Yucheng Kao

A new ant colony optimization algorithm for the multidimensional Knapsack problem

The paper proposes a new ant colony optimization (ACO) approach, called binary ant system (BAS), to multidimensional Knapsack problem (MKP). Different from other ACO-based algorithms applied to MKP, BAS uses a pheromone laying method specially designed for the binary solution structure, and allows the generation of infeasible solutions in the solution construction procedure. A problem specific repair operator is incorporated to repair the infeasible solutions generated in every iteration. Pheromone update rule is designed in such a way that pheromone on the paths can be directly regarded as selecting probability. To avoid premature convergence, the pheromone re-initialization and different pheromone intensification strategy depending on the convergence status of the algorithm are incorporated. Experimental results show the advantages of BAS over other ACO-based approaches for the benchmark problems selected from OR library.

Data aggregation in wireless sensor networks using ant colony algorithm

Data aggregation is important in energy constraint wireless sensor networks which exploits correlated sensing data and aggregates at the intermediate nodes to reduce the number of messages exchanged network. This paper considers the problem of constructing data aggregation tree in a wireless sensor network for a group of source nodes to send sensory data to a single sink node. The ant colony system provides a natural and intrinsic way of exploring search space in determining data aggregation. Moreover, we propose an ant colony algorithm for data aggregation in wireless sensor networks. Every ant will explore all possible paths from the source node to the sink node. The data aggregation tree is constructed by the accumulated pheromone. Simulations have shown that our algorithm can reduce significant energy costs.

A sensor deployment approach using glowworm swarm optimization algorithm in wireless sensor networks

A wireless sensor network is composed of a large number of sensor nodes that are densely deployed in a sensing environment. The effectiveness of the wireless sensor networks depends to a large extent on the coverage provided by the sensor deployment scheme. In this paper, we present a sensor deployment scheme based on glowworm swarm optimization (GSO) to enhance the coverage after an initial random deployment of the sensors. Each sensor node is considered as individual glowworms emitting a luminant substance called luciferin and the intensity of the luciferin is dependent on the distance between the sensor node and its neighboring sensors. A sensor node is attracted towards its neighbors having lower intensity of luciferin and decides to move towards one of them. In this way, the coverage of the sensing field is maximized as the sensor nodes tend to move towards the region having lower sensor density. Simulation results show that our GSO-based sensor deployment approach can provide high coverage with limited movement of the sensor nodes.

An ACO-based clustering algorithm

Data clustering is one of important research topics of data mining. In this paper, we propose a new clustering algorithm based on ant colony optimization, called Ant Colony Optimization for Clustering (ACOC). At the core of the algorithm we use both the accumulated pheromone and the heuristic information, the distances between data objects and cluster centers of ants, to guide artificial ants to group data objects into proper clusters. This allows the algorithm to perform the clustering process more effectively and efficiently. Due to the nature of stochastic and population-based search, the ACOC can overcome the drawbacks of traditional clustering methods that easily converge to local optima. Experimental results show that the ACOC can find relatively good solutions.

An unified group technology implementation using the backpropagation learning rule of neural networks

Abstract Two engineering problems in implementing Group Technology are part family formation and part classification. Regardless of the approach adopted for the formation and classification, a critical problem is how to maintain consistency. The consistency problem can be addressed most effectively if the formation and classification is a single procedure rather than two separate procedures. A feedforward neural network using the Backpropagation learning rule is adopted to automatically generate part families during the part classification process. The spontaneous generalization capability of the neural network is utilized in classifying the parts into the families and creating new families if necessary. A heuristic algorithm using the neural network is described with an illustrative example.

Ant colony optimization based sensor deployment protocol for wireless sensor networks

Research highlights? We consider the problem of sensor deployment to achieve complete coverage of the service region and maximize the lifetime of the network. ? We model the deployment problem as the multiple knapsack problem. ? Based on ACO algorithm, we proposed a deployment scheme to prolong the network lifetime, while ensuring complete coverage of the service region. Sensor deployment is one of the most important issues in wireless sensor networks, because an efficient deployment scheme can reduce the deployment cost and enhance the detection capability of the wireless sensor networks. In addition, it can enhance the quality of monitoring in wireless sensor networks by increasing the coverage area. Ant colony optimization (ACO) algorithm provides a natural and intrinsic way of exploration of search space for multiple knapsack problem (MKP). In this work, we consider the problem of sensor deployment to achieve complete coverage of the service region and maximize the lifetime of the network. We model the deployment problem as the multiple knapsack problem. Based on ACO algorithm, we proposed a deployment scheme to prolong the network lifetime, while ensuring complete coverage of the service region. The simulations show that our algorithm can prolong the lifetime of the network.

Combining K-means and particle swarm optimization for dynamic data clustering problems

This paper presents a new dynamic data clustering algorithm based on K-means and Combinatorial Particle Swarm Optimization, called KCPSO. Unlike the traditional K-means method, KCPSO does not need a specific number of clusters given before performing the clustering process and is able to find the optimal number of clusters during the clustering process. In each iteration of KCPSO, a discrete PSO is used to optimize the number of clusters with which the K-means is used to find the best clustering result. KCPSO has been developed into a software system and evaluated by testing some datasets. Encouraging results show that KCPSO is an effective algorithm for solving dynamic clustering problems.

A Hybrid Algorithm Based on ACO and PSO for Capacitated Vehicle Routing Problems

The vehicle routing problemVRPis a well-known combinatorial optimization problem. It has been studied for several decades because finding effective vehicle routes is an important issue of logistic management. This paper proposes a new hybrid algorithm based on two main swarm intelligenceSIapproaches, ant colony optimizationACOand particle swarm optimization � PSO� , for solving capacitated vehicle routing problemsCVRPs� . In the proposed algorithm, each artificial ant, like a particle in PSO, is allowed to memorize the best solution ever found. After solution construction, only elite ants can update pheromone according to their own best-so-far solutions. Moreover, a pheromone disturbance method is embedded into the ACO framework to overcome the problem of pheromone stagnation. Two sets of benchmark problems were selected to test the performance of the proposed algorithm. The computational results show that the proposed algorithm performs well in comparison with existing swarm intelligence approaches.

A PSO-based approach to cell formation problems with alternative process routings

Group technology (GT) has been extensively applied to cellular manufacturing system (CMS) design for decades due to many benefits such as decreased number of part movements among cells and increased machine utilisation in cells. This paper considers cell formation problems with alternative process routings and proposes a discrete particle swarm optimisation (PSO) approach to minimise the number of exceptional parts outside machine cells. The approach contains two main steps: machine partition and part-routing assignment. Through inheritance and random search, the proposed algorithm can effectively partition machines into different cells with consideration of multiple part process routings. The computational results are compared with those obtained by using simulated annealing (SA)-based and tabu search (TS)-based algorithms. Experimental results demonstrate that the proposed algorithm can find equal or fewer exceptional elements than existing algorithms for most of the test problems selected from the literature. Moreover, the proposed algorithm is further tailed to incorporate various production factors in order to extend its applicability. Four sample cases are tested and the results suggest that the algorithm is capable of solving more practical cell formation problems.

An Ant Colony Algorithm for Data Aggregation in Wireless Sensor Networks

This paper considers the problem of constructing data aggregation tree in a wireless sensor network for a group of source nodes to send sensory data to a single sink node. Our goal is to minimize the number of non-source nodes in the tree to save energies. In this paper, we propose an ant colony algorithm for data aggregation in wireless sensor networks. Every ant will explore some paths from source node to sink node. The data aggregation tree will be constructed by the accumulated pheromone. The simulations have shown that our algorithm can deduce significant energy cost.