Ant Colony Optimization Strategy Research Articles

An energy-aware ant colony optimization strategy for virtual machine placement in cloud computing

An energy-aware ant colony optimization strategy for virtual machine placement in cloud computing

Information-Theory-based Nondominated Sorting Ant Colony Optimization for Multiobjective Feature Selection in Classification.

Feature selection (FS) has received significant attention since the use of a well-selected subset of features may achieve better classification performance than that of full features in many real-world applications. It can be considered as a multiobjective optimization consisting of two objectives: 1) minimizing the number of selected features and 2) maximizing classification performance. Ant colony optimization (ACO) has shown its effectiveness in FS due to its problem-guided search operator and flexible graph representation. However, there lacks an effective ACO-based approach for multiobjective FS to handle the problematic characteristics originated from the feature interactions and highly discontinuous Pareto fronts. This article presents an Information-theory-based Nondominated Sorting ACO (called INSA) to solve the aforementioned difficulties. First, the probabilistic function in ACO is modified based on the information theory to identify the importance of features; second, a new ACO strategy is designed to construct solutions; and third, a novel pheromone updating strategy is devised to ensure the high diversity of tradeoff solutions. INSA's performance is compared with four machine-learning-based methods, four representative single-objective evolutionary algorithms, and six state-of-the-art multiobjective ones on 13 benchmark classification datasets, which consist of both low and high-dimensional samples. The empirical results verify that INSA is able to obtain solutions with better classification performance using features whose count is similar to or less than those obtained by its peers.

Energy Dispatching Based on an Improved PSO-ACO Algorithm

In order to improve the comprehensive performance of energy dispatching between different sites, the optimization research of particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm is carried out. We proposed a new improved PSO-ACO algorithm based on the idea of hybrid algorithm to solve the problem of poor energy dispatching efficiency between sites. First, the multiobjective performance indicators were introduced to transform the sites’ energy dispatching problem into a multiobjective optimization problem. Second, the vitality factor was introduced into the PSO strategy to solve the local optimal problem, and in the PSO-ACO fusion strategy, the PSO routes were transformed into the ant colony enhancement pheromone to accelerate the accumulation speed of the ACO initial pheromone. Then, the angle guidance function was introduced into the state transition probability of the ACO strategy to improve the global search capability, and a high-quality pheromone update rule was proposed to improve the convergence speed of the algorithm. Finally, simulation experiments were carried out on the improved PSO-ACO algorithm, Min–Max Ant System (MMAS) algorithm, ACO algorithm, PSO algorithm, and PSO update algorithm in a variety of complex site scenarios. The simulation results show that the improved PSO-ACO algorithm can plan a site energy dispatching route with shorter route, less time-consuming, and higher security and realize the comprehensive and global optimization of energy dispatching.

Mobile sink-based data collection in event-driven wireless sensor networks using a modified ant colony optimization

The hotspot problem is one of the primary challenges in the wireless sensor networks (WSNs) because it isolates the sink node from the remaining part of the WSN. A mobile sink (MS)-based data acquisition strategy mitigates the hotspot problem, but the traditional MS-based data gathering approaches do not resolve the issue. However, the conventional techniques follow a fixed order of visits and static traversal of the MS. In this context, this paper uses a modified version of the ant colony optimization strategy for the data collected through a MS to mitigate the hotspot problem in the WSNs while improving the energy efficiency, network lifetime, throughput by reducing the packet loss and delay. In our work, we initially construct a forwarded load spanning tree to estimate the freight of each node in the WSN. Further, we choose RPs and their path simultaneously using the modified ACO algorithm by considering the forward loads, remaining energy, distance, etc. The proposed work also adopts the virtual RP selection strategy void unnecessary data exchanges between the nodes and RPs. Hence, it reduces the burden on relay nodes and optimize the energy usage among the nodes. We compare our approach with the recent ACO-based algorithms, and our approach outperforms them.

Optimal resource scheduling for SMEs using cloud in manufacturing sectors

Defining on a common policy which effectively suits all the production units, stakeholders and suppliers is very difficult to figure-out in highly populated countries like India. Manufacturing industries are ineffective in managing services, this is due to the communication gap between stakeholders. Approximately 48 million small and medium businesses are registered in India, according to a report by the Ministry of Micro, Small and Medium Enterprises (MSME). Among these numbers, SMEstend to contribute 40% of the total exports to India's industrial output, which is nearly 45%. As scheduling process majorly impacts on the time and cost of job completion, it is a critical part of the project. To efficiently schedule resources related to the casting industry, we propose a hybrid Ant Colony Optimization strategy. The efficiency of this enhanced algorithm was simulated to verify its effectiveness and the results show that, the proposed model minimizes both cost and time. It also maintains workload balance to maintain optimum resource utilization.

An efficient ant colony optimization framework for HPC environments

Combinatorial optimization problems arise in many disciplines, both in the basic sciences and in applied fields such as engineering and economics. One of the most popular combinatorial optimization methods is the Ant Colony Optimization (ACO) metaheuristic. Its parallel nature makes it especially attractive for implementation and execution in High Performance Computing (HPC) environments. Here we present a novel parallel ACO strategy making use of efficient asynchronous decentralized cooperative mechanisms. This strategy seeks to fulfill two objectives: (i) acceleration of the computations by performing the ants’ solution construction in parallel; (ii) convergence improvement through the stimulation of the diversification in the search and the cooperation between different colonies. The two main features of the proposal, decentralization and desynchronization, enable a more effective and efficient response in environments where resources are highly coupled. Examples of such infrastructures include both traditional HPC clusters, and also new distributed environments, such as cloud infrastructures, or even local computer networks. The proposal has been evaluated using the popular Traveling Salesman Problem (TSP), as a well-known NP-hard problem widely used in the literature to test combinatorial optimization methods. An exhaustive evaluation has been carried out using three medium and large size instances from the TSPLIB library, and the experiments show encouraging results with superlinear speedups compared to the sequential algorithm (e.g. speedups of 18 with 16 cores), and a very good scalability (experiments were performed with up to 384 cores improving execution time even at that scale).

Optimal operation of a multi-distribution natural gas pipeline grid: an ant colony approach

The enormous cost of transporting oil and gas through pipelines and the operational benefits that the industry receives through optimization has incited analysts for decades to find optimization strategies that help pipeline managers operate pipeline grids with the least expense. The paper aims to minimize the pipeline grids' operating costs using an ant colony optimization strategy. The article constructs a multi-objective modeling framework for a natural gas pipeline grid based on data from the French gas pipeline network corporation 'Gaz De France,' using pipeline and compressor hydraulics. The gas pipeline grid comprises seven gas supply nodes and nineteen gas distribution centers. Seven compressor stations provided at various locations on the pipeline route raise the gas pressure. Two competing objectives of reducing fuel usage in compressors and increasing throughput at distribution centers are acknowledged to reduce the pipeline's operating cost. The 'multi-objective ant colony optimization (MOACO)' approach is implemented to the pipeline transportation model to reduce the natural gas pipeline grid's operating cost. The process variables include the amount of gas flowing through the pipe and the pressure at pipe nodes. This method provides the optimum solution for each fuel consumption level on each compressor, and it does so by producing a Pareto front for each of the nineteen gas distribution points. The blueprints of the methodology used and the findings collected intend to guide pipeline managers and select the best of the most preferred solutions.

Landslide Susceptibility Mapping Using Ant Colony Optimization Strategy and Deep Belief Network in Jiuzhaigou Region

Landslidesusceptibility mapping (LSM) is the primary link of geological disaster risk evaluation, which is significant for postearthquake emergency response and rebuilding after disasters. Existing LSM studies applying deep learning (DL) methods have shortcomings such as easy overfitting, slow convergence, and insufficient hyperparametric optimization. In response to these problems, this study proposes an ensemble model based on ant colony optimization strategy and deep belief network (ACO-DBN). In ACO-DBN, DL optimization strategies were added to DBN and their combined parameters were optimized with ACO. Next, Pearson's correlation coefficient and random forest importance ranking methods were utilized to optimize landslide causative factors. Then, the Jiuzhaigou earthquake region was selected as an example to explore the applicability of this model. Besides, we conducted the Wilcoxon signed rank test in order to verify that the differences were statistically significant. In a comprehensive comparative all indexes and landslide density, the model proposed in this article shows good rationality, scientificity, and interpretability. The newly occurred landslide site further demonstrates that heuristically optimized DL could make scientific and accurate evaluation of landslide susceptibility.

Optimize 2-D Path Planning of Mobile Robot by using ACO with Guidance Factor

In this research article, the path planning of a mobile robot done by using ant colony optimization (ACO) with guidance factor. A two-dimensional (2-D) threat map structure design for path planning strategy, in which threat points are fixed in the path of moving robot. The two main objective of this research; to reach the mobile robot at the target position by using optimal route using ACO strategy. Secondly, by using guidance factor all the ants of ACO arrive at the fixed targeted area. Moreover, the results of the proposed algorithm compares with the classical ant system methodology. The simulated results show that the design method has short path planning and less steady state error to reach the designated path robustly.

Developing parallel ant colonies filtered by deep learned constrains for predicting RNA secondary structure with pseudo-knots

RNA plays important roles in cells besides being a simple carrier of genetic information. RNA secondary structure prediction is an efficient way to explore its biochemical function. RNA secondary structure prediction with pseudo-knots is really difficult, which has been proven as an NP-hard problem. Many of the existing predictions seemed to be limited not only by the quality of the search algorithms but also by the quality of the objective functions used. In this paper, a novel prediction model called DpacoRNA is proposed to improve the accuracy of the RNA secondary structure prediction with pseudo-knots, which mainly consists of two features: a parallel search algorithm and learning structural constraints using a deep model. First, based on the base-paired and single-stranded probabilities as multiple objective functions, DpacoRNA applied a parallel ant colony optimization strategy to predict the RNA secondary structure. Second, a bi-directional LSTM recurrent neural network was used to learn the base-pairing constraints. Finally, the constraints learned from the deep model were applied to the output of parallel ant colonies to refine the final secondary structures. To examine the strength and the weakness of the proposed method, multiple RNA types, including RNase P RNA, 5s rRNA, hammerhead ribozyme, transfer RNA, and tmRNA, were used to carefully benchmark DpacoRNA with other state-of-the-art solutions. The final results showed that DpacoRNA was competitive to the other methods.

TRUSS SHAPE OPTIMIZATION USING EVOLUTIONARY ANT COLONY OPTIMIZATION

Optimization is receiving attention in the capacity of engineering. The technique is applied to a variety of engineering problems such as Pile foundation layout on a footing of the residential house; trees layout; materials decision and structural problems. In structural engineering, morphogenesis by using structural optimization techniques is also receiving attention. There are three kinds of optimization problems: size optimization; topology optimization and shape optimization. In the structural problem, obtaining minimum compliance or minimum weight of structure subjected to constraints such as stress, displacement or volume are the main objectives. The methods by using heuristics such as Genetic Algorithm (GA) or Simulated Annealing (SA) to solve structural optimization problems have been reported recently. Swarm Intelligence (SI), simulating behaviors of biology group in nature, is also being reported. Ant Colony Optimization (ACO), simulating behaviors of ants in nature, is one kind of the SI approach. ACO is a multi-agent approach and ants, the agent, test design points based on random search technique. Hence, it is possible to test a huge design domain by using ACO. However, due to the randomness of ACO, since only the local information is considered, it is not efficient to solve structural optimizations by using ACO. On the other hand, a method of using an evolutionary strategy such as Evolutionary Structural Optimization (ESO) method or Bi-directional Evolutionary Structural Optimization (BESO) method are proved to be suitable for solving a variety of structural optimization problems. The strategy of these methods is updating design variables based on the sensitivity number, defined by the stress or particular displacement which are calculated by the finite element analysis. Sensitivity number is defined by considering the evolutionary mechanisms of the structure. The strategy has been applied for shape optimization of truss problems. By integrating the ESO strategy into the ACO, it is possible to test a huge design domain by using the ACO strategy and modifying the design variables to consider the behavior of structure following the evolutionary mechanisms to solve structural optimization problems. In this study, the Evolutionary Ant Colony Optimization (EACO) is proposed by integrating the ESO strategy into the ACO to solve shape optimization problems of trusses. The EACO is then tested by solving some shape optimization problems of trusses. The results obtained in this study are as follow: 1) By integrating the ESO strategy into the ACO, it is possible to test a huge design domain by using the ACO strategy and modifying the design variables to consider the behavior of truss structure. 2) In the shape optimization of trusses, the optimal shapes can be obtained efficiently by using the EACO. 3) The EACO is a new approach which has been proved to be suitable for solving the shape optimization of truss problems.

An efficient ant colony optimization strategy for the resolution of multi-class queries

Ant Colony Optimization is a bio-inspired computational technique for establishing optimal paths in graphs. It has been successfully adapted to solve many classical computational problems, with considerable results. Nevertheless, the attempts to apply ACO to the question of multidimensional problems and multi-class resource querying have been somewhat limited. They suffer from either severely decreased efficiency or low scalability, and are usually static, custom-made solutions with only one particular use. In this paper we employ Angry Ant Framework, a multipheromone variant of Ant Colony System that surpasses its predecessor in terms of convergence quality, to the question of multi-class resource queries. To the best of the authors knowledge it is the only algorithm capable of dynamically creating and pruning pheromone levels, which we refer to as dynamic pheromone stratification. In a series of experiments we verify that, due to this pheromone level flexibility, Angry Ant Framework, as well as our improvement of it called Entropic Angry Ant Framework, have significantly more potential for handling multi-class resource queries than their single pheromone counterpart. Most notably, the tight coupling between pheromone and resource classes enables convergence that is both better in quality and more stable, while maintaining a sublinear cost.

Solving integrated process planning and scheduling problem with constructive meta-heuristics

For product manufacturing, process planning is to select a series of manufacturing processes according to the product design specification, and scheduling is to allocate manufacturing resources such as machines and tools to these processes. It is a common problem that the process plan and the schedule are not able to cope with the changes in real time manufacturing. Integrated process planning and scheduling (IPPS) is to conduct the process planning and scheduling functions concurrently, with the aim to improve the dynamic responsiveness of the production schedule. This paper investigates the formulation and implementation of constructive meta-heuristics for solving IPPS problems. To begin with, a model representation is established to express IPPS problems with AND/OR graphs. With this model representation, a generic framework is proposed for implementing constructive meta-heuristics in the solution model. The generic framework provides a common procedure for the constructive meta-heuristics, which encapsulates the calculation of the search frontier and state transitions, and provides two interfaces for accommodating different constructive search algorithms. Ant colony optimization (ACO), a commonly-used algorithm which possesses all typical characteristics of constructive meta-heuristics, is adopted as a representative example for illustrating the implementation. Experiments and tests are conducted to validate the proposed system. The single objective minimizing the makespan is set for evaluating the performance of the proposed system. Experimental results of the benchmark problems have shown the effectiveness and high performance of the proposed approach based on the integration of the generic framework and ACO strategy.

Parallel ant colony optimization for the determination of a point heat source position in a 2-D domain

The Ant Colony Optimization (ACO) and its parallel implementations are applied to determine the unknown position of a point heat source in a two-dimensional steady-state heat conduction problem. The heuristic value, the determination of path and the objective function are all studied based on the inverse heat source problem. Some of the standard steps of the ACO are also modified according to the features of the heat conduction problem. In order to accelerate the speed of solving the inverse problem, two kinds of parallel ACO strategies, the fine-grained master-slave strategy and the coarse-grained strategy combined with the above modification are studied in this paper. The results show that the fine-grained strategy has a higher speedup than the coarse-grained strategy whiles the coarse-grained strategy has a higher accuracy rate. This means that the coarse-grained strategy is more proper during the parallel implementation for solving the inverse heat source problem and the parallel implementation of the ACO can improve the computational efficiency.

A new approach to detect epistasis utilizing parallel implementation of ant colony optimization by MapReduce framework

Genome-wide association studies (GWAS) involve the detection and interpretation of epistasis, which is responsible for the ‘missing heritability’ and influences common complex disease susceptibility. Many epistasis detection algorithms cannot be directly applied into GWAS as many combinations of genetic components are present in only a small amount of samples or even none at all. For a huge number of single nucleotide polymorphisms and inappropriate statistical tests, epistasis detection remains a computational and statistical challenge in genetic epidemiology. Here, we develop a novel method to identify epistatic interactions related to disease susceptibility utilizing an ant colony optimization strategy implemented by Google's MapReduce platform. We incorporate expert knowledge used to guide ants to make the best choice in the search process into the pheromone updating rule. We conduct sufficient experiments using simulated and real genome-wide data sets and experimental results demonstrate excellent performance of our algorithm compared with its competitors.

Ant Colony Optimization Based on Random Walk for Community Detection in Complex Networks

网络簇结构是复杂网络最普遍和最重要的拓扑属性之一,网络聚类问题就是要找出给定网络中的所有类簇.有很多实际应用问题可被建模成网络聚类问题.尽管目前已有许多网络聚类方法被提出,但如何进一步提高聚类精度,特别是在没有先验知识(如网络簇个数)的情况下如何发现合理的网络簇结构,仍是一个未能很好解决的难题. 针对该问题,在马尔可夫随机游走思想的启发下,从仿生角度出发提出一种全新的网络聚类算法——基于随机游走的蚁群算法RWACO.该算法将蚁群算法的框架作为RWACO 的基本框架,对于每一代,以马尔可夫随机游走模型作为启发式规则;基于集成学习思想,将蚂蚁的局部解融合为全局解,并用其更新信息素矩阵.通过“强化簇内连接,弱化簇间连接”这一进化策略,使网络簇结构逐渐地呈现出来.实验结果表明,对一些典型的计算机生成网络和真实网络, 该算法能够较准确地探测出网络的真实类簇数,与一些有代表性的算法相比,具有较高的聚类精度.;Community structure is one of the most important topological properties in complex networks. The network clustering problem (NCP) refers to the detection of network community structures, and many practical problems can be modeled as NCPs. So far, lots of network clustering algorithms have been proposed. However, further improvements in the clustering accuracy, especially when discovering reasonable community structure without prior knowledge, still constitute an open problem. Building on Markov random walks, the paper addresses this problem with a novel ant colony optimization strategy, named as RWACO, which improves prior results on the NCPs and does not require knowledge of the number of communities present on a given network. The framework of ant colony optimization is taken as the basic framework in the RWACO algorithm. In each iteration, a Markov random walk model is taken as heuristic rule. All of the ants’ local solutions are aggregated to a global one through clustering ensemble, which then will be used to update a pheromone matrix. The strategy relies on the progressive strengthening of within-community links and the weakening of between-community links. Gradually, this converges to a solution where the underlying community structure of the complex network will become clearly visible. The performance of algorithm RWACO was tested against a set of benchmark computer-generated networks, and as well on real-world network data sets. Experimental results confirm the validity and improvements of this approach.

ANT COLONY OPTIMIZATION WITH A NEW RANDOM WALK MODEL FOR COMMUNITY DETECTION IN COMPLEX NETWORKS

Detecting communities from complex networks has recently triggered great interest. Aiming at this problem, a new ant colony optimization strategy building on the Markov random walks theory, which is named as MACO, is proposed in this paper. The framework of ant colony optimization is taken as the basic framework in this algorithm. In each iteration, a Markov random walk model is employed as heuristic rule; all of the ants' local solutions are aggregated to a global one through an idea of clustering ensemble, which then will be used to update a pheromone matrix. The strategy relies on the progressive strengthening of within-community links and the weakening of between-community links. Gradually this converges to a solution where the underlying community structure of the complex network will become clearly visible. The proposed MACO has been evaluated both on synthetic benchmarks and on some real-world networks, and compared with some present competing algorithms. Experimental result has shown that MACO is highly effective for discovering communities.

Hybrid ant colony and immune network algorithm based on improved APF for optimal motion planning

SUMMARYInspired by the mechanisms of idiotypic network hypothesis and ant finding food, a hybrid ant colony and immune network algorithm (AC-INA) for motion planning is presented. Taking the environment surrounding the robot and robot action as antigen and antibody respectively, an artificial immune network is constructed through the stimulation and suppression between the antigen and antibody, and the antibody network is searched using improved ant colony algorithm (ACA) with pseudo- random-proportional rule and super excellent ant colony optimization strategy. To further accelerate the convergence speed of AC-INA and realize the optimal dynamic obstacle avoidance, an improved adaptive artificial potential field (AAPF) method is provided by constructing new repulsive potential field on the basis of the relative position and velocity between the robot and obstacle. Taking the planning results of AAPF method as the prior knowledge, the initial instruction definition of new antibody is initialized through vaccine extraction and inoculation. During the motion planning, once the robot meets with moving obstacles, the AAPF method is used for the optimal dynamic obstacle avoidance. The simulation results indicate that the proposed algorithm is characterized by good convergence property, strong planning ability, self-organizing, self-learning, and optimal obstacle avoidance in dynamic environments. The experiment in known indoor environment verifies the validity of AAPF-based AC-INA, too.

An efficient variable selection method based on the use of external memory in ant colony optimization. Application to QSAR/QSPR studies

A novel approach for the use of external memory in ant colony optimization strategy for solving descriptor selection problem in quantitative structure-activity/property relationship studies is described. In this approach, several ant colony system algorithms are run to build an external memory containing a number of elite ants. In the next step, all of the elite ants in the external memory are allowed to update the pheromones. Then the external memory is emptied and the updated pheromones are used again, by several ant colony system algorithms to build a new external memory. These steps are iteratively run for certain number of iterations. At the end, the memory will be containing several top solutions to the problem. The proposed approach was applied to solving variable selection problem in quantitative structure-activity/property relationship studies of rate constants of o-methylation of 36 phenol derivatives and activities of 31 antifilarial antimycin compounds, for which the obtained results revealed that both the speed and the solution quality are improved compared to conventional ant colony system algorithms.

Ant-Tree: an ant colony optimization approach to the generalized minimum spanning tree problem

The ant colony optimization is a meta-heuristic inspired by knowledge sharing amongst ants using pheromone, which serves as a kind of collective memory. Since the past few years, there have been several successful applications of this new approach for finding approximate solutions for computationally difficult problems in reasonable times. In this paper, we study the generalized minimum spanning tree problem that involves the design of a minimum weight connected network spanning at least one node out of every disjoint subset of the nodes in a graph. This problem has a wealth of pertinence to a wide range of applications in different areas. As the problem is known as computationally challenging, we adopt the ant colony optimization strategy and present a new solution method, called Ant-Tree, to develop approximate solutions. As an initial attempt, our study aims to provide an investigation of the ant colony optimization approach for coping with tree optimization problems. Through computational experiments, we compare the performances of our approach and the method available in the literature. Numerical results indicate that the proposed method is effective in producing quality approximate solutions.