Data Mining - Concepts and Techniques.

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A Break in the Clouds: Towards a Cloud Definition

A Protocol Test Generation Procedure.

In this paper, an improved ant colony optimization (ICMPACO) algorithm based on the multi-population strategy, co-evolution mechanism, pheromone updating strategy, and pheromone diffusion mechanism is proposed to balance the convergence speed and solution diversity, and improve the optimization performance in solving the large-scale optimization problem. In the proposed ICMPACO algorithm, the optimization problem is divided into several sub-problems and the ants in the population are divided into elite ants and common ants in order to improve the convergence rate, and avoid to fall into the local optimum value. The pheromone updating strategy is used to improve optimization ability. The pheromone diffusion mechanism is used to make the pheromone released by ants at a certain point, which gradually affects a certain range of adjacent regions. The co-evolution mechanism is used to interchange information among different sub-populations in order to implement information sharing. In order to verify the optimization performance of the ICMPACO algorithm, the traveling salesmen problem (TSP) and the actual gate assignment problem are selected here. The experiment results show that the proposed ICMPACO algorithm can effectively obtain the best optimization value in solving TSP and effectively solve the gate assignment problem, obtain better assignment result, and it takes on better optimization ability and stability.

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An Improved Ant Colony Optimization Algorithm Based on Hybrid Strategies for Scheduling Problem

To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in ant colony optimization (ACO) algorithm in solving complex optimization problems, the chaotic optimization method, multi-population collaborative strategy and adaptive control parameters are introduced into the GA and ACO algorithm to propose a genetic and ant colony adaptive collaborative optimization (MGACACO) algorithm for solving complex optimization problems. The proposed MGACACO algorithm makes use of the exploration capability of GA and stochastic capability of ACO algorithm. In the proposed MGACACO algorithm, the multi-population strategy is used to realize the information exchange and cooperation among the various populations. The chaotic optimization method is used to overcome long search time, avoid falling into the local extremum and improve the search accuracy. The adaptive control parameters is used to make relatively uniform pheromone distribution, effectively solve the contradiction between expanding search and finding optimal solution. The collaborative strategy is used to dynamically balance the global ability and local search ability, and improve the convergence speed. Finally, various scale TSP are selected to verify the effectiveness of the proposed MGACACO algorithm. The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.

A novel collaborative optimization algorithm in solving complex optimization problems

Distributed permutation flow shop scheduling problem (DPFSP) is a newly proposed scheduling problem, which is a generalisation of classical permutation flowshop scheduling problem. Studies on algorithms for solving this problem are in the early stage. In this paper, we propose a new tabu algorithm for solving this problem, which exploits a novel tabu strategy. A method that swaps sub-sequences of jobs is presented to generate neighbourhood. Moreover, an enhanced local search method is proposed and also combined into the tabu algorithm. We also use the well-known benchmark of Taillard (extended to the distributed permutation flowshop problems) to test the algorithm. From the intensive experiments we carried out, we conclude that the proposed tabu algorithm outperforms all the existing algorithms including heuristics algorithms (i.e. NEH1, NEH2, VND(a) and VND(b)) and a hybrid genetic algorithm, so the best-known solutions for 472 instances are updated. Moreover, it is worth mentioning that the efficiency o...

An efficient tabu search algorithm for the distributed permutation flowshop scheduling problem

This paper presents a novel two-stage hybrid swarm intelligence optimization algorithm called GA---PSO---ACO algorithm that combines the evolution ideas of the genetic algorithms, particle swarm optimization and ant colony optimization based on the compensation for solving the traveling salesman problem. In the proposed hybrid algorithm, the whole process is divided into two stages. In the first stage, we make use of the randomicity, rapidity and wholeness of the genetic algorithms and particle swarm optimization to obtain a series of sub-optimal solutions (rough searching) to adjust the initial allocation of pheromone in the ACO. In the second stage, we make use of these advantages of the parallel, positive feedback and high accuracy of solution to implement solving of whole problem (detailed searching). To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems from TSPLIB are tested to demonstrate the potential of the proposed two-stage hybrid swarm intelligence optimization algorithm. The simulation examples demonstrate that the GA---PSO---ACO algorithm can greatly improve the computing efficiency for solving the TSP and outperforms the Tabu Search, genetic algorithms, particle swarm optimization, ant colony optimization, PS---ACO and other methods in solution quality. And the experimental results demonstrate that convergence is faster and better when the scale of TSP increases.

A novel two-stage hybrid swarm intelligence optimization algorithm and application

Manually inspecting bugs to determine their severity is often an enormous but essential software development task, especially when many participants generate a large number of bug reports in a crow...

Identify Severity Bug Report with Distribution Imbalance by CR-SMOTE and ELM

An adaptive large neighborhood search heuristic for dynamic vehicle routing problems

With the help of automated classification, severe bugs can be rapidly identified so that the latent damage to software projects can be minimized. However, bug report datasets commonly suffer from disproportionate number of category samples. When presented with the situation of class imbalance, most standard classification learning approaches fail to properly learn the distributive characteristics of the samples and tend to result in unfavorable performance to predict class label. In this case, imbalanced learning becomes critical to advance classification algorithms. In this paper, we propose an improved synthetic minority oversampling technique to avoid the degraded performance caused by class imbalance in bug report datasets. Moreover, to lessen the chance of occasionalities in random sampling process, we propose a repeated sampling technique to train different, but related classifiers. Finally, an ensemble algorithm based on Choquet fuzzy integral is employed to combine the wisdom of crowds and make better decisions. We conduct comprehensive experiments on several bug report datasets from real-world bug repositories. The results demonstrate that the proposed method boosts the classification performance across the classes of the data. Specifically, compared with various ensemble learning techniques, the Choquet fuzzy integral achieves outstanding results on integrating multiple random oversampling techniques.

Rong Chen

Papers

An efficient tabu search algorithm for the distributed permutation flowshop scheduling problem

A novel two-stage hybrid swarm intelligence optimization algorithm and application

Identify Severity Bug Report with Distribution Imbalance by CR-SMOTE and ELM

An adaptive large neighborhood search heuristic for dynamic vehicle routing problems

Fusion of Multi-RSMOTE With Fuzzy Integral to Classify Bug Reports With an Imbalanced Distribution