Short-term power generation scheduling rules for cascade hydropower stations based on hybrid algorithm

In job shop scheduling problems, the previously used nonpreemptive schedules are semiactive, active, and non-delay schedules, and they are initially designed for traditional scheduling performances and do not take energy efficiency into consideration. To fill the gap, this article presents a novel class of nonpreemptive schedules by investigating the special properties of job shop scheduling problems. Then, an energy-efficient generation procedure of the novel nonpreemptive schedules and the standby policy are proposed. An improved genetic algorithm is introduced with three superior features, including the elitist selection, the improved generation alternation model, and the operator decision mechanism. The Friedman test and the Holm multiple comparison test are conducted on experimental results to determine the appropriate parameter for the proposed genetic algorithm. Finally, the computation results and two-sample t-tests show that both energy-efficient generation procedure and standby policy help to reduce energy consumption without worsening production efficiency.

BackgroundProtein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm (GA) and tabu search (TS) algorithm, is developed to complete this task.ResultsIn order to develop an efficient optimization algorithm, several improved strategies are developed for the proposed genetic tabu search algorithm. The combined use of these strategies can improve the efficiency of the algorithm. In these strategies, tabu search introduced into the crossover and mutation operators can improve the local search capability, the adoption of variable population size strategy can maintain the diversity of the population, and the ranking selection strategy can improve the possibility of an individual with low energy value entering into next generation. Experiments are performed with Fibonacci sequences and real protein sequences. Experimental results show that the lowest energy obtained by the proposed GATS algorithm is lower than that obtained by previous methods.ConclusionsThe hybrid algorithm has the advantages from both genetic algorithm and tabu search algorithm. It makes use of the advantage of multiple search points in genetic algorithm, and can overcome poor hill-climbing capability in the conventional genetic algorithm by using the flexible memory functions of TS. Compared with some previous algorithms, GATS algorithm has better performance in global optimization and can predict 3D protein structure more effectively.

Genetic programming hyper-heuristic-based solution for dynamic energy-efficient scheduling of hybrid flow shop scheduling with machine breakdowns and random job arrivals

The wafer probing scheduling problem (WPSP) is a variation of the parallel-machine problem, which carries with the objective to minimize the total workload to enhance the utilization rate of machine capacity. However, to minimize the maximum completion time for the wafer probing scheduling problem (WPSP) is very important for equally utilizing the capacity of parallel-machine, while satisfying the requirements of product types, product family, sequence dependent setup time, product-type dependent processing time, due dates of jobs, and capacities of machines. To solve such a complicated problem efficiently and effectively, we adopt the hybrid genetic algorithm approach, which generates initial population with the insertion and savings algorithms for WPSP and provide a new crossover operator to generate better solutions. Considering the disturbance of due dates of jobs in scheduling, we design a new crossover which makes each machine keep the critical sub-schedule in the generation of offspring. The performance of the proposed hybrid genetic algorithm is evaluated by two real-world problems and the solution quality of the proposed approach is analyzed under five levels of mutation rates and four levels of number of generations. Computational results reveal that the hybrid genetic algorithm can efficiently solve the considered problem and the reduction of makespan is related to the mutation rates and the number of generations.

A hybrid algorithm (HA) integrated genetic algorithm (GA) with ant colony optimization (ACO) for solving Traveling Salesman Problems(TSP) was studied to get better optimization performance than each single algorithm, and complement merits each other and avoid each own demerits. The hybrid algorithm runs GA first and then ACO. A new strategy called GSA was proposed aiming at the key link in the HA that converts genetic solution from GA into information pheromone to distribute in ACO. GSA takes new matrix which is formed by the combination of the former 90% of individual from genetic solution and 10% of individual by random generation as the basis of transformation of pheromone value. The best combination of genetic operators in GA was also discussed. Several TSP were used as simulation tests to test genetic operators matching and optimization performance of HA. The results show that PMX crossover matched with IVM mutation in the GA is the best combination of genetic operators which is able to make GA improve the precision of optimal solution, and HA using the best combination operators and GSA strategy is successful and available to search for optimal solution in high efficiency and has good convergence.

This study is concerned about how to optimize the input sequence of product models in Mixed-Model Assembly Lines (MMALs) with limited intermediate buffers.Two objectives are considered simultaneously: minimizing the variation in parts usage and minimizing the makespan. The mathematical model is presented by incorporating the two objectives according to their relative importance weights. A hybrid algorithm (GASA), based on a genetic algorithm (GA) and a simulated annealing algorithm (SA), is proposed for solving the model. The performance of the GASA is compared with the GA over various test problems. The results show that, in terms of solutions’ quality, both the GASA and GA can find the same best solution for small-sized problems and the GASA performs better than the GA for mediate and large-sized problems. Moreover, the impact of buffer size on MMAL’s performance is investigated.

This paper proposed and implemented an genetic and weed invasion hybrid algorithm in Sparse Array Synthesis. The classical weed invasion algorithm has high requirements on the convergence parameters in the process of generating weeds from seeds. Inappropriate parameters can easily lead to local convergence of evolution. The mutation and crossover operations similar to the genetic algorithm were introduced into the classical weed invasion algorithm. The method improves convergence speed, avoids local convergence, and enhances the stability of the algorithm. The algorithm is used in practical application in the synthesis of linear sparsely distributed array pattern, and the predetermined index is obtained through calculation. At the same time, compared with the genetic algorithm and the classical weed invasion algorithm, the results show that the hybrid algorithm has certain advantages.

The traditional single intelligent algorithm has slow convergence speed in the frequency assignment, and the effect cannot meet the increasing frequency equipment in the naval battlefield. Based on the single intelligent frequency assignment algorithm and the frequency conflict analysis model between systems, this paper proposes three heuristic frequency assignment algorithms based on hybrid genetic algorithms, namely, Genetic Algorithm and Tabu Search (GATS), Hybrid Genetic Simulated Annealing Algorithm (HGSAA) and Genetic Algorithm-ant Colony Algorithm (GAA). The simulation results show that the hybrid algorithm can quickly converge to a better allocation result than the single intelligent algorithm in the frequency assignment problem, and the optimal value is better than the single intelligent algorithm. Among them, the convergence speed and optimal value of the GAA are the best of the three algorithms. Therefore, this algorithm can be applied to frequency assignment with more frequency-using equipment within a fixed range.

An efficient assignment and scheduling of tasks of a multiprocessor program is one of the key elements in the effective utilization of multiprocessor systems. This problem being hard to solve exactly, is the reason that so many heuristic methods for finding a suboptimal schedule exist (Hou et al., 1994; Tsuchiya et al., 1997). This paper addresses the problem of multiprocessor scheduling represented as a directed acyclic task graph (DAG) without communication costs to fully connected multiprocessors. We proposed an algorithm, called PGA/SPF (Priority-based Genetic Algorithm/Shortest Processor First schedule) where a priority-based genetic algorithm is improved with the introduction of some knowledge about the scheduling problem, which is represented by the use of genetic operators as crossover and mutation operators. And, a new mapping method, shortest processor first schedule, assigns the selected task to a processor that can minimize a task execution time efficiently. The proposed algorithms generate similar or even better solutions than the previous algorithms in terms of the completion times of the resulting schedules.

With the minimum amount of coal consumption, pollution emissions, the cost of power generation and carbon emissions, this paper analyzes the running status and the output status of the units and builds the multi-objective optimized model of low-carbon and energy-conservation generation dispatching in single phase. Then it takes this as the foundation to establish a dynamic multi-targeting optimized model of low-carbon power generation dispatching. This article lays a foundation for the study of the algorithm of the mixed programming of large-scale and multi-objective power generation dispatching.

A pattern synthesis method based on the genetic algorithm and convex algorithm is presented for featuring a minimum side-lobe level of the planar thinned antenna array. The genetic algorithm focuses on the selection of the planar array elements excitation to accomplish the array thinning. The convex optimization is applied to assign the weight coefficient of array elements for the lower side-lobe level. In this way, the hybrid algorithm ensures the global optimal solution and improves the search process efficiency. The computed results indicate that the average value of side lobe level for the thinned sub-array optimized by hybrid algorithm is lower to -27 dB. Meanwhile, the processing time for the whole elements of the array will be shortened.

This study aims to estimate the solute transport parameters in saturated porous media using a hybrid algorithm. In this study, the Physical Non-Equilibrium (PNE) model was used to describe the transport of solutes in porous media. A numerical solution for the PNE model is obtained using the Finite Volume Method (FVM) based on the Ttri-Diagonal Matrix Algorithm (TDMA). The developed program, written in Matlab, is capable to solve the advection-dispersion (ADE) and the PNE equations for the mobile -immobile (MIM)model with linear sorption isotherm. The Solute transport parameters, (immobile water content, mass transfer coefficient, and dispersion coefficient), are estimated using different algorithms such as the Levenberg-Marquardt algorithm (LM), genetic algorithm (GA), simulated annealing algorithm (SA). To overcome the limitations of deterministic optimization models which are rather unstable and divergent around a local minimum, a hybrid algorithm (GA+LM, SA+LM) permits to estimate of the solute transport parameters. Numerical solutions are verified using the experiments conducted by Semra (2003) which are about the transport of toluene through a column composed of impregnated Chromosorb grains at ambient temperature (20 °C) for three flow rates (1, 2 and 5ml/min). The results show that the hybrid algorithm (GA+LM, SA+LM) is more accurate than others (GA, SA, and LM). Comparing to the ADE model, The PNE with linear isotherm model gives a better description to the BeakThrough Curves (BTCs) with higher values of determination coefficient (R2) and lower values of Root Mean Square Error (RMSE). Also, the solute transport parameters tended to vary with the flow rate.

In this paper we propose a dynamic multi automated guided vehicle (AGV) scheduling method based on the scheduling properties of automated container terminal handling systems. In multi-AGV scheduling, the composition of AGV handling time and the precedence order of certain tasks are major constraints. Taking these into consideration, we design a genetic algorithm (GA) for a dynamic multi-AGV scheduling model to minimize completion time and standard deviation of handling time of quay cranes (QC), and validated the proposed model through numerical experiment. We expect this model to be significant for multi-agent scheduling of discrete production systems.

In order to study the prediction and optimization of borehole stability parameters in deepwater drilling based on genetic algorithm. First, a genetic hybrid algorithm based on pattern search is proposed. Then, based on the adaptive genetic algorithm, the evolutionary population is searched for patterns, which makes the hybrid algorithm not only has a strong global search ability but also improves the local optimization accuracy. Finally, the unit footage cost in the drilling process is taken as the objective function, and the algorithm is verified by taking the drilling in Karamay area as an example. The calculation results show that if the bit wear reaches 0.8-0.9 and then the bit is pulled out, the utilization rate of the bit can be increased, the design efficiency and accuracy can be improved, and the drilling cost can be reduced. The wear amount of the optimized bit is higher than that of the actual bit. Increasing the utilization rate of the bit can reduce the cost of drilling meters to a certain extent and improve the economic benefits of drilling. The objective function and constraint conditions for the optimization of drilling parameters are determined, and the algorithm is verified with the drilling data of Karamay Oilfield. The results show that the algorithm improves the stability and speed of iterative convergence and improves the reliability of data analysis results. Based on the regional three-dimensional formation rock parameter data volume, the optimization method can be used to optimize the drilling parameters before drilling and provide a basis for formulating the drilling design scheme.

Under the condition of power market, how to coordinate the medium and long-term trading plan with the power generation dispatching is a new problem. Considering the medium and long-term trading electricity, this paper proposes a daily generation planning method based on standard load curve. The power plants are divided into priority power plants with balanced provincial regulation, market-oriented power plants without regulation capacity and marketoriented power plants with regulation capacity; The first type of power plant curve adopts the predicted value, the second type of power plant determines the power generation curve according to the unit of different standards in the flood and dry seasons, and obtains the standard load curve from the load curve of the whole network, and the third type of power plant adopts the standard load curve to determine the power generation curve. The results show that this method can effectively connect the medium and long-term trading electricity with the daily power generation plan.