Performance modeling and optimization for the stock preparation unit of a paper plant using genetic algorithm

PurposeThis paper deals with the performance optimization and sensitivity analysis for crystallization system of a sugar plant.Design/methodology/approachCrystallization system comprises of five subsystems, namely crystallizer, centrifugal pump and sugar grader. The Chapman–Kolmogorov differential equations are derived from the transition diagram of the crystallization system using mnemonic rule. These equations are solved to compute reliability and steady state availability by putting the appropriate combinations of failure and repair rates using normalizing and initial boundary conditions. The performance optimization is carried out by varying number of generations, population size, crossover and mutation probabilities. Finally, sensitivity analysis is performed to analyze the effect of change in failure rates of each subsystem on availability, mean time to failure (MTBF) and mean time to repair (MTTR).FindingsThe highest performance observed is 96.95% at crossover probability of 0.3 and sugar grader subsystem comes out to be the most critical and sensitive subsystem.Originality/valueThe findings of the paper highlights the optimum value of performance level at failure and repair rates for subsystems and also helps identify the most sensitive subsystem. These findings are highly beneficial for the maintenance personnel of the plant to plan the maintenance strategies accordingly.

Optimization of the performance of flotation circuits using a genetic algorithm oriented by process-based rules

The article presents the structure and stages of implementing a computer geometric modeling program designed to solve a discrete geometry problem: the approximation of free-form surfaces using polyhedra with groups of congruent faces. Solving this problem reduces the costs of physical construction of the specified surface in the form of building facades, particularly in the new architectural trend known as "parametricism." The optimization method utilized in the program enables the creation of geometric models that are valuable in architecture, industrial design, and computer graphics. A key component of the program is a genetic algorithm, one of the evolutionary computation methods. The paper provides a detailed analysis of the genetic algorithm's parameters, including the number of generations, population size, mutation and crossover probabilities. The optimal set of parameters for the program is determined experimentally. Experimental results demonstrate the effectiveness of various algorithm configurations. The findings reveal that the optimization method used minimizes the number of distinct congruent polyhedral faces. The advantages of the method are evaluated within the program’s constraints, as well as its limitations, such as computational complexity and the need for parameter tuning. The conclusion discusses prospects for future research, including enhancing the algorithm's efficiency, developing new approaches for normalizing and discretizing input data, controlling and modifying the topology of the resulting polyhedra, and exploring alternative optimization methods.

The query cost usually as an important criterion for a distributed database. The genetic algorithm is an adaptive probabilistic search algorithm, but the crossover and mutation probability usually keep a probability in traditional genetic algorithm. If the crossover probability keep a large value, the possibility of damage for genetic algorithm model is greater; In turn, if the crossover probability keep a small value, the search process will transform a slow processing or even stagnating. If the mutation probability keep a small value, a new individual can be reproduced difficultly; In turn, if the mutation probability keep a large value, the genetic algorithm will as a Pure random search algorithm. To solve this problem, proposed a improved genetic algorithm that multiple possibility of crossover and mutation based on k-means clustering algorithm. The experiment results indicate that the algorithm is effective.

The Unified Parallel C (UPC) programming language offers parallelism via logically partitioned shared memory, which typically spans physically disjoint memory subsystems. One convenient feature of UPC is its ability to automatically execute between-thread data movement, such that the entire content of a shared data array appears to be freely accessible by all the threads. The programmer friendliness, however, can come at the cost of substantial performance penalties. This is especially true when indirectly indexing the elements of a shared array, for which the induced between-thread data communication can be irregular and have a fine-grained pattern. In this paper, we study performance enhancement strategies specifically targeting such fine-grained irregular communication in UPC. Starting from explicit thread privatization, continuing with block-wise communication, and arriving at message condensing and consolidation, we obtained considerable performance improvement of UPC programs that originally require fine-grained irregular communication. Besides the performance enhancement strategies, the main contribution of the present paper is to propose performance models for the different scenarios, in the form of quantifiable formulas that hinge on the actual volumes of various data movements plus a small number of easily obtainable hardware characteristic parameters. These performance models help to verify the enhancements obtained, while also providing insightful predictions of similar parallel implementations, not limited to UPC, that also involve between-thread or between-process irregular communication. As a further validation, we also apply our performance modeling methodology and hardware characteristic parameters to an existing UPC code for solving a 2D heat equation on a uniform mesh.

Modeling and optimization of performance and emission parameters of a diesel engine: A comparative evaluation between date seed oil biodiesel produced via three different heating systems

In this paper, gratings to apply for the optical interconnection are designed using a genetic algorithm (GA) for a robust and efficient schema. The real-time optical interconnection system architecture is composed with LC-SLM, CCD array detector, IBM-PC, He-Ne laser, and Fourier transform lens. A pixelated binary phase grating is displayed on LC-SLM and could interconnect incoming beams to desired output spots freely by real-time. So as to adapt a GA for finding near globally-cost solutions, a chromosome is coded as a binary integer of length 32 X 32, the stochastic tournament method for decreasing the stochastic sampling error is performed, and a single-point crossover having 16 X 16 block size is used. The characteristics on the several parameters are analyzed in the desired grating design. Firstly, as the analysis of the effect on the probability of crossover, a designed grating when the probability of crossover is 0.75 has a 74.7[%] high diffraction efficiency and a 1.73 X 10<SUP>-1</SUP> uniformity quantitatively, where the probability of mutation is 0.001 and the population size is 300. Secondly, on the probability of mutation, a designed grating when the probability of mutation is 0.001 has a 74.4[%] high efficiency and a 1.61 X 10<SUP>-1</SUP> uniformity quantitatively, where the probability of crossover is 1.0 and the population size is 300. Thirdly, on the population size, a designed grating when the population size is 300 and the generation is 400 has above 74[%] diffraction efficiency, where the probability of mutation is 0.001 and the probability of crossover is 1.0.

We review the recent work done by our group on theoretically analyzing the behavior of genetic algorithms(GAs) and searching for new ideas and strategies to design more efficient GAs. By introducing the concept of degree of population diversity and using the tools from finite Markov chain, we presented fairly clear characterizations of premature convergence in GAs and proved some results on the relationships between premature convergence and GA’s control parameters such as the population size, the mutation and crossover probabilities, and some population statistics. In the mean time, we have also obtained a result that depicts the search ability of the crossover operators in GAs. An upper bound on the convergence rate for the GAs has been established. Based on the theoretical analyses, we have proposed a novel GA called Genetic Algorithm with No Genetic Operators (GANGO), which needs neither to maintain a population nor to use the conventional genetic operators, and yet has the same search mechanisms as the classical GAs. As another development in improving the performance of GAs, a new concept called Dual Fitness Function was proposed which, when incorporated into the classical GAs will enable the algorithms to judge whether the current population is in the local or global attractive basins. And in this way, the control parameters of GAs can be adjusted adaptively. Some applications of our results to fields such as fuzzy systems, multi-destination routing problem (MDR) are briefly

A new performance and area optimization algorithm for complex VLSI systems is presented. It is widely believed within the VLSI CAD community that the relationship between delay and silicon area of a VLSI chip is convex. This conclusion is based on a simplified linear RC model to predict gate delays. In the proposed optimization algorithm, a nonlinear, non‐RC based transistor delay model was used which resulted in a non‐convex relationship between the delay and the silicon area of a VLSI chip. Genetic algorithms are better suited for discrete, non‐convex, non‐linear optimization problems than traditional calculus‐based algorithms. By using the genetic algorithms in the performance and area optimization, we are able to find the optimal values for both delay and silicon area for the ISCAS benchmark circuits.

Genetic algorithms (GA) have been widely used to solve planning problems. However, they require one to determine the optimal values of many genetic parameters, such as population sizes, crossover probability, mutation probability, and so on. To make matters worse, the most suitable combination of parameters for one problem is not always optimal for others. Therefore, these parameters should be tuned whenever the problem changes. In this paper, we propose an adaptable GA mechanism that has autonomic parameter tuning for the composition of generic operators. This mechanism raises questions concerning the probability of genetic operators that acted effectively, that is, the probability that one operator created better individuals than the other operators. It also successively adjusts the combinations of genetic parameters suitable for the target problem. We applied the adaptable GA mechanism to a project scheduling model (PSM) and evaluated it with manual tuning methods. © 1998 Scripta Technica, Electr Eng Jpn, 124(2): 36–42, 1998

Genetic algorithms (GAs) are general-purpose heuristic search algorithms that mimic the evolutionary process in order to find the most fitting solutions. The algorithms were introduced by Holland in 1975. Since then, they have received growing interest due to their ability to discover good solutions quickly for complex searching and optimization problems. The traditional GAs have been converted to multi-objective GAs to solve multi-objective optimization problems successfully. However, GAs require parameter tunings (such as population size, mutation probabilities, crossover probabilities, selection rates) in order to achieve the desirable solutions. The task of tuning GA parameters has been proven to be far from trivial due to the complex interactions among the parameters. It takes trial and error experiments to obtain the optimal GA parameter settings for an arbitrary real-world problem, Many researchers have been trying to understand the interdependencies of GA parameters in order to determine their optimal settings. The objective of this research is to develop the elitist non-dominated sorting GA (NSGA-II) for multi-objective optimization as a parameter-less multiobjective GA. The research then evaluates and discusses the performance of the parameter-less NSGA-II against the original NSGA-II with optimal parameter settings using the experimental result for a test problem borrowed from the literature.

Improved random selection strategy, self-adaptive crossover and mutation operators, population co-evolution algorithm are adopted in this paper to set up a new genetic algorithm(GA), guaranteeing the universality of population, the thoroughness of solution and the execution efficiency of algorithm in solving multi-process, process crossover, work-piece flow and other condition based scheduling problem of flexible manufacturing system(FMS). The problem of GA in solving FMS scheduling is summarized in this paper, reasons and countermeasures for GA's pre-maturing are studied, longitudinal and transverse comprehensive state-based correlative self-adaptive crossover and mutation operators are raised according to the analysis on GA to avoid local optimization and pre-maturity, the co-evolutionary GA is designed to solve the scheduling model for FMS, and the algorithm is validated for the scheduling of box-type parts for precise machine tools.

This paper studies the use of Genetic Algorithms (GA) in the design of Fuzzy Logic Controllers (FLC) and show how population size, probability of crossover and rate of mutation can effect the performance of the GA. The comparison of various parameters shows that GA is helpful in improving the performance of FLC. A fuzzy logic is fully defined by its membership function. What is the best to determine the membership function is the first question that has be tackled. Thus it is important to select the accurate membership functions but these methods possess one common weakness where conventional FLC use membership function generated by human operators. The membership function selection process is done with trial and error and it runs step by step which is too long in completing the problem. This research develops a system that may help users to determine the membership function of FLC using the technique of GA optimization for the fastest processing in completing the problems. The data collection is based on the simulation results and the results refer to the transient response specification is maximum overshoot. From the results presented, the system which we developed is very helpful to determine membership function and it is clear that the GA is very promising in improving the performance of the FLC to get more accurate in order to find the optimum result.

As a result of the emergence of many kinds of high-speed communication systems and increasing demand of distributed multimedia applications, efficient and effective support of quality of service (QoS) has become more and more essential. Many service models and mechanisms have been put forward by IETF to meeting QoS requirement, multicast service is a key one of them and is becoming a key requirement of computer network supposing multimedia application.Multicasting consists of concurrently sending the same information from a source to a subset of all possible destinations in a computer network. Multicast utilizes a tree delivery structure, on which data packets are duplicated only at fork nodes and are forwarded only once over each link. This approach makes multicast resource—efficient in delivering data to a group of member simultaneously and can scale well to support very large multicast group. This paper focuses on the algorithms to construct low cost multicast routing tree with QoS requirements.In this paper, we study the bandwidth, delay, delay jitter, and packet loss constrained least cost multicast routing problem which is known to be NP-complete, and present a adaptive genetic simulated annealing algorithm (AGASA) to solve the problem. The proposed genetic algorithm has the following characteristics: (1) Tree structure coding scheme, by which we can use any data structure of the tree to describe the chromosome structure, is an effective solution to the algorithm in the coding and decoding. (2) Genetic algorithm is improved, and simulated annealing algorithm is combined with. Simulated annealing algorithm has stronger local search capabilities, which avoids a partial optimum during the search process. (3) The crossover and mutation probability of adaptive genetic algorithm is improved. The selection of the crossover and mutation probability is the key to the behavior and performance of the algorithm, which has the direct impact on the convergence of the algorithm.The simulation results show that the algorithm has fast convergence and better performance. It is able to satisfy the changing network scale.

This research work aims at evaluating the possibilities of using artificial intelligence in dynamic resource management and optimization of software system performance. In today’s complex world of application usage, normal methods of resource management are unable to cater to these dynamic needs and fulfill its usage potential. In this work, an assessment of three mainstream AI techniques – reinforcement learning, neural network, and genetic algorithm – is performed based on performance indicators such as resource utilization and consumption, average response time, throughput, costs, prediction capability, stability, and time taken to converge. The results show that the neural networks have the best resource acquisition performance as well as response rates, while the reinforcement learning has the best cost management and flexibility rates. As it has been pointed out, genetic algorithms are quite useful in finding optimization solutions, however real-time responsiveness is lack. Thus, the results provide significant understating of how to choose the proper AI technique depending on the specific application needs which in turn will be useful for organizations willing to improve their resource management using AI-based solutions.