Steady-state Reproduction Research Articles

Performance of genetic algorithms with different selection operators for solving short-term optimized reservoir scheduling problem

The tournament operator genetic algorithm (TGA) often shows poor convergence and easily gets trapped in a local optimum when solving optimized reservoir scheduling problems. The selection operation is the most important operation determining an algorithm’s convergence; therefore, this study proposes a proportional reproduction selection-based operator genetic algorithm (RGA) and a steady-state reproduction selection-based operator genetic algorithm (SGA) as alternatives to TGA. This study used TGA, RGA, and SGA to solve the maximum power generation model for the Gezhouba hydropower station, the largest runoff hydropower station in the world. Then, by using the maximum hydropower station output under a given typical runoff scenario as the optimization criterion, this study evaluated the optimized solution performance of the GA using different selection operators. The results show that TGA, SGA, and RGA can be applied to solve the short-term reservoir scheduling model. As the number of iterations increases, the hydropower station output optimized by these three GAs increases. Based on the maximum power generated by the Gezhouba hydropower station, SGA and RGA provide better results than TGA, and between SGA and RGA, the former provides better results.

Well balancing of the SWE schemes for moving-water steady flows

In this work, the exact reproduction of a moving-water steady flow via the numerical solution of the one-dimensional shallow water equations is studied.A new scheme based on a modified version of the HLLEM approximate Riemann solver (Dumbser and Balsara (2016) [18]) that exactly preserves the total head and the discharge in the simulation of smooth steady flows and that correctly dissipates mechanical energy in the presence of hydraulic jumps is presented. This model is compared with a selected set of schemes from the literature, including models that exactly preserve quiescent flows and models that exactly preserve moving-water steady flows.The comparison highlights the strengths and weaknesses of the different approaches. In particular, the results show that the increase in accuracy in the steady state reproduction is counterbalanced by a reduced robustness and numerical efficiency of the models. Some solutions to reduce these drawbacks, at the cost of increased algorithm complexity, are presented.

Study on high-speed cutting parameters optimization of AlMn1Cu based on neural network and genetic algorithm

In this article, the cutting parameters optimization method for aluminum alloy AlMn1Cu in high-speed milling was studied in order to properly select the high-speed cutting parameters. First, a back propagation neural network model for predicting surface roughness of AlMn1Cu was proposed. The prediction model can improve the prediction accuracy and well work out the higher-order nonlinear relationship between surface roughness and cutting parameters. Second, considering the constraints of technical requirements on surface roughness, a mathematical model for optimizing cutting parameters based on the Bayesian neural network prediction model of surface roughness was established so as to obtain the maximum machining efficiency. The genetic algorithm adopting the homogeneous design to initialize population as well as steady-state reproduction without duplicates was also presented. The application indicates that the algorithm can effectively avoid precocity, strengthen global optimization, and increase the calculation efficiency. Finally, a case was presented on the application of the proposed cutting parameters optimization algorithm to optimize the cutting parameters.

Dynamic Route Guidance Using Improved Genetic Algorithms

This paper presents an improved genetic algorithm (IGA) for dynamic route guidance algorithm. The proposed IGA design a vicinity crossover technique and a greedy backward mutation technique to increase the population diversity and strengthen local search ability. The steady-state reproduction is introduced to protect the optimized genetic individuals. Furthermore the junction delay is introduced to the fitness function. The simulation results show the effectiveness of the proposed algorithm.

A mathematical model and a genetic algorithm for two-sided assembly line balancing

A two-sided assembly line is a type of production line where tasks are performed in parallel at both sides of the line. The line is often found in producing large products such as trucks and buses. This paper presents a mathematical model and a genetic algorithm (GA) for two-sided assembly line balancing (two-ALB). The mathematical model can be used as a foundation for further practical development in the design of two-sided assembly lines. In the GA, we adopt the strategy of localized evolution and steady-state reproduction to promote population diversity and search efficiency. When designing the GA components, including encoding and decoding schemes, procedures of forming the initial population, and genetic operators, we take account of the features specific to two-ALB. Through computational experiments, the performance of the proposed GA is compared with that of a heuristic and an existing GA with various problem instances. The experimental results show that the proposed GA outperforms the heuristic and the compared GA.

Evolving locomotion gaits for quadruped walking robots

PurposeThis paper presents an evolutionary algorithm (EA) for Sony legged robots to learn good walking behaviours with little or no interaction with the designers. Once the learning method is put into place, the module can learn through its interaction with the real world.Design/methodology/approachAn EA for developing locomotion gaits of quadruped walking robots is presented in this paper. It is based on a hybrid approach that changes the probability of genetic operators in respect to the performance of the operator's offspring.FindingsThe mutating and combination behaviours of the genetic algorithms allow the process to develop a useful behaviour over time. The resulting gait from this training proved to be a better solution than the non‐interference training for movements over all types of surfaces, pointing to a local optima being discovered in the non‐environmental interference situation.Research limitations/implicationsThe behaviour of these algorithms is stochastic so that they may potentially present different solutions in different runs of the same algorithm. The mechanism described here has several features that should be noted. It allows rapid parameterisation of operator probabilities across the range of potential genetic algorithms and operator set. It is tailored to a steady state reproduction scheme. It would not be literally applicable to problems with noisy evaluation functions.Originality/valueProvides novel application of genetic algorithms to a potentially practical application area.

A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling

This paper addresses the integrated problem of process planning and scheduling in job shop flexible manufacturing systems. Due to production flexibility, it is possible to generate many feasible process plans for each job. The two functions of process planning and scheduling are tightly interwoven with each other. The optimality of scheduling depends on the result of process planning. The integration of process planning and scheduling is therefore important for an efficient utilization of manufacturing resources. In this paper, a new method using an artificial intelligent search technique, called symbiotic evolutionary algorithm, is presented to handle the two functions at the same time. For the performance improvement of the algorithm, it is important to enhance population diversity and search efficiency. We adopt the strategies of localized interactions, steady-state reproduction, and random symbiotic partner selection. Efficient genetic representations and operator schemes are also considered. While designing the schemes, we take into account the features specific to each of process planning and scheduling problems. The performance of the proposed algorithm is compared with those of a traditional hierarchical approach and an existing cooperative coevolutionary algorithm. The experimental results show that the proposed algorithm outperforms the compared algorithms. Scope and purpose The traditional job shop scheduling literature generally assumed that there is a single feasible process plan for each job. This implies that no flexibility in the process plan is considered. Today's many manufacturing systems are becoming increasingly flexible in processing operations. In such systems, most jobs may have a large number of feasible process plans. Although process planning and job shop scheduling are highly related with each other, many prior researches considered them separately or sequentially. Process planning and scheduling involve the assignment of manufacturing resources to production tasks. For an efficient use of manufacturing resources, process planning needs to be integrated with scheduling. This paper presents a new method that can efficiently solve the problem of integrating process planning with scheduling in flexible job shops. Artificial coevolution is becoming a prospective approach to solving such a problem that it consists of multiple sub-problems interrelated with each other. A coevolutionary algorithm, called symbiotic evolutionary algorithm, is employed as the underlying framework. Several strategies and genetic operators are provided to improve the performance of the proposed algorithm. Extensive computational experiments are carried out to verify the efficacy of our algorithm.

Balancing and sequencing mixed-model U-lines with a co-evolutionary algorithm

A mixed-model production line is such a line where a variety of product models are produced. In U-lines used in the just-in-time production system, the strategy of mixing product models is often employed to provide various types of products to customers on time. Line balancing and model sequencing problems are important for an efficient use of mixed-model U-lines. Although the two problems are tightly interrelated with each other, prior research has considered them separately or sequentially. In this paper, a new approach using an artificial intelligence search technique, called co-evolutionary algorithm, is proposed to solve the two problems at the same time. To promote population diversity and search efficiency in the algorithm, we adopt strategies of localized evolution and steady-state reproduction, and develop methods of selecting environmental individuals and evaluating fitness. Efficient genetic representations and operator schemes are also provided. When designing the schemes, we take into account the features specific to the problems. The experimental results demonstrate that the proposed algorithm outperforms existing approaches.

Inversion of seismoacoustic data using genetic algorithms and a posteriori probability distributions

The goal of many underwater acoustic modeling problems is to find the physical parameters of the environment. With the increase in computer power and the development of advanced numerical models it is now feasible to carry out multiparameter inversion. The inversion is posed as an optimization problem, which is solved by a directed Monte Carlo search using genetic algorithms. The genetic algorithm presented in this paper is formulated by steady-state reproduction without duplicates. For the selection of ‘‘parents’’ the object function is scaled according to a Boltzmann distribution with a ‘‘temperature’’ equal to the fitness of one of the members in the population. The inversion would be incomplete if not followed by an analysis of the uncertainties of the result. When using genetic algorithms the response from many environmental parameter sets has to be computed in order to estimate the solution. The many samples of the models are used to estimate the a posteriori probabilities of the model parameters. Thus the uniqueness and uncertainty of the model parameters are assessed. Inversion methods are generally formulated independently of forward modeling routines. Here they are applied to the inversion of geoacoustic parameters (P and S velocities and layer thickness) in the bottom using a horizontally stratified environment. The examples show that for synthetic data it is feasible to carry out an inversion for bottom parameters using genetic algorithms.