PID control for chaotic synchronization using particle swarm optimization

In this paper, we attempt to use the proportional-integral-derivative (PID) controller to achieve the chaos synchronization for delayed discrete chaotic systems. Three PID control gains can be optimally determined by means of using a novel optimization algorithm, called the particle swarm optimization (PSO). The algorithm is motivated from the organism behavior of fish schooling and bird flocking, and involves the social psychology principles in socio-cognition human agents and evolutionary computations. It has a good numerical convergence for solving optimization problem. To show the validity of the PSO-based PID control for chaos synchronization, several cases with different initial populations are considered and some simulation results are shown.     

LAHS: A novel harmony search algorithm based on learning automata

This study presents a learning automata-based harmony search (LAHS) for unconstrained optimization of continuous problems. The harmony search (HS) algorithm performance strongly depends on the fine tuning of its parameters, including the harmony consideration rate (HMCR), pitch adjustment rate (PAR) and bandwidth (bw). Inspired by the spur-in-time responses in the musical improvisation process, learning capabilities are employed in the HS to select these parameters based on spontaneous reactions. An extensive numerical investigation is conducted on several well-known test functions, and the results are compared with the HS algorithm and its prominent variants, including the improved harmony search (IHS), global-best harmony search (GHS) and self-adaptive global-best harmony search (SGHS). The numerical results indicate that the LAHS is more efficient in finding optimum solutions and outperforms the existing HS algorithm variants.     

An improved artificial bee colony algorithm for directing orbits of chaotic systems

Artificial bee colony algorithm (ABC) is a relatively new optimization technique which has been shown to be competitive to other population-based algorithms. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. To address this concerning issue, we propose an improved ABC (IABC) by using a modified search strategy to generate a new food source in order that the exploration and exploitation can be well balanced and satisfactory optimization performances can be achieved. In addition, to enhance the global convergence, when producing the initial population, both opposition-based learning method and chaotic maps are employed. In this paper, the proposed algorithm is applied to control and synchronization of discrete chaotic systems which can be formulated as both multimodal numerical optimization problems with high dimension. Numerical simulation and comparisons with some typical existing algorithms demonstrate the effectiveness and robustness of the proposed approach.     

Model reference adaptive synchronization of T–S fuzzy discrete chaotic systems using output tracking control

This paper presents a model reference adaptive control approach for the synchronization of a discrete-time chaotic systems using output tracking control. The reference model system is chosen using the output of master system and Takagi–Sugeno (T–S) fuzzy model is employed to represent the discrete-time chaotic slave system. Design the control input so that the controlled slave system achieves asymptotic synchronization with the reference system given that two systems start from different initial conditions, different parameters and/or different type of model. Using a gradient algorithm, the ideal controller gains which can stabilize the error equation are estimated. Simulation examples of two cases are given to demonstrate the validity of our proposed adaptive method.     

Adaptive projective synchronization for a class of switched chaotic systems

This paper addresses the problem of projective synchronization of chaotic systems and switched chaotic systems by adaptive control methods. First, a necessary and sufficient condition is proposed to show how many state variables can realize projective synchronization under a linear feedback controller for the chaotic systems. Then, accordingly, a new algorithm is given to select all state variables that can realize projective synchronization. Furthermore, according to the results of the projective synchronization of chaotic systems, the problem of projective synchronization of the switched chaotic systems comprised by the unified chaotic systems is investigated, and an adaptive global linear feedback controller with only one input channel is designed, which can realize the projective synchronization under the arbitrary switching law. It is worth mentioning that the proposed method can also realize complete synchronization of the switched chaotic systems. Finally, the numerical simulation results verify the correctness and effectiveness of the proposed method.     

Chaotic harmony search algorithms

Harmony Search (HS) is one of the newest and the easiest to code music inspired heuristics for optimization problems. Like the use of chaos in adjusting note parameters such as pitch, dynamic, rhythm, duration, tempo, instrument selection, attack time, etc. in real music and in sound synthesis and timbre construction, this paper proposes new HS algorithms that use chaotic maps for parameter adaptation in order to improve the convergence characteristics and to prevent the HS to get stuck on local solutions. This has been done by using of chaotic number generators each time a random number is needed by the classical HS algorithm. Seven new chaotic HS algorithms have been proposed and different chaotic maps have been analyzed in the benchmark functions. It has been detected that coupling emergent results in different areas, like those of HS and complex dynamics, can improve the quality of results in some optimization problems. It has been also shown that, some of the proposed methods have somewhat increased the solution quality, that is in some cases they improved the global searching capability by escaping the local solutions.     

Two improved harmony search algorithms for solving engineering optimization problems

This paper describes two new harmony search (HS) meta-heuristic algorithms for engineering optimization problems with continuous design variables. The key difference between these algorithms and traditional (HS) method is in the way of adjusting bandwidth (bw). bw is very important factor for the high efficiency of the harmony search algorithms and can be potentially useful in adjusting convergence rate of algorithms to optimal solution. First algorithm, proposed harmony search (PHS), introduces a new definition of bandwidth (bw). Second algorithm, improving proposed harmony search (IPHS) employs to enhance accuracy and convergence rate of PHS algorithm. In IPHS, non-uniform mutation operation is introduced which is combination of Yang bandwidth and PHS bandwidth. Various engineering optimization problems, including mathematical function minimization problems and structural engineering optimization problems, are presented to demonstrate the effectiveness and robustness of these algorithms. In all cases, the solutions obtained using IPHS are in agreement or better than those obtained from other methods.     

Synchronization and anti-synchronization of chaotic oscillators under input saturation

This paper addresses the design of simple state feedback controllers for synchronization and anti-synchronization of chaotic oscillators under input saturation and disturbance. By employing sector condition, linear matrix inequality (LMI)-based sufficient conditions are derived to design (global or local) controllers for chaos synchronization. The proposed local synchronization strategy guarantees a region of stability in terms of difference between states of the master–slave systems. This region of stability can be enlarged by means of an LMI-based optimization algorithm, through which asymptotic synchronization of chaotic oscillators can be ensured for a large difference in their initial conditions. Further, a novel LMI-based robust control strategy is developed, for local synchronization of input-constrained chaotic oscillators, by providing an upper bound on synchronization error in terms of disturbance and initial conditions of chaotic systems. Moreover, the proposed robust state feedback control methodology is modified to provide an inaugural treatment for robust anti-synchronization of chaotic systems under input saturation and disturbance. The results of the proposed methodologies are verified through numerical simulations for synchronization and anti-synchronization of the master–slave chaotic Chua’s circuits under input saturation.     

Synchronization of the unified chaotic systems via active control

This paper investigates the synchronization of coupled unified chaotic systems via active control. The synchronization is given in the slave–master scheme and the controller ensures that the states of the controlled chaotic slave system exponentially synchronize with the state of the master system. Numerical simulations are provided for illustration and verification of the proposed method.     

Global optimization using a synchronization of multiple search Points autonomously driven by a chaotic dynamic model

In the present paper, we propose a new multipoint type global optimization model using a chaotic dynamic model and a synchronization phenomenon in nonlinear dynamic systems for a continuously differentiable optimization problem. We first improve the Discrete Gradient Chaos Model (DGCM), which drives each search point’s autonomous movement, based on theoretical analysis. We then derive a new coupling structure called PD type coupling in order to obtain stable synchronization of all search points with the chaotic dynamic model in a discrete time system. Finally, we propose a new multipoint type global optimization model, in which each search point moves autonomously by improved DGCM and their trajectories are synchronized to elite search points by the PD type coupling model. The proposed model properly achieves diversification and intensification, which are reported to be important strategies for global optimization in the Meta-heuristics research field. Through application to proper benchmark problems [Liang et al. Novel composition test functions for numerical global optimization. In: Proceedings of Swarm Intelligence Symposium, 2005 (SIS 2005), pp. 68–75 (2005); Liang et al. Nat. Comput. 5(1), 83–96, 2006] (in which the drawbacks of typical benchmark problems are improved) with 100 or 1000 variables, we confirm that the proposed model is more effective than other gradient-based methods.