A Simulated Annealing Algorithm with Constant Temperature for Discrete Stochastic Optimization

A modification of the simulated annealing (SA) algorithm for solving discrete stochastic optimization problems where the objective function is stochastic and can be evaluated only through Monte Carlo simulation is proposed. In this modification, the Metropolis criterion depends on whether the objective function values indicate a statistically significant difference at each iteration. The differences between objective function values are considered to be statistically significant based on confidence intervals associated with these values. Unlike the original SA, the proposed method uses a constant temperature. It is shown that the configuration that has been visited most often in the first k iterations converges almost surely to a global optimizer. Computational results and comparisons with other SA algorithms are presented to demonstrate the performance of the proposed SA algorithm.

In this paper, we study the problem of mutual interference cancellation among secondary users (SUs) and interference control to primary users (PUs) in spectrum sharing underlay cognitive radio networks (CRNs). Multiple antennas are used at the secondary base station (SBS) to form multiple beams towards to individual SUs, and a set of SUs are selected to adapt to the beams. For the interference control to PUs, we study power allocation among SUs to guarantee the interference to PUs below a tolerable level while maximizing SUs' QoS. Based on these conditions, the problem of joint power allocation and beamforming with SUs selection is studied. Specifically, we emphasize on the condition of imperfect channel sensing. And we formulate the problem as a discrete stochastic optimization problem, then an efficient algorithm based on a discrete stochastic optimization method is proposed to solve the joint power allocation and beamforming with SUs selection problem. The proposed algorithm has fast convergence rate. Finally, simulation results are presented to demonstrate the performance of the proposed scheme.

Solution quality of random search methods for discrete stochastic optimization

The patent applications filings (PAF) are complex to conduct due to its nonlinearity of influenced factors. In this study, a grey support vector machines with simulated annealing algorithms (GSVMS) is proposed to forecast PAF. In addition, GM (1, N) model of grey system is used to add a grey layer before neural input layer and white layer after SVM layer. Simulated annealing algorithms (SA) are used to determine free parameters of support vector machines. Evaluation method has been used for comparing the performance of forecasting techniques. The experiments show that the GSVMS model is outperformed GM (1, N) model and SVM with simulated annealing algorithms (SVMS) model, and PAF forecasting based on GSVMS is of validity and feasibility.

Simulation-based optimization using simulated annealing with ranking and selection

A combined strategy of an improved simulated annealing (SA) algorithm and genetic algorithm is presented, with the goal of reducing the computational expenses. The improvements made on the SA algorithm include two parts, i.e., the adaptive regulating for the step vector, and the dynamic testing for the equilibrium of the Metropolis process. The proposed strategy has both the advantage of SA algorithm, the ability to avoid being trapped in a local optimum, and that of genetic algorithm, the ability to use the information about the searched states for the next iteration. A practical application on geometry optimization of pole shoes in large salient pole synchronous generators is effectively implemented using the strategy. The numerical results show that the number of iterations used by executing the combined strategy are only about 75% of those by executing basic SA algorithm.

We consider here the average deviation as the most important objective when designing numerical techniques and algorithms. We call that a Bayesian approach. We start by describing the Bayesian approach to the continu­ ous global optimization. Then we show how to apply the results to the adaptation of parameters of randomized techniques of optimiza­ tion. We assume that there exists a simple function which roughly predicts the consequences of decisions. We call it heuristics. We define the probability of a decision by a randomized decision func­ tion depending on heuristics. We fix this decision function, except for some parameters that we call the decision parameters. We repeat the randomized decision procedure several times given the decision parameters and regard the best outcome as a result. We optimize the decision parameters to make the search more efficient. Thus we replace the original optimization problem by an auxiliary problem of continuous stochastic optimization. We solve the auxiliary problem by the Bayesian methods of global op­ timization. Therefore we call the approach as the Bayesian one. We discuss the advantages and disadvantages of the Bayesian approach. We describe the applications to some of discrete pro-

Hybrid membrane-distillation systems are reported to be an energy-efficient separation processes compared to conventional distillation process; especially for separating close-boiling mixtures. However, several optimization methods are available and can be applied to find a cost-effective design and operation of hybrid membrane-distillation systems. This work evaluates the hybrid system design in terms of energy and compares the performance (i.e. the accuracy and computational efficiency) of stochastic optimisation method (Simulated Annealing) and deterministic method (Pattern search method) for optimisation of process operating conditions. The parallel hybrid membrane-distillation system for the separation of ethylene-ethane mixture is selected for analysis. The decision variables manipulated for the optimisation are the stage cut and permeate pressure. Results show that pattern search is faster than Simulated Annealing but that is more able to find the close-global results. The difference between the optimal result found by Simulated Annealing and pattern search is about 0.02 % for the total operating costs, 0.3 % for the stage cut and 5 % for the permeate pressure. These findings suggest that it is possible to use deterministic method for optimisation complex system. However, to locate an optimal solution and increase confidence in the result, a variety of initial points should be tested.

This paper develops a variant of simulated annealing (SA) algorithm for solving discrete stochastic optimization problems where the objective function is stochastic and can be evaluated only through Monte Carlo simulations. In the proposed variant of SA, the Metropolis criterion depends on whether the objective function values indicate statistically significant difference at each iteration. The differences between objective function values are considered to be statistically significant based on confidence intervals associated with these values. Unlike the original SA, our method uses a constant temperature. We show that the configuration that has been visited most often in the first m iterations converges almost surely to a global optimizer.

Cognitive radio networks with cooperative transmission could effectively improve the spectrum efficiency and data rate. Under this network scenario, based on the sensing results and the channel quality on all channels, there are three possible transmission modes: direct transmission, multi-hop transmission and cooperative transmission. To optimize the system performance, three issues should be carefully considered: whether or not and which relay is needed, which channel is selected, and which transmission mode is used. Therefore, in this paper, we solve these three problems jointly to optimize the outage capacity for cognitive radio networks with cooperative transmission. Specifically, we emphasize on the imperfect channel sensing condition. We formulate the optimization problem as a discrete optimization problem. Then an efficient algorithm based on a discrete stochastic optimization algorithm is proposed to maximize the outage capacity to determine the relay and select the optimal channel and transmission mode. Simulation results are presented to demonstrate the performance of the proposed scheme.

Nowadays signature attacks are termed as very big problem because it leads to software vulnerability.Malware writers confuse their malicious code to malicious code detectors such as Signature-based detection. However,it fails to detect new malware. This research article addresses the signature based intrusion detection from IntrusionDetection (IDS) systems. The proposed hybrid techniques for Generation of Signature are done using GeneticAlgorithm (GA) and Simulated Annealing (SA) approaches. For this, signature-set in execution statements are selected by using simulated annealing and genetic algorithm, which produce the optimal solution of selection. Then thegenerated signatures are matched with IDS by using the two pattern matching techniques, namely (i). Finite stateautomaton based search for Single Pattern matching technique and (ii) Rabin Karp string search algorithm for multiple pattern matching technique. These techniques are used to match the signature as in an effective manner. In addition tothis the Fuzzy Logic classification is used to find the degrees of truth of vulnerability for classification. The aim ofthe proposed work is to improve the final resultant accuracy in compared to existing techniques. The proposed RabinKarp- fuzzy logic system returns the higher performance metrics namely precision is 88% and Recall is 80% and inopen source dataset it contains 30 vulnerabilities this proposed worked well in detecting 28 vulnerabilities/ defect, theaccuracy of this proposed is 94.27%.

Over recent years, new methods have been developed to integrate uncertainty into the optimisation of life-of-mine production planning. One of these methods is based on scheduling with a simulated annealing (SA) algorithm and equally probable realisations of a given mineral deposit. The latter realisations are used to generate production schedules that minimise the possibility of deviating from production targets, and result in schedules with a substantial improvement in the net present value (NPV), shown to be in the order of 25% when compared to conventional scheduling within the conventionally optimal pit limits. To facilitate the utilisation of this method, a sensitivity analysis is presented in this study. The study documents the case of a copper deposit where 10 simulated realisations are sufficient to provide stable life-of-mine optimisation results. In addition, the study shows that the selected true optimal pit limits are larger than those derived through conventional optimisation. Stochastically optimised pit limits are found to be ∼17% larger, in terms of total tonnage, than the conventional (deterministic) optimal pit limits. The difference adds one year of mining and ∼10% of additional NPV when compared to the NPV of conventional optimal pit limits and a production schedule generated stochastically with the same simulated annealing algorithm.

With the increasing complexity of modern VLSI circuits, achieving high quality built-in self-test requires monitoring an increasingly large number of internal nodes. Due to the limitations in observing large numbers of nodes, it has become increasingly necessary to compact the output from a large number of lines to a small number of lines in a process known as space compaction. In the past, space compaction has usually been performed by circuit-independent compactors, such as the popular parity function. Recently, a class of circuit-specific space compactors, known as programmable space compactors (PSCs), has been proposed. A PSC is a highly effective space compactor tailored for a specific circuit under test (CUT) subjected to a specific set of test stimuli. Circuit-specific information such as the fault-free and expected faulty behaviour of a circuit can be used to choose compactors which have better fault coverage and/or lower area costs than the parity function. The drawback of PSCs is the difficulty involved in finding optimal PSCs for a circuit. The space of possible PSC functions is extremely large and grows exponentially with the number of lines to be compacted. Although searching for optimal PSC functions is a difficult problem, it is not impossible. This thesis presents a method for searching for combinational PSCs based upon genetic algorithms (GAs), a randomized search technique. Randomized search techniques, such as simulated annealing and genetic algorithms, have become increasingly popular in recent years as methods for solving search and optimization problems beyond the capabilities of classical techniques. This thesis develops a procedure for using circuit-specific information in the application of (GAs) to the search for optimal PSCs for a given CUT and a given set of test stimuli. A measure of the effectiveness, or fitness, of PSCs is developed as a means for guiding the genetic search. The factors used to assess the effectiveness of a PSC are its

Summary The Simulated Annealing (SA) algorithm has been widely used in geophysical data processing. However, the seri al SA algorithm is limited by low computing efficiency. In this paper, the annealing schedule has been improved in the SA algorithm, and a constrained inversion of magnetotelluric (MT) data using the parallel SA algorithm has been applied. The test of synthetic data shows that the improved Very Fast Simulated Annealing (VFSA) method is significantly better than the traditional method. Additionally, the computing efficiency has been improved using the parallel SA inversion algorithm. This method has also been applied in MT data processing for geothermal exploration and good geological results have been achieved.

We discuss the choice of the estimation of the optimal solution when random search methods are applied to solve discrete stochastic optimization problems. At the present time, such optimization methods usually estimate the optimal solution using either the feasible solution the method is currently exploring or the feasible solution visited most often so far by the method. We propose using all the observed objective function values generated as the random search method moves around the feasible region seeking an optimal solution to obtain increasingly more precise estimates of the objective function values at the different points in the feasible region. At any given time, the feasible solution that has the best estimated objective function value (largest one for maximization problems; the smallest one for minimization problems) is used as the estimate of the optimal solution. We discuss the advantages of using this approach for estimating the optimal solution and present numerical results showing that modifying an existing random search method to use tnhis approach for estimating the optimal soluation appears to yield improved performance. We also present sereval rate of convergence results for random search methods using our approach for estimating the optimal solution. One these random search methods is a new variant of the stochastic comparison method; in addition to specifying the rate of convergence of this method, we prove that it is guaranteed to converge almost surely to the set of global optimal solutions and present a result that demonstrates that this method is likely to perform well in practice.