Layout exploration for three-dimensional Networks-on-Chip: Chemical equilibrium simulation approach

This paper combines the chemical reaction optimisation algorithm with the greedy algorithm to solve the three-dimensional bin packing problem (3D-BPP). Traditionally, the 3D-BPP is solved with algorithms based on particle swarm optimisation, genetic algorithm, or simulated annealing algorithm, etc. The chemical reaction optimisation algorithm is proposed in recent years, which has the advantages of traditional simulated annealing algorithm and has a faster convergence speed. After making appropriate adjustments to the chemical reaction optimisation algorithm, the experiment was performed on classic 320 examples. The results show that the proposed algorithm outperforms the existing algorithms such as greedy variable neighbourhood descent (GVND) and biased random key genetic algorithm (BRKGA).

In this paper, we propose a novel discrete chemical-reaction optimization (DCRO) algorithm for solving the flexible job shop scheduling problem with three objectives. The molecule is used to represent a solution. The four elementary reactions, i.e., the on-wall ineffective collision, the decomposition, the inter-molecular ineffective collision, and the synthesis, are used as the operators for the hybrid algorithm. In the hybrid algorithm, the crossover operator is embedded to learn information among molecules. To increase the ability to escape from the local optima, the buffer is used as the energy center to share kinetic energy among molecules. Experimental results on the well-known benchmarks show the efficiency and effectiveness of the proposed algorithm.

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.

A novel approach for optimization of electrically serial two-stage thermoelectric refrigeration systems using chemical reaction optimization (CRO) algorithm

To find the best polarity of large-scale Mixed Polarity Reed-Muller (MPRM) logic circuits, this paper proposes a new Adaptive Simulated Annealing Genetic Algorithm (ASAGA) which can effectively find out the best polarity. Genetic Algorithm (GA) has outstanding global searching ability but easily falls into the local optimum, while the Simulated Annealing Algorithm (SAA) is expert in local searching but has the limitation of poor convergence. The new method incorporating SAA into GA is proposed which can play their strengths to the utmost extent and makes up their shortcomings. Additionally, for the key parameters of ASAGA, we adopt self-adaptive adjustment, which can effectively improve the convergence and robustness of the algorithm. The results proved the efficiency and accuracy of ASAGA in searching the best polarity of large-scale MPRM logic circuits.

A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems

As demonstrated in Chap. 3, different evolutionary algorithms, including genetic algorithm (GA), simulated annealing (SA) algorithm, hybrid GASA algorithm, particle swarm optimization (PSO) algorithm, continuous ant colony optimization (CACO) algorithm, artificial bee colony (ABC) algorithm, and immune algorithm (IA), are employed to determine suitable for parameter combination of an SVR-based electric load forecasting model. These forecasting results indicate that almost all SVR-based models with different evolutionary algorithms are superior to other competitive forecasting models (including ARIMA, HW, GRNN, and BPNN models); however, these algorithms almost lack knowledge memory or storage mechanisms which would be neither time-consuming nor inefficient in searching the suitable parameters, that is, premature convergence (being trapped in local optimum). For example, for SVRGA model, in GA processing, new individuals are generated by the following operators, selection, crossover, and mutation. For all types of objective functions, the generation begins with a binary coding for the parameter set. Based on this special binary coding process, GA is able to solve some specified problems which are not easily to be solved by traditional algorithms. GA can empirically provide a few best fitted offsprings from the whole population; however, after some generations, due to low diversity of the population, it might lead to a premature convergence [1]. For SVRSA model, SA algorithm is a generic probabilistic search technique that simulates the material physical process of heating and controlled cooling. Each step of SA attempts to replace the current state by a random move. The new state may then be accepted with a probability that depends both on the difference between the corresponding function values and also on a global parameter, temperature. Thus, SA has some institution to reach more ideal solutions. However, SA algorithm requires the subtle and skillful adjustment in the annealing schedule; therefore, the settings of the size of the temperature steps during annealing, the temperature range, the number of restarts, and the redirection of the search will be taken into account carefully [1]; in addition, its Monte Carlo scheme and lack of knowledge memory mechanism will also lead to time-consuming and inefficient searching in annealing process.

The intensity modulated radiation therapy (IMRT) treatment planning problem consists of several subproblems which are typically solved sequentially. We seek to combine two of the subproblems: the beam orientation optimization (BOO) problem and the fluence map optimization (FMO) problem. The BOO problem is the problem of selecting the beam orientations to deliver radiation to the patient. The FMO problem is the problem of determining the amount of radiation intensity, or fluence, of each beamlet in each beam. The solution to the FMO problem measures the quality of a beam set, but the majority of previous BOO studies rely on heuristics and approximations to gauge the quality of the beam set. In contrast with these studies, we use an exact measure of the treatment plan quality attainable using a given beam set, which ensures convergence to a global optimum in the case of our simulated annealing algorithm and a local optimum in the case of our local search algorithm. We have also developed a new neighborhood structure that allows for faster convergence using our simulated annealing and local search algorithms, thus reducing the amount of time required to obtain a good solution. Finally, we show empirically that we can generate clinically acceptable treatment plans that require fewer beams than in current practice. This may reduce the length of treatment time, which is an important clinical consideration in IMRT.

n this study, development of Al-water fed arcjet was conducted, in which water and aluminum were fed to the thruster as the propellant. Numerical investigations based on chemical equilibrium calculations were conducted to understand the details of combustion reactions and products of the electrically augmented aluminum and water reactions. From the chemical equilibrium simulation, it was shown that thrust performance of the Al-water fed arcjet was as well as that of hydrazine arcjets at low specific power range. In addition, to observe behaviors of vaporized aluminum propellant fed as a solid state rod to the arcjet and then vaporized in the discharge plenum, a novel coaxial double cathode was developed and tested. From hispeed camera images, it was shown that streams of vaporized aluminum were injected through the injection holes at a tip of the coaxial double cathode. From the spectroscopic diagnostics of the plasma in the plenum, it was confirmed that injected vapor from the injectors of the coaxial double cathode was mainly vaporized aluminum.

This paper presents the solving unit commitment (UC) problem using Modified Subgradient Method (MSG) method combined with Simulated Annealing (SA) algorithm. UC problem is one of the important power system engineering hard-solving problems. The Lagrangian relaxation (LR) based methods are commonly used to solve the UC problem. The main disadvantage of this group of methods is the difference between the dual and the primal solution which gives some significant problems on the quality of the feasible solution. In this paper, MSG method which does not require any convexity and differentiability assumptions is used for solving the UC problem. MSG method depending on the initial value reaches zero duality gap. SA algorithm is used in order to assign the appropriate initial value for MSG method. The major advantage of the proposed approach is that it guarantees the zero duality gap independently from the size of the problem. In order to show the advantages of this proposed approach, the four-unit Tuncbilek thermal plant and ten-unit thermal plant which is usually used in literature are chosen as test systems. Penalty function (PF) method is also used to compare with our proposed method in terms of total cost and UC schedule.

For the congestion problems in asynchronous transfer mode (ATM) networks, a hybrid intelligent controller based on simulated annealing (SA) and reinforcement learning (RL) algorithm is proposed. The SA algorithm is a powerful way to solve hard combinatorial optimization problems, which is used to adjust the parameters of the controller. The RL algorithm shows the particular superiority in ATM networks, which is independent of the mathematic model and just needs simple fuzzy information obtained through trial-and-error and interaction with the environment. With the advantages of the two algorithms, the proposed controller forces the queue size in the multiplexer buffer to the desired value by adjusting the source transmission rate of the available bit rate (ABR) service. Simulation results show that the proposed method can promote the performance of the networks and avoid the occurrence of congestion effectively.

A hybrid imperialist competitive-simulated annealing algorithm for a multisource multi-product location-routing-inventory problem

An algorithm for the steady-state simulation of two- and three-phase multistage reactive distillation processes with equilibrium chemical reactions is developed. In the developed algorithm, the phase stability, phase equilibrium, and chemical reaction equilibrium calculations are preformed simultaneously. The algorithm was used to simulate a variety of two- and three-phase reactive distillation processes. The simulation results were compared against the available experimental data in the literature. Good agreement was observed between the simulation results and experimental data. A multistage reactive distillation process for the production of cyclohexanol from the hydration of cyclohexene with two liquid phases present on multiple stages was simulated. A high-purity cyclohexanol product with a high cyclohexene conversion was obtained for this process.

Functional biological sequences, which typically come in families, have retained some level of similarity and function during evolution. Finding consensus regions, alignment of sequences, and identifying the relationship between a sequence and a family allow inferences about the function of the sequences. Profile hidden Markov models (HMMs) are generally used to identify those relationships. A profile HMM can be trained on unaligned members of the family using conventional algorithms such as Baum-Welch, Viterbi, and their modifications. The overall quality of the alignment depends on the quality of the trained model. Unfortunately, the conventional training algorithms converge to suboptimal models most of the time. This work proposes a training algorithm that early identifies many imperfect models. The method is based on the Simulated Annealing approach widely used in discrete optimization problems. The training algorithm is implemented as a component in HMMER. The performance of the algorithm is discussed on protein sequence data.KeywordsSimulated AnnealingHide Markov ModelMultiple AlignmentTraining AlgorithmSimulated Annealing AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Dynamic economic dispatch (DED) that considers valve-point effects is a complex nonconvex and nonsmooth optimization problem in power systems. Over the past few decades, multiple approaches have been developed to solve this problem. In this paper, an adaptive differential evolution based on simulated annealing algorithm is proposed to solve the DED problem with valve-point effects. Simulated annealing (SA) algorithm is employed to carry out an adaptive selection mechanism in which the mutation operators of differential evolution (DE) are selected adaptively based on their historical performance. A mutation operator pool consisting of five operators is built to make each operator show its strength at different stages of the evolutionary process. Moreover, a heuristic strategy is introduced to transform infeasible solutions towards feasible ones to enhance the convergence rate of the proposed algorithm. The effectiveness of the proposed methods is demonstrated first on 10 popular benchmark functions with 100 dimensions, in comparison with the classic DE and five variants. Then, it is used to solve four DED problems with 10, 15, 30, and 54 units, which consider the valve-point effects, transmission loss, and prohibited operating zones. The simulation results are compared with those of state-of-the-art algorithms to clarify the significance of the proposed method and verify its performance. Three systems with 100-500 generators are also tested to confirm the advantages of the proposed method on large-scale DED problem.