Yihui Jin

An Effective PSO-Based Memetic Algorithm for Flow Shop Scheduling

This paper proposes an effective particle swarm optimization (PSO)-based memetic algorithm (MA) for the permutation flow shop scheduling problem (PFSSP) with the objective to minimize the maximum completion time, which is a typical non-deterministic polynomial-time (NP) hard combinatorial optimization problem. In the proposed PSO-based MA (PSOMA), both PSO-based searching operators and some special local searching operators are designed to balance the exploration and exploitation abilities. In particular, the PSOMA applies the evolutionary searching mechanism of PSO, which is characterized by individual improvement, population cooperation, and competition to effectively perform exploration. On the other hand, the PSOMA utilizes several adaptive local searches to perform exploitation. First, to make PSO suitable for solving PFSSP, a ranked-order value rule based on random key representation is presented to convert the continuous position values of particles to job permutations. Second, to generate an initial swarm with certain quality and diversity, the famous Nawaz-Enscore-Ham (NEH) heuristic is incorporated into the initialization of population. Third, to balance the exploration and exploitation abilities, after the standard PSO-based searching operation, a new local search technique named NEH_1 insertion is probabilistically applied to some good particles selected by using a roulette wheel mechanism with a specified probability. Fourth, to enrich the searching behaviors and to avoid premature convergence, a simulated annealing (SA)-based local search with multiple different neighborhoods is designed and incorporated into the PSOMA. Meanwhile, an effective adaptive meta-Lamarckian learning strategy is employed to decide which neighborhood to be used in SA-based local search. Finally, to further enhance the exploitation ability, a pairwise-based local search is applied after the SA-based search. Simulation results based on benchmarks demonstrate the effectiveness of the PSOMA. Additionally, the effects of some parameters on optimization performances are also discussed

An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers

In this paper, an effective hybrid algorithm based on particle swarm optimization (HPSO) is proposed for permutation flow shop scheduling problem (PFSSP) with the limited buffers between consecutive machines to minimize the maximum completion time (i.e., makespan). First, a novel encoding scheme based on random key representation is developed, which converts the continuous position values of particles in PSO to job permutations. Second, an efficient population initialization based on the famous Nawaz-Enscore-Ham (NEH) heuristic is proposed to generate an initial population with certain quality and diversity. Third, a local search strategy based on the generalization of the block elimination properties, named block-based local search, is probabilistically applied to some good particles. Moreover, simulated annealing (SA) with multi-neighborhood guided by an adaptive meta-Lamarckian learning strategy is designed to prevent the premature convergence and concentrate computing effort on promising solutions. Simulation results and comparisons demonstrate the effectiveness of the proposed HPSO. Furthermore, the effects of some parameters are discussed.

An effective hybrid particle swarm optimization for batch scheduling of polypropylene processes

Abstract Short-term scheduling for batch processes which allocates a set of limited resources over time to manufacture one or more products plays a key role in batch processing systems of the enterprise for maintaining competitive position in fast changing market. This paper proposes an effective hybrid particle swarm optimization (HPSO) algorithm for polypropylene (PP) batch industries to minimize the maximum completion time, which is modeled as a complex generalized multi-stage flow shop scheduling problem with parallel units at each stage and different inventory storage policies. In HPSO, a novel encoding scheme based on random key representation, a new assignment scheme STPT (smallest starting processing time) by taking the different intermediate storage strategies into account, an effective local search based on the Nawaz–Enscore–Ham (NEH) heuristic, as well as a local search based on simulated annealing with an adaptive meta-Lamarckian learning strategy are proposed. Simulation results based on a set of random instances and comparisons with several adaptations of constructive methods and meta-heuristics demonstrate the effectiveness of the proposed HPSO.

Hybrid particle swarm optimization for flow shop scheduling with stochastic processing time

The stochastic flow shop scheduling with uncertain processing time is a typical NP-hard combinatorial optimization problem and represents an important area in production scheduling, which is difficult because of inaccurate objective estimation, huge search space, and multiple local minima. As a novel evolutionary technique, particle swarm optimization (PSO) has gained much attention and wide applications for both function and combinatorial problems, but there is no research on PSO for stochastic scheduling cases. In this paper, a class of PSO approach with simulated annealing (SA) and hypothesis test (HT), namely PSOSAHT is proposed for stochastic flow shop scheduling with uncertain processing time with respect to the makespan criterion (i.e. minimizing the maximum completion time). Simulation results demonstrate the feasibility, effectiveness and robustness of the proposed hybrid algorithm. Meanwhile, the effects of noise magnitude and number of evaluation on searching performances are also investigated.

A New Strategy of Integrated Control and On-line Optimization on High-purity Distillation Process

Abstract For high-purity distillation processes, it is difficult to achieve a good direct product quality control using traditional proportional-integral-differential (PID) control or multivariable predictive control technique due to some difficulties, such as long response time, many un-measurable disturbances, and the reliability and precision issues of product quality soft-sensors. In this paper, based on the first principle analysis and dynamic simulation of a distillation process, a new predictive control scheme is proposed by using the split ratio of distillate flow rate to that of bottoms as an essential controlled variable. Correspondingly, a new strategy with integrated control and on-line optimization is developed, which consists of model predictive control of the split ratio, surrogate model based on radial basis function neural network for optimization, and modified differential evolution optimization algorithm. With the strategy, the process achieves its steady state quickly, so more profit can be obtained. The proposed strategy has been successfully applied to a gas separation plant for more than three years, which shows that the strategy is feasible and effective.

Stable adaptive control of backlash nonlinear system with bounded disturbances

An adaptive control algorithm for backlash nonlinear systems with bounded disturbances is presented. Its closed-loop stability is proved, and the upper bound of the control error is given, even for nonminimum-phase systems. Simulation results show the effectiveness of the algorithm.<<ETX>>

Nonlinear adaptive predictive control based on orthogonal wavelet networks

A nonlinear adaptive predictive control strategy using an orthogonal wavelet network model is presented. Based on a set of orthogonal wavelet functions, a wavelet neural network performs a nonlinear mapping from the network input space to the wavelons output space in the hidden layer first. Its weight coefficients can be estimated by a linear least-square estimation algorithm. The excellent statistical properties of the weight parameters of the wavelet network also can be obtained. A single input single output (SISO) nonlinear adaptive predictive control strategy is implemented in the simulation of a CSTR process.

An Effective PSO-Based Memetic Algorithm for TSP

This paper proposes an effective Particle Swarm Optimization (PSO) based Memetic Algorithm (MA) for Traveling Salesman Problem (TSP) which is a typical NP-hard combinatorial optimization problem with strong engineering background. In the proposed PSO-based MA (PSOMA), a novel encoding scheme is developed, and an effective local search based on Simulated Annealing (SA) with adaptive meta-Lamarckian learning strategy is proposed and incorporated into PSO. Simulation results based on well-known benchmarks and comparisons with some existing algorithms demonstrate the effectiveness of the proposed hybrid algorithm for TSP.

A Dynamic Soft-sensing Method Based on Impulses Response Template and Parameter Estimation with Modified DE Optimization

Abstract As major experimental modeling methods, static soft-sensing methods have been widely used in modern chemical production process now. In fact, for the sampling frequency to output variable by laboratory analyzer off-line is rather low and uniform, the computed results, which gained from those methods or the existed major dynamic methods such as neural networks, are difficult to satisfy the requirements of dynamic control on-line. A dynamic soft-sensing method (DSSM) based on impulse response template (IRT) and parameter estimation using differential evolution (DE) optimization is presented in this paper. However, for a multi-variables system, learning of template parameters still takes large computational cost, and is not only slow in the convergence speed but also easy to be trapped into local optima so as to enlarge the modeling errors. To account for these problems, the original DE (ODE) is modified in the aspects of scaling factor and crossover rate, which could dynamically change with iterative loops. Subsequently, a complete implementation of the modified DE (MDE) is presented. Experiment based on hysys simulation of a primary tower system to build a three-inputs-single-output model is carried out, under various impulse response length and noise standard, and the final comparison results demonstrate the effectiveness and robustness of this method.

The application of wavelet neural networks to nonlinear predictive control

An identification and predictive control strategy for nonlinear processes based on orthogonal wavelet basis function networks is proposed. In this paper, a wavelet neural network with a linear least squares learning algorithm is developed for a process model. This can be used with nonlinear programming to implement nonlinear model predictive control strategy. Since simplified online optimization method has been developed, this control strategy is very easy to implement. Using the proposed identification and control strategy, a control system of bilinear process is simulated. It shows excellent performance superior to a standard PID controller for the nonlinear processes.