstract: Assembly Line Balancing (ALB) is one of the important problems of production/operations management area. As small improvements in the performance of the system can lead to significant monetary consequences, it is of utmost importance to develop practical solution procedures that yield high-quality design decisions with minimal computational requirements. Due to the NP-hard nature of the ALB problem, heuristics are generally used to solve real life problems. In this paper, we propose an efficient heuristic to solve the deterministic and single-model ALB problem. The proposed heuristic is a Genetic Algorithm (GA) with a special chromosome structure that is partitioned dynamically through the evolution process. Elitism is also implemented in the model by using some concepts of Simulated Annealing (SA). In this context, the proposed approach can be viewed as a unified framework which combines several new concepts of AI in the algorithmic design. Our computational experiments with the proposed algorithm indicate that it outperforms the existing heuristics on several test problems.

Hybrid evolutionary techniques for the maintenance scheduling problem

Abstract: The incorporation of local search operators into a genetic algorithm has provided very good results in certain scheduling problems. The resulting algorithm from this hybrid approach has been termed a memetic algorithm. This paper investigates the use of a memetic algorithm for the thermal generator maintenance scheduling problem. The local search operators alone have been found (in earlier work by the authors and others) to produce good quality results. The main purpose of this paper is to discover whether a memetic approach can produce better results. We describe the approach taken and highlight the variety of local search algorithms that were employed. We compare the memetic algorithms with a variety of algorithms that include the local search operators on their own and a range of algorithms that apply the local search operator to randomly generated solutions. We see that, for the problems tested, the memetic algorithms produce better quality solutions (although they do take more time about it). Of course, in practice, for a problem like this, the time taken to produce a solution is not a major issue. What is far more important is the quality of the solution. We conclude that the most effective method (of the ones tested here) is a memetic approach that employs a tabu-search operator.

The annealing robust backpropagation (ARBP) learning algorithm

Abstract: Multilayer feedforward neural networks are often referred to as universal approximators. Nevertheless, if the used training data are corrupted by large noise, such as outliers, traditional backpropagation learning schemes may not always come up with acceptable performance. Even though various robust learning algorithms have been proposed in the literature, those approaches still suffer from the initialization problem. In those robust learning algorithms, the so-called M-estimator is employed. For the M-estimation type of learning algorithms, the loss function is used to play the role in discriminating against outliers from the majority by degrading the effects of those outliers in learning. However, the loss function used in those algorithms may not correctly discriminate against those outliers. In the paper, the annealing robust backpropagation learning algorithm (ARBP) that adopts the annealing concept into the robust learning algorithms is proposed to deal with the problem of modeling under the existence of outliers. The proposed algorithm has been employed in various examples. Those results all demonstrated the superiority over other robust learning algorithms independent of outliers. In the paper, not only is the annealing concept adopted into the robust learning algorithms but also the annealing schedule k/t was found experimentally to achieve the best performance among other annealing schedules, where k is a constant and t is the epoch number.

Simulated annealing algorithms for continuous global optimization: convergence conditions

Abstract: In this paper, simulated annealing algorithms for continuous global optimization are considered. After a review of recent convergence results from the literature, a class of algorithms is presented for which strong convergence results can be proved without introducing assumptions which are too restrictive. The main idea of the paper is that of relating both the temperature value and the support dimension of the next candidate point, so that they are small at points with function value close to the current record and bounded away from zero otherwise.

Multiobjective simulated annealing: a comparative study to evolutionary algorithms

Abstract: As multiobjective optimization problems have many solutions, evolutionary algorithms have been widely used for complex multiobjective problems instead of simulated annealing. However, simulated annealing also has favorable characteristics in the multimodal search. We developed several simulated annealing schemes for the multiobjective optimization based on this fact. Simulated annealing and evolutionary algorithms are compared in multiobjective NK model. The preliminary results of the simulated annealing developed show that simulated annealing method performs well and sometimes better than evolutionary algorithms. More systematical analyses to the various problems are discussed as further researches.

Simulated annealing for parallel machine scheduling with earliness-tardiness penalties and sequence-dependent set-up times

Abstract: Scheduling problems with earliness and tardiness penalties are commonly encountered in today's manufacturing environment due to the current emphasis on the just-in-time (JIT) production philosophy. The problem studied in this work is the parallel machine earliness-tardiness non-common due date sequence-dependent set-up time scheduling problem (PETNDDSP) for jobs with varying processing times, where the objective is to minimize the sum of the absolute deviations of job completion times from their corresponding due dates. The research presented provides a first step towards obtaining near optimal solutions for this problem using local search heuristics in the framework of a meta-heuristic technique known as simulated annealing (SA). The computational study shows that, using the SA methodology, significant improvements to the local search heuristic solutions can be achieved for problems of this type.

A Simulated Annealing Approach for Buffer Allocation in Reliable Production Lines

Abstract: We describe a simulated annealing approach for solving the buffer allocation problem in reliable production lines. The problem entails the determination of near optimal buffer allocation plans in large production lines with the objective of maximizing their average throughput. The latter is calculated utilizing a decomposition method. The allocation plan is calculated subject to a given amount of total buffer slots in a computationally efficient way.