Pei-Wei Tsai

ENHANCED ARTIFICIAL BEE COLONY OPTIMIZATION

The complete mitochondrial DNA D‐loop structure of pigeon (Columba livia) was established in this study. A strategy of amplifying three partial fragments of the D‐loop and then combing the three fragments to cover the full length of the D‐loop was adopted. Ten samples from pigeons were collected and were successfully amplified and sequenced. Repetitive sequences of a VNTR and an STR were both observed at the 3′‐end of D‐loop region. DNA sequence data revealed polymorphic sequences including indels, SNP, VNTR and STR within the D‐loop. The size of the D‐loop ranged from 1310 to 1327 bp from the initiation site of D‐loop to the site immediately upstream of the repeat sequences depending upon the number of insertions or deletions. Each sample could be distinguished based on four genotyping procedures; being indels, SNPs, VNTRs and STRs. The polymorphic nature of the D‐loop can be a valuable method for maternal identification and genetic linkage of pigeon in particular forensic science investigations.

Bat Algorithm Inspired Algorithm for Solving Numerical Optimization Problems

Inspired by Bat Algorithm, a novel algorithm, which is called Evolved Bat Algorithm (EBA), for solving the numerical optimization problem is proposed based on the framework of the original bat algorithm. By reanalyzing the behavior of bats and considering the general characteristics of whole species of bat, we redefine the corresponding operation to the bats’ behaviors. EBA is a new method in the branch of swarm intelligence for solving numerical optimization problems. In order to analyze the improvement on the accuracy of finding the near best solution and the reduction in the computational cost, three well-known and commonly used test functions in the field of swarm intelligence for testing the accuracy and the performance of the algorithm, are used in the experiments. The experimental results indicate that our proposed method improves at least 99.42% on the accuracy of finding the near best solution and reduces 6.07% in average, simultaneously, on the computational time than the original bat algorithm.

Cat swarm optimization

In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).

Enhanced parallel cat swarm optimization based on the Taguchi method

In this paper, we present an enhanced parallel cat swarm optimization (EPCSO) method for solving numerical optimization problems. The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers. The Taguchi method is widely used in the industry for optimizing the product and the process conditions. By adopting the Taguchi method into the tracing mode process of the PCSO method, we propose the EPCSO method with better accuracy and less computational time. In this paper, five test functions are used to evaluate the accuracy of the proposed EPCSO method. The experimental results show that the proposed EPCSO method gets higher accuracies than the existing PSO-based methods and requires less computational time than the PCSO method. We also apply the proposed method to solve the aircraft schedule recovery problem. The experimental results show that the proposed EPCSO method can provide the optimum recovered aircraft schedule in a very short time. The proposed EPCSO method gets the same recovery schedule having the same total delay time, the same delayed flight numbers and the same number of long delay flights as the Liu, Chen, and Chou method (2009). The optimal solutions can be found by the proposed EPCSO method in a very short time.

Parallel Cat Swarm Optimization

We investigate a parallel structure of cat swarm optimization (CSO) in this paper, and we call it parallel cat swarm optimization (PCSO). In the experiments, we compare particle swarm optimization (PSO) with CSO and PCSO. The experimental results indicate that both CSO and PCSO perform well. Moreover, PCSO is an effective scheme to improve the convergent speed of cat swarm optimization in case the population size is small and the whole iteration is less.

A Novel Optimization Approach: Bacterial-GA Foraging

In this paper, we proposed a novel optimization model, which combines bacterial foraging with genetic algorithm. Though these two well-known optimization algorithms have their own good points, they also have their own drawbacks respectively. In our work, a combined evolutional model, bacterial-GA foraging, is proposed. Via applying this new model, experimental results indicate that the new combined model performs much better performance than applying any of these two algorithms singly.

Parallelized Artificial Bee Colony with Ripple-communication Strategy

In this paper, a communication strategy for the parallelized Artificial Bee Colony (ABC) optimization is proposed for solving numerical optimization problems. The artificial agents are split into several independent subpopulations based on the original structure of the ABC, and the proposed communication strategy provides the information flow for the agents to communicate in different subpopulations. Three benchmark functions are used to test the behavior of convergence, the accuracy, and the speed of the proposed method. According to the experimental result, the proposed communicational strategy increases the accuracy of the ABC on finding the near best solution.

A balanced power consumption algorithm based on enhanced parallel cat swarm optimization for wireless sensor network

The wireless sensor network (WSN) is composed of a set of sensor nodes. It is deemed suitable for deploying with large-scale in the environment for variety of applications. Recent advances in WSN have led to many new protocols specifically for reducing the power consumption of sensor nodes. A new scheme for predetermining the optimized routing path is proposed based on the enhanced parallel cat swarm optimization (EPCSO) in this paper. This is the first leading precedent that the EPCSO is employed to provide the routing scheme for the WSN. The experimental result indicates that the EPCSO is capable of generating a set of the predetermined paths and of smelting the balanced path for every sensor node to forward the interested packages. In addition, a scheme for deploying the sensor nodes based on their payload and the distance to the sink node is presented to extend the life cycle of the WSN. A simulation is given and the results obtained by the EPCSO are compared with the AODV, the LD method based on ACO, and the LD method based on CSO. The simulation results indicate that our proposed method reduces more than 35% power consumption on average.

A novel criterion for nonlinear time-delay systems using LMI fuzzy Lyapunov method

We merge Lyapunov criteria with LMI to evaluate the stability of the system.The H infinity control performance is achieved by the controller design of PDC.Converting the stability problem into system parameter matrices.EBA is employed to find the feasible solutions insuring the system is stable.The stability is used to construct the fitness function of EBA. This study presents a kind of fuzzy robustness design for nonlinear time-delay systems based on the fuzzy Lyapunov method, which is defined in terms of fuzzy blending quadratic Lyapunov functions. The basic idea of the proposed approach is to construct a fuzzy controller for nonlinear dynamic systems with disturbances in which the delay-independent robust stability criterion is derived in terms of the fuzzy Lyapunov method. Based on the robustness design and parallel distributed compensation (PDC) scheme, the problems of modeling errors between nonlinear dynamic systems and Takagi-Sugeno (T-S) fuzzy models are solved. Furthermore, the presented delay-independent condition is transformed into linear matrix inequalities (LMIs) so that the fuzzy state feedback gain and common solutions are numerically feasible with swarm intelligence algorithms. The proposed method is illustrated on a nonlinear inverted pendulum system and the simulation results show that the robustness controller cannot only stabilize the nonlinear inverted pendulum system, but has the robustness against external disturbance.

A Hybrid Krill-ANFIS Model for Wind Speed Forecasting

Finding an alternative renewable energy source instead of using traditional energy such as electricity or gas is an important research trend and challenge. This paper presents a new hybrid algorithm that uses Krill Herd (KH) optimization algorithm and Adaptive Neuro-Fuzzy Inference System (ANFIS) to be able to fit for wind speed forecasting, which is an essential step to generate wind power. ANFIS’s parameters are optimized using KH. The proposed model called (Krill-ANFIS). This model is compared with three models basic ANFIS, PSO-ANFIS, and GA-ANFIS. Krill-ANFIS proved that it can be used as an efficient predictor for the wind speed as well as it can achieve high results and performance measures of root mean square error (RMSE), Coefficient of determination \(R^{2}\) and average absolute percent relative error (AAPRE).