Rongbin Qi

Dynamic Optimization of Industrial Processes With Nonuniform Discretization-Based Control Vector Parameterization

This paper proposes a novel scheme of nonuniform discretizetion-based control vector parameterization (ndCVP, for short) for dynamic optimization problems (DOPs) of industrial processes. In our ndCVP scheme, the time span is partitioned into a multitude of uneven intervals, and incremental time parameters are encoded, along with the control parameters, into the individual to be optimized. Our coding method can avoid handling complex ordinal constraints. It is proved that ndCVP is a natural generalization of uniform discretization-based control vector parameterization (udCVP). By integrating ndCVP into hybrid gradient particle swarm optimization (HGPSO), a new optimization method, named ndCVP-HGPSO for short, is formed. By application in four classic DOPs, simulation results show that ndCVP-HGPSO is able to achieve similar or even better performances with a small number of control intervals; while the computational overheads are acceptable. Furthermore, ndCVP and udCVP are compared in terms of two situations: given the same number of control intervals and given the same number of optimization variables. The results show that ndCVP can achieve better performance in most cases.

Self-adaptive differential evolution algorithm with α-constrained-domination principle for constrained multi-objective optimization

Real-world problems are inherently constrained optimization problems often with multiple conflicting objectives. To solve such constrained multi-objective problems effectively, in this paper, we put forward a new approach which integrates self-adaptive differential evolution algorithm with α-constrained-domination principle, named SADE-αCD. In SADE-αCD, the trial vector generation strategies and the DE parameters are gradually self-adjusted adaptively based on the knowledge learnt from the previous searches in generating improved solutions. Furthermore, by incorporating domination principle into α-constrained method, α-constrained-domination principle is proposed to handle constraints in multi-objective problems. The advantageous performance of SADE-αCD is validated by comparisons with non-dominated sorting genetic algorithm-II, a representative of state-of-the-art in multi-objective evolutionary algorithms, and constrained multi-objective differential evolution, over fourteen test problems and four well-known constrained multi-objective engineering design problems. The performance indicators show that SADE-αCD is an effective approach to solving constrained multi-objective problems, which is basically enabled by the integration of self-adaptive strategies and α-constrained-domination principle.

Chaos-Genetic Algorithm for Multiobjective Optimization

Chaos-genetic algorithm (CGA) combining local chaotic search and nondominated sorting genetic algorithm for multiobjective optimization is proposed. The method is composed of two stages. The wide search with nondominated sorting genetic algorithm (NSGA-II) is performed at the first searching stage, then the local search with chaotic mutation is performed at the second stage. Moreover, we cancel the limitation of the number of the elitism at each generation and improve the original clustering method. We apply the coverage measure and spread measure to evaluate the performance of the two methods, and obtain more satisfactory results with CGA than that with NSGA-II

Multiobjective evolutionary algorithm based on the Pareto Archive and individual migration

A multiobjective evolutionary algorithm based on the parallel evolution of multiple single objective populations and Pareto archive population is proposed. For each single objective population, single objective evolutionary algorithm is applied to optimize separately each of multiobjective functions, where individuals generated by tournament selection from the union of single objective and Pareto archive population form the single objective population of next generation. At each evolving iteration, based on the concept of Pareto dominance, a finite-sized Pareto archive population is iteratively updated and trimmed by a new crowded-comparison operation. Especially, individuals in Pareto archive population also join evolutionary operations to increase the converging speed and improve quality of nondominated solutions. Simulations manifest that the proposed method can realize the search from multiple directions to obtain the nondominated solutions scattered more uniformly over the Pareto frontier with better convergence metric compared to well-known NSGA-II algorithm. Individuals migrating from Pareto archive population by tournament selection is also proved to have the advantage in improving the converging speed and converging precision.

IPSO: An immune based PSO supervised learning system for incremental learning

PSO has been proved as an effective supervised learning system in recent years, but its not an effective method for incremental learning problems. Aiming at the incremental learning target for classification, a hybrid algorithm of Particle Swarm Optimization (PSO) and Artificial Immune System (AIS) called Immune based PSO (IPSO) is presented in this paper. IPSO inherits the incremental learning ability of AIS. In IPSO, training data is presented to the algorithm one by one, and the training proceed is a one-shot incremental algorithm. Besides, the swarm does not converge to a single solution; instead, each particle is a part of the classifier, and the whole memory population is taken as the integral classifier to the problem. Compared the results of standard PSO and IPSO in several benchmark problems from the UCI data sets, we found that IPSO achieved a better classification accuracy than standard PSO in most cases. It is also competitive with some of the algorithms most commonly used for classification.

Minimum time dynamic optimization using double-layer optimization algorithm

A double-layer optimization algorithm (DLOA) was proposed to solve the minimum time dynamic optimization problem. The first step of DLOA was to discrete time region and control region. The inner optimization is to construct optimal control problem with free final states. Differential evolution algorithm is used to find the optimal solution in given terminal time, then the optimization results was compared with the threshold set. In the outer, DLOA calculated the time range of next iteration according to the inner calculation. When applied to typical minimum time dynamic optimization problem, DLOA demonstrated a competitive optimal searching ability and more accurate optimization results. DLOA could solve the optimization problem with local optimum and applied to models without gradient information.

Fuzzy-Based Hybrid Control for Nonlinear Multivariable System

Hybrid controller based on fuzzy logic system is designed for a class of nonlinear multivariable systems. The nonlinear systems involve plant uncertainties and external disturbances. The controller is based on a combination of the Hinfin tracking theory, fuzzy logic system and supervisor control algorithm (SCA). The robust stability of the control system is proofed in the paper. When apply the hybrid controller, all the states and signals of the closed-loop system are bounded and Hinfin tracking performance is guaranteed. Consequently, the control performance of the system is greatly improved. The hybrid controller can be applied to more general nonlinear system involving a large class of uncertainties and variations. At last the simulation result is given.

Stabilizing the Unstable Periodic Orbits via Improved Delayed Feedback Control for Discrete Chaotic Systems

Delayed feedback control (DFC) is a powerful method for stabilizing unstable periodic orbits embodied in chaotic attractors, but it has an odd number limitation, that is DFC can never stabilize a target unstable periodic orbit of a chaotic system if the transition matrix of the linearized system around the unstable periodic orbit has an odd number of real eigenvalues greater than unity. In this paper, we proposed periodic delayed feedback control method with nonlinear estimation for stabilizing unstable periodic orbits of chaotic discrete-time systems. This method can overcome the inherent weak point of the DFC, and avoid the difficulty of the stabilizing analysis of controlling high-periodic orbits. Periodic feedback gain is derived easily by applying pole-assignment theory