Bin-Yih Liao

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.

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 ladder diffusion algorithm using ant colony optimization for wireless sensor networks

In this paper, an algorithm based on ladder diffusion and ACO [5,6] is proposed to solve the power consumption and transmission routing problems in wireless sensor networks. The proposed ladder diffusion algorithm is employed to route paths for data relay and transmission in wireless sensor networks, reducing both power consumption and processing time to build the routing table and simultaneously avoiding the generation of circle routes. Moreover, to ensure the safety and reliability of data transmission, our algorithm provides backup routes to avoid wasted power and processing time when rebuilding the routing table in case part of sensor nodes are missing. According to the experimental results, the proposed algorithm not only reduces power consumption by 52.36% but also increases data forwarding efficiency by 61.11% as compared to the directed diffusion algorithm. This decrease is because the algorithm properly assigns the transmission routes to balance the load on every sensor node.

Fault Node Recovery Algorithm for a Wireless Sensor Network

This paper proposes a fault node recovery algorithm to enhance the lifetime of a wireless sensor network when some of the sensor nodes shut down. The algorithm is based on the grade diffusion algorithm combined with the genetic algorithm. The algorithm can result in fewer replacements of sensor nodes and more reused routing paths. In our simulation, the proposed algorithm increases the number of active nodes up to 8.7 times, reduces the rate of data loss by approximately 98.8%, and reduces the rate of energy consumption by approximately 31.1%.

Tabu search based multi-watermarks embedding algorithm with multiple description coding

Digital watermarking is a useful solution for digital rights management systems, and it has been a popular research topic in the last decade. Most watermarking related literature focuses on how to resist deliberate attacks by applying benchmarks to watermarked media that assess the effectiveness of the watermarking algorithm. Only a few papers have concentrated on the error-resilient transmission of watermarked media. In this paper, we propose an innovative algorithm for vector quantization (VQ) based image watermarking, which is suitable for error-resilient transmission over noisy channels. By incorporating watermarking with multiple description coding (MDC), the scheme we propose to embed multiple watermarks can effectively overcome channel impairments while retaining the capability for copyright and ownership protection. In addition, we employ an optimization technique, called tabu search, to optimize both the watermarked image quality and the robustness of the extracted watermarks. We have obtained promising simulation results that demonstrate the utility and practicality of our algorithm.

An Optimized Approach on Applying Genetic Algorithm to Adaptive Cluster Validity Index

The partitioning or clustering method is an important research branch in data mining area, and it partitions the dataset into an arbitrary number k of clusters according to the correlation attribute of all elements of the dataset. Most datasets have the original clusters number, which is estimated with cluster validity index. But most current cluster validity index methods give the error estimation for most real datasets. In order to solve this problem, this paper applies the optimization technology of genetic algorithm to the new adaptive cluster validity index, which is called the gene index (GI). The algorithm applies genetic algorithm to adjust the weight value of the valuation function of adaptive cluster validity index to train an optimal cluster validity index. The algorithm is tested with many real datasets, and results show the proposed algorithm can give higher performance and accurately estimate the original cluster number of real datasets compared with the current cluster validity index methods.

A Transmission Power Optimization with a Minimum Node Degree for Energy-Efficient Wireless Sensor Networks with Full-Reachability

Transmission power optimization is the most significant factor in prolonging the lifetime and maintaining the connection quality of wireless sensor networks. Un-optimized transmission power of nodes either interferes with or fails to link neighboring nodes. The optimization of transmission power depends on the expected node degree and node distribution. In this study, an optimization approach to an energy-efficient and full reachability wireless sensor network is proposed. In the proposed approach, an adjustment model of the transmission range with a minimum node degree is proposed that focuses on topology control and optimization of the transmission range according to node degree and node density. The model adjusts the tradeoff between energy efficiency and full reachability to obtain an ideal transmission range. In addition, connectivity and reachability are used as performance indices to evaluate the connection quality of a network. The two indices are compared to demonstrate the practicability of framework through simulation results. Furthermore, the relationship between the indices under the conditions of various node degrees is analyzed to generalize the characteristics of node densities. The research results on the reliability and feasibility of the proposed approach will benefit the future real deployments.

An extensible particles swarm optimization for energy-effective cluster management of underwater sensor networks

Acoustic communication networks in underwater environment are the key technology to explore global ocean. There are major challenges including (1) lack of stable and sufficient power supply, (2) disable of radio frequency signal and (3) no communication protocol designed for underwater environment. Thus, acoustic so far is the only media suitable to operate for underwater communication. In this paper, we study the technology of underwater acoustic communication to support underwater sensor networks. Toward the energy-effective goal, a cluster-based sensor network is assumed. The energy-dissipation of sensor nodes is optimized by biological computing such as Particle Swarm Optimization (PSO). The objective function of sensor node clustering is formulized to constraint on the network coverage and energy dissipation. The problem of dual-objective optimization is solved by the proposed extensible PSO (ePSO). ePSOis an innovation from traditional PSO. The major innovation is to offer an extensible particle structure and to enable more flexible search for optimal solutions in space. The experimental results demonstrate that the proposed ePSO effectively and fast tackles multi-objective optimization problem. The application of ePSO on underwater acoustic communication systems shows the feasibility in real world.

A Reduce Identical Event Transmission Algorithm for Wireless Sensor Networks

This paper proposed a Reduce Identical Event Transmission Algorithm (RIET). The algorithm can decide that which sensor nodes could send the event to sink node when sensor nodes sense a same even. Moreover, other nodes can save power because they didn’t send the same event. In our simulation, the RIET algorithm can enhance sensor nodes’ life time about 12.9 times and saving power consumption about 52.43 % than tradition algorithms.