Intelligent optimization techniques for mammogram image analysis through bilateral subtraction

With the continuous development of mobile robot technology, its application fields are becoming increasingly widespread, and path planning is one of the most important topics in the field of mobile robot research. This paper focused on the study of the path planning problem for mobile robots in a complex environment based on the ant colony optimization (ACO) algorithm. In order to solve the problems of local optimum, susceptibility to deadlocks, and low search efficiency in the traditional ACO algorithm, a novel parallel ACO (PACO) algorithm was proposed. The algorithm constructed a rank-based pheromone updating method to balance exploration space and convergence speed and introduced a hybrid strategy of continuing to work and killing directly to address the problem of deadlocks. Furthermore, in order to efficiently realize the path planning in complex environments, the algorithm first found a better location for decomposing the original problem into two subproblems and then solved them using a parallel programming method-single program multiple data (SPMD)-in MATLAB. In different grid map environments, simulation experiments were carried out. The experimental results showed that on grid maps with scales of 20 $ \times $ 20, 30 $ \times $ 30, and 40 $ \times $ 40 compared to nonparallel ACO algorithms, the proposed PACO algorithm had less loss of solution accuracy but reduced the average total time by 50.71, 46.83 and 46.03%, respectively, demonstrating good solution performance.

Ant colony optimization(ACO) provides an effective way to solve combinatorial optimization problem. However, with the complexity of the problem increasing, the ACO algorithm needs considerable computational time and resources to improve the good quality of solution, and this rarely satisfies the requirement of real-time computing in M&S (Modeling and Simulation) area. Parallel implementation of ACO can reduce the computational time obviously for the large scale combinatorial optimization problem, and much of the previous work in this field focuses on parallel implementation using MPI which is executed on clusters. Meanwhile, great emphasis is placed on multi-core computing technology with the development of multi-processor architecture and multi-core architecture. A new parallel ant colony optimization (PACO) algorithm is proposed, which applies two kinds of typical multi-core computing technologies, the well-known OpenMP and the recently introduced TBB (Threading Building Blocks) library by Intel Corporation, to solve target assignment problem(TAP). Effectiveness and efficiency of proposed algorithm is validated by studying the convergence speed, problem size scalability and thread size scalability of it.

In this work, we introduce a new parallel ant colony optimization algorithm based on an ant metaphor and the crossover operator from genetic algorithms.The performance of the proposed model is evaluated usingwell-known numerical test problems and then it is applied to train recurrent neural networks to identify linear and nonlinear dynamic plants. The simulation results are compared with results using other algorithms.

In any organization and business, efficient scheduling cause increased efficiency, reducing the time required to complete jobs and increasing an organization’s profitability in a competitive environment. Also, the flow-shop scheduling problem is a vital type of scheduling problem with many real-world applications. Flow-shop scheduling has numerous exciting applications in various manufacturing and industrial domains. During the past eras, the growing interests in the arrangement of flow shops with diverse objective functions (for example, minimizing the makespan and flow-time) were observed. The permutation flow-shop is formulated as mixed-integer programming, and it is an NP-Hard problem. Therefore, in this paper, a novel method is provided to decrease the makespan and completion time. Since parallel algorithms use some computing elements to accelerate the search and present a new exploration pattern that is frequently suitable to enhance the quality of the results, in this research, a parallel ant colony optimization algorithm is employed to solve the mentioned problem. The Matlab simulation setting in Net Beans IDE 8.0.2 and Java to simulate the introduced method is applied. According to the obtained results, the suggested procedure has more efficiency than the previous methods. The Matlab simulator outcomes have indicated that the average response time has been improved compared to the PSO-SA and HBC algorithms. Also, the makespan is improved in comparison to GA and MOACSA.

Ant Colony Optimization is computationally expensive when it comes to complex problems. The Jacket toolbox allows implementation of MATLAB programs in Graphics Processing Unit (GPU). This paper presents and implements a parallel MAX-MIN Ant System (MMAS) based on a GPU+CPU hardware platform under the MATLAB environment with Jacket toolbox to solve Traveling Salesman Problem (TSP). The key idea is to let all ants share only one pseudorandom number matrix, one pheromone matrix, one taboo matrix, and one probability matrix. We also use a new selection approach based on those matrices, named AIR (All-In-Roulette). The main contribution of this paper is the description of how to design parallel MMAS based on those ideas and the comparison to the relevant sequential version. The computational results show that our parallel algorithm is much more efficient than the sequential version.

Solving the path planning problem of a UAV is a challenging issue especially if there are too many checkpoints to visit. Mainly, the brute force approach is used to find the shortest path in the mission area, which requires too many times to find a solution. Therefore, evolutionary algorithms and swarm intelligence techniques are used to find a feasible solution in an acceptable time. In this study, path planning problem of a UAV is solved by using a highly parallelized Ant Colony Optimization (ACO) algorithm on CUDA platform. The UAV path is constructed for disseminating keys and collecting data from a Wireless Sensor Network, which is previously defined. Due to its simplicity and effectiveness, ACO is selected as a path planning algorithm. However, ACO is not satisfactory if the mission area becomes large and there are an excessive number of checkpoints and/or additional constraints. In order to increase the performance, some parallelization techniques must be used in high performance computing platforms. GPU architecture has emerged as a powerful and low cost architecture for enabling impressive speedups for scientific calculations. Therefore, the parallel structure is constructed on CUDA architecture. The experimental results are compared with the CPU performance of the serial algorithm, and they clearly show that the proposed approach have a great potential for acceleration of ACO and allow to solve many complex tasks such as UAV path planning problem. We also present the execution results with different parameter values to expose the results for the researchers.

Recently, GPGPU (General Purpose computation on Graphics Processing Units) has become popular with great success, especially in scientific fields such as fluid dynamics, image processing, and visualization using particle methods. In this paper, we discuss how the GPGPU is used in implementations of parallel ant colony optimization (ACO) for fast solution of quadratic assignment problems (QAPs). As for the ACO, we use the cunning ant system (cAS) which is one of the most promising ACO algorithms.

Ant colony optimization (ACO) can be used to solve complex optimization problems in engineering, economic management and military strategy. Most of these are NP hard problems, which are difficult to solve with traditional methods. An improved parallel ACO algorithm based on pattern learning is proposed in this paper. It extracts parameters automatically to reduce solution space and enhance calculation efficiency. Various parameters in the algorithm are analyzed, and a refining strategy is formed according to ACO’s characteristics. The parallel ACO algorithm is carried out under the MIC/CPU architecture, and it can significantly enhance performance.

Advances in computer technologies and internet have accumulated data at an exceptional speed. Majority of data available is in textual form and might contain billions of significant observations and thousands of features. In the era of Big data, text categorization is the primary step for handling and organizing textual data. Mining of knowledge from unstructured data is a more challenging task and proper representation of unstructured data will significantly improve the performance of the knowledge extraction process. Bag of words is a basic text representation technique, but has not been much successful due to the properties synonymy and polysemy of the English language. These problems could be reduced when documents are represented as a bag of phrases. Finding the best subset of features for a problem in a large domain becomes intractable and many such problems have been proved to be NP-Hard. Optimization algorithms are designed to approach such NP-Hard problems to find nearly optimal solutions. This chapter discusses about using MapReduce programming model for statistical feature selection when documents are represented as a bag of syntactic phrases and designing a parallel ant colony optimization algorithm for feature selection.

A distribution network plays an extremely important role in the safe and efficient operation of a power grid. As the core part of a power grid’s operation, a distribution network will have a significant impact on the safety and reliability of residential electricity consumption. it is necessary to actively plan and modify the distribution network’s structure in the power grid, improve the quality of the distribution network, and optimize the planning of the distribution network, so that the network can be fully utilized to meet the needs of electricity consumption. In this paper, a distribution network grid planning algorithm based on the reliability of electricity consumption was completed using ant colony algorithm. For the distribution network structure planning of dual power sources, the parallel ant colony algorithm was used to prove that the premise of parallelism is the interactive process of ant colonies, and the dual power distribution network structure model is established based on the principle of the lowest cost. The artificial ants in the algorithm were compared with real ants in nature, and the basic steps and working principle of the ant colony optimization algorithm was studied with the help of the travelling salesman problem (TSP). Then, the limitations of the ant colony algorithm were analyzed, and an improvement strategy was proposed by using python for digital simulation. The results demonstrated the reliability of model-building and algorithm improvement.

In this paper, a concise method is proposed to locate the nipple position on a mammogram image. Mammogram registration is an important preprocessing technique, which can help in finding asymmetrical regions in left or the corresponding right breast. In particular, correct nipple position is the crucial key point of mammogram registration since it is still the most consistent and stable landmark on a mammogram. This work first presents an algorithm of maximum height of the breast border (MHBB) and then proposes two novel approaches, local spatial-maximum mean intensity (MMI) and local maximum zero-crossing (MZC) based on the second-order derivative, finally a combined process depending on the MMI and MZC is obtained. The 213 mammogram images from MIAS and 200 ones from DDSM database are tested for estimating the proposed method. Consequently, the mean Euclidean distance (MED) between the nipple position detected and the ground truth identified by the radiologist is 0.63 cm, within 1 cm of the gold standard. The experimental results hence indicate that our combined process can detect the nipple positions more accurately, as compared to other previous methods. Moreover, the proposed LVNM (Locate visible-nipple mammograms) algorithm designed with the generalization ability for automatic nipple clustering in the MIAS database has also yielded an identification rate of 99.53%.

Ant Colony Optimization (ACO) is a well-established metaheuristic successfully applied to solve hard combinatorial optimization problems, including Travelling Salesman Problem (TSP). However, ACO algorithm as many population-based approaches has some disadvantages, such as lower solution quality and longer computational time. To overcome these issues, parallel Cultural Ant Colony Optimization (pCACO) is introduced in this paper. The proposed approach hybridises Cultural Algorithm with ACO-based \(\mathcal {MAX}\)-\(\mathcal {MIN}\) Ant System. pCACO has been implemented on Graphics Processing Units (GPUs) using CUDA programming model. Through testing nine benchmark asymmetric TSP problems, the experimental results show the new method enhances the solution quality when compared to sequential and parallel ACO, yielding comparable computational time to parallel ACO.

This paper proposes a image enhancement and segmentation method for mammographic images, which is based on the pixel intensity transformation, spatial filtering, edge detection, and region growing techniques. This method consists of two approaches. The first approach is applying intensity transformation, logarithmic transformation, contrast stretching, histogram equalization and spatial filtering techniques separately to enhance the mammogram images. In the second approach is applying edge detection operators, local and global thresholding techniques used to extract the suspicious regions from back ground tissue.

Text categorisation (TC) is the task of assigning predefined categories to text. Most of the machine learning algorithms are sensitive to the features fed into it. Feature selection (FS) is an important preprocessing step to remove redundant and irrelevant terms from the training corpus. This paper proposes an ant colony optimisation (ACO) algorithm to select features for categorising longer documents to closely related categories. Heuristic value for each word is computed by the statistical dependency of the term to a category and its compactness value. Experiments were conducted with documents from 20newsgroup and Reuters-21578 benchmarks. The selected features were fed into the Naïve Bayes classifier. It was observed that the performance of the classifier improves with the features selected by the proposed method. The processes involved in algorithm are time intensive and demand parallelism. Hence, the ACO algorithm was parallelised using the MapReduce programming model of Hadoop.

A hybrid algorithm (HA) integrated genetic algorithm (GA) with ant colony optimization (ACO) for solving Traveling Salesman Problems(TSP) was studied to get better optimization performance than each single algorithm, and complement merits each other and avoid each own demerits. The hybrid algorithm runs GA first and then ACO. A new strategy called GSA was proposed aiming at the key link in the HA that converts genetic solution from GA into information pheromone to distribute in ACO. GSA takes new matrix which is formed by the combination of the former 90% of individual from genetic solution and 10% of individual by random generation as the basis of transformation of pheromone value. The best combination of genetic operators in GA was also discussed. Several TSP were used as simulation tests to test genetic operators matching and optimization performance of HA. The results show that PMX crossover matched with IVM mutation in the GA is the best combination of genetic operators which is able to make GA improve the precision of optimal solution, and HA using the best combination operators and GSA strategy is successful and available to search for optimal solution in high efficiency and has good convergence.