Chaotic Ant Swarm Optimization Research Articles

A chaotic secure communication method based on chaos systems partial series parameter estimation

We proposed a chaotic secure communication method, namely the partial series of the chaos system for parameter estimation and the other series for secure communications. Parameter estimation could be obtained from the partial series of chaos system with the chaotic ant swarm optimization algorithm, to understand all of the information of the chaos system. In the process of parameter estimation, the introduced parameter space and ant swarm space transformed into each other through space transformation function. Numerical simulation validated the feasibility of the chaos system partial series parameter estimation and the chaotic secure communication method.

A modified chaotic ant swarm optimization algorithm

This paper proposed a modified chaotic ant swarm optimization under an effective termination condition. Numerical calculation verifies the validity of the modified algorithm. Simulation of the algorithm indicates that the algorithm can have a high-accuracy optimal solution. Through multiple chaotic searches, the modified algorithm gradually approaches the optimal solution which can be used to solve complex continuous domain optimization problem.

Dynamic search space squeezing strategy based intelligent algorithm solutions to economic dispatch with multiple fuels

This paper presents a new approach to the solution of optimal power generation for economic dispatch (ED) using improved particle swarm optimization (IPSO) technique. In this paper an improved PSO technique is suggested that deals with equality and inequality constraints in ED problems. A constraint treatment mechanism called dynamic search space squeezing strategy is devised to accelerate the optimization process and simultaneously the dynamic process inherent in the conventional PSO algorithm is preserved. The application and statistical performance of various intelligent algorithms such as differential evolution (DE), particle swarm optimization (PSO) and improved particle swarm optimization (IPSO) are considered on economic dispatch problems with non-smooth cost functions considering valve point effects and multiple fuel options. To determine the efficiency and effectiveness of various intelligent algorithms, three experiments are conducted considering only multiple fuel options, considering both valve-point and multiple fuel options and also taking into account the valve point loadings, ramp rate limits and prohibited operating zones. The simulation results reveal that the proposed IPSO has provided the better solution with a very high probability to demonstrate its robustness over other intelligent techniques such as DE, PSO and improved genetic algorithm with multiplier updating (IGA_MU), ant colony optimization (ACO), artificial bee colony algorithm (ABC), hybrid swarm intelligent based harmony search algorithm (HHS) and fuzzy adaptive chaotic ant swarm optimization (FCASO). The proposed IPSO ensures convergence within least execution time and provides quality solutions as compared to earlier reported best results.

Optimum design of fractional order PIλDμ controller for AVR system using chaotic ant swarm

Fractional-order PID (FOPID) controller is a generalization of standard PID controller using fractional calculus. Compared to PID controller, the tuning of FOPID is more complex and remains a challenge problem. This paper focuses on the design of FOPID controller using chaotic ant swarm (CAS) optimization method. The tuning of FOPID controller is formulated as a nonlinear optimization problem, in which the objective function is composed of overshoot, steady-state error, raising time and settling time. CAS algorithm, a newly developed evolutionary algorithm inspired by the chaotic behavior of individual ant and the self-organization of ant swarm, is used as the optimizer to search the best parameters of FOPID controller. The designed CAS-FOPID controller is applied to an automatic regulator voltage (AVR) system. Numerous numerical simulations and comparisons with other FOPID/PID controllers show that the CAS-FOPID controller can not only ensure good control performance with respect to reference input but also improve the system robustness with respect to model uncertainties.

A hybrid FCASO-SQP method for solving the economic dispatch problems with valve-point effects

In this paper, a hybrid method integrating the fuzzy adaptive chaotic ant swarm optimization (FCASO) algorithm and the sequential quadratic programming (SQP) techniques, named FCASO-SQP method, is presented for solving the economic dispatch (ED) problems in power systems. The FCASO algorithm is the main optimizer in the hybrid method and the SQP technique is used to fine tune its results to improve the solution. The FCASO algorithm introduces a fuzzy system to dynamically tune the characteristic parameters ψd and ri of chaotic ant swarm optimization (CASO). The proposed method was applied to three different cases of power systems with three units, thirteen units and forty units, and the simulation results demonstrate its applicability and effectiveness to solve the ED problems with valve-point effect.

A fuzzy adaptive chaotic ant swarm optimization for economic dispatch

This paper developed a fuzzy adaptive chaotic ant swarm optimization (FCASO) algorithm for solving the economic dispatch (ED) problems of thermal generators in power systems. The FCASO algorithm introduces a fuzzy system to dynamically tune the characteristic parameters ψ d and r i of chaotic swarm optimization (CASO). The proposed method was applied to two cases of power systems. The simulation results demonstrate the applicability and effectiveness of the proposed algorithm to the practical ED problem.

Chaotic ant swarm optimization with passive congregation

Chaotic ant swarm optimization (CASO) is a powerful chaos search algorithm for optimization problems, but it is often easy to be premature convergence. To overcome the weakness, this paper presents a CASO with passive congregation (CASOPC). Passive congregation is one type of biological information sharing mechanisms that allow animals to aggregate into groups and help to enhance the exploitation of animals. By introducing passive congregation strategy into the CASO, a modified evolution equation based on the CASO is proposed in the CASOPC. The modified evolution equation cannot only employ the parallel search of all ants and the well exploration ability of the CASO, but also stress and control the exploitation by passive congregation coefficient c in the stage of evolution. Due to linearly increasing c in the CASOPC, the exploration and exploitation ability of ants are well balanced so that premature convergence can be avoided and good performance can be achieved. In order to estimate the capability of the CASOPC, it is tested with a set of 5 benchmark functions with 30 dimensions and compared to the CASO. Experimental results indicate that the CASOPC improves the search performance on the benchmark functions significantly.

Chaotic ant swarm optimization for fuzzy-based tuning of power system stabilizer

In this paper, chaotic ant swarm optimization (CASO) is utilized to tune the parameters of both single-input and dual-input power system stabilizers (PSSs). This algorithm explores the chaotic and self-organization behavior of ants in the foraging process. A novel concept, like craziness, is introduced in the CASO to achieve improved performance of the algorithm. While comparing CASO with either particle swarm optimization or genetic algorithm, it is revealed that CASO is more effective than the others in finding the optimal transient performance of a PSS and automatic voltage regulator equipped single-machine-infinite-bus system. Conventional PSS (CPSS) and the three dual-input IEEE PSSs (PSS2B, PSS3B, and PSS4B) are optimally tuned to obtain the optimal transient performances. It is revealed that the transient performance of dual-input PSS is better than single-input PSS. It is, further, explored that among dual-input PSSs, PSS3B offers superior transient performance. Takagi Sugeno fuzzy logic (SFL) based approach is adopted for on-line, off-nominal operating conditions. On real time measurements of system operating conditions, SFL adaptively and very fast yields on-line, off-nominal optimal stabilizer variables.

Application of chaotic ant swarm optimization in electric load forecasting

Support vector regression (SVR) had revealed strong potential in accurate electric load forecasting, particularly by employing effective evolutionary algorithms to determine suitable values of its three parameters. Based on previous research results, however, these employed evolutionary algorithms themselves have several drawbacks, such as converging prematurely, reaching slowly the global optimal solution, and trapping into a local optimum. This investigation presents an SVR-based electric load forecasting model that applied a novel algorithm, namely chaotic ant swarm optimization (CAS), to improve the forecasting performance by searching its suitable parameters combination. The proposed CAS combines with the chaotic behavior of single ant and self-organization behavior of ant colony in the foraging process to overcome premature local optimum. The empirical results indicate that the SVR model with CAS (SVRCAS) results in better forecasting performance than the other alternative methods, namely SVRCPSO (SVR with chaotic PSO), SVRCGA (SVR with chaotic GA), regression model, and ANN model.

A multi-objective chaotic ant swarm optimization for environmental/economic dispatch

Since the environmental issues caused by the pollutant emissions from fossil-fueled power plants are concerned, it is necessary to develop the conventional economic dispatch (ED) into environmental/economic dispatch (EED) which considers both economic and environmental issues. This paper developed a multi-objective chaotic ant swarm optimization (MOCASO) method for solving the multi-objective EED problems of thermal generators in power systems. The proposed MOCASO method was applied to three test power systems. Simulation results demonstrated that the MOCASO method can obtain feasible and effective solutions and it is a promising alternative approach for solving the EED problems in practical power systems.

Parameter estimation of dynamical systems via a chaotic ant swarm

Through the construction of suitable objective function, the parameter estimation of the dynamical system could be converted to the problem of parameter optimization. Based on the chaotic ant swarm optimization approach, we investigate the problem of parameter optimization for the dynamical systems in the presence of noise. We systematically analyze the basic relationships among the complexity of objective function, the length of time series, and the performance of the searching algorithm. Furthermore, we consider the effect of measurable additive noise on the objective function. Numerical simulations are also provided to show the effectiveness and feasibility of the proposed methods.

Hybrid chaotic ant swarm optimization

Chaotic ant swarm optimization (CASO) is a powerful chaos search algorithm that is used to find the global optimum solution in search space. However, the CASO algorithm has some disadvantages, such as lower solution precision and longer computational time, when solving complex optimization problems. To resolve these problems, an improved CASO, called hybrid chaotic swarm optimization (HCASO), is proposed in this paper. The new algorithm introduces preselection operator and discrete recombination operator into the CASO; meanwhile it replaces the best position found by own and its neighbors’ ants with the best position found by preselection operator and discrete recombination operator in evolution equation. Through testing five benchmark functions with large dimensionality, the experimental results show the new method enhances the solution accuracy and stability greatly, as well as reduces the computational time and computer memory significantly when compared to the CASO. In addition, we observe the results can become better with swarm size increasing from the sensitivity study to swarm size. And we gain some relations between problem dimensions and swam size according to scalability study.

Computation of multiple global optima through chaotic ant swarm

This paper proposes a technique aiming to compute all global minimizers, while avoiding local minimizers, through chaotic ant swarm (CAS) optimization method. This technique incorporates the recently proposed deflection and repulsion techniques to alleviate local minimizers. Those approaches can be used in combination with CAS to detect all global minimizers effectively. The performance of the algorithm is illustrated on test problems of global optimization. Experimental results indicate the effectiveness of the proposed algorithm.

Chaotic ant swarm optimization to economic dispatch

This paper developed a novel algorithm named chaotic ant swarm optimization (CASO) for solving the economic dispatch (ED) problems of thermal generators in power systems. This algorithm combines with the chaotic and self-organization behavior of ants in the foraging process. It includes both effects of chaotic dynamics and swarm-based search. The algorithm was employed to solve the ED problems of thermal generators. The proposed method was applied to three examples of power systems. Simulation results demonstrated that the method can obtain feasible and effective solutions, and it is a promising alternative approach for solving the ED problems in practical power systems.

AN OPTIMIZATION METHOD INSPIRED BY "CHAOTIC" ANT BEHAVIOR

Inspired by the behavior of the ants in nature, we propose an optimization method, which combines the chaotic behavior of individual ants with the intelligent optimization action of an ant colony. Our method includes both effects of chaotic dynamics and swarm-based search. It is a deterministic process different from the conventional ant algorithm. The nonlinear dynamics of the proposed method are analyzed, and we show how the algorithm, called chaotic ant swarm optimization, can be applied to numerical optimization problems with encouraging results.