Ant Swarm Intelligence Research Articles

Liquid temperature prediction in bubbly flow using ant colony optimization algorithm in the fuzzy inference system as a trainer

In the current research paper a novel hybrid model combining first-principle and artificial intelligence (AI) was developed for simulation of a chemical reactor. We study a 2-dimensional reactor with heating sources inside it by using computational fluid dynamics (CFD). The type of considered reactor is bubble column reactor (BCR) in which a two-phase system is created. Results from CFD were analyzed in two different stages. The first stage, which is the learning stage, takes advantage of the swarm intelligence of the ant colony. The second stage results from the first stage, and in this stage, the predictions are according to the previous stage. This stage is related to the fuzzy logic system, and the ant colony optimization learning framework is build-up this part of the model. Ants movements or swarm intelligence of ants lead to the optimization of physical, chemical, or any kind of processes in nature. From point to point optimization, we can access a kind of group optimization, meaning that a group of data is studied and optimized. In the current study, the swarm intelligence of ants was used to learn the data from CFD in different parts of the BCR. The learning was also used to map the input and output data and find out the complex connection between the parameters. The results from mapping the input and output data show the full learning framework. By using the AI framework, the learning process was transferred into the fuzzy logic process through membership function specifications; therefore, the fuzzy logic system could predict a group of data. The results from the swarm intelligence of ants and fuzzy logic suitably adapt to CFD results. Also, the ant colony optimization fuzzy inference system (ACOFIS) model is employed to predict the temperature distribution in the reactor based on the CFD results. The results indicated that instead of solving Navier–Stokes equations and complex solving procedures, the swarm intelligence could be used to predict a process. For better comparisons and assessment of the ACOFIS model, this model is compared with the genetic algorithm fuzzy inference system (GAFIS) and Particle swarm optimization fuzzy inference system (PSOFIS) method with regards to model accuracy, pattern recognition, and prediction capability. All models are at a similar level of accuracy and prediction ability, and the prediction time for all models is less than one second. The results show that the model’s accuracy with low computational learning time can be achieved with the high number of CIR (0.5) when the number of inputs ≥ 4. However, this finding is vice versa, when the number of inputs < 4. In this case, the CIR number should be 0.2 to achieve the best accuracy of the model. This finding could also highlight the importance of sensitivity analysis of tuning parameters to achieve an accurate model with a cost-effective computational run.

A learning automata‐based clustering algorithm using ant swarm intelligence

AbstractAnt‐based clustering algorithms are inspired by the behaviour of real ants. In most ant‐based clustering algorithms, each ant lacks global visibility and only uses local search to find the best place on a grid to drop its data item. This paper presents a new clustering method called learning automata‐based clustering algorithm using learning automata (LA) and ant swarm intelligence. In this paper, the problem of finding the best location of grid for dropping a data item is solved using LA. We introduced a new drop operation using the global search capability of LA that can increase the quality of clustering. In the proposed method, each ant is equipped with an LA and a two‐dimensional grid is partitioned into a number of clusters. To drop a data item, the LA of each ant will be responsible for finding one of the best clusters on a grid unlike other methods that use the random search of a grid. To evaluate the results, some clustering evaluation criteria, such as the number of obtained clusters, f‐measure, Rand index, intracluster variance, Dunn index, error rate, and time cost were employed. Additionally, the proposed algorithms were compared with k‐means, Lumer and Faieta, ant clustering algorithm, modified version of the ant‐clustering algorithm, Chaotic Ant clustering (CAC), chaotic ant clustering algorithm, adaptive ant‐based clustering algorithm, adaptive artificial ant clustering algorithm, and ant clustering algorithm with information theoretic learning algorithms on both real and synthetic datasets. Experimental results show that the proposed method can increase the efficiency of the real dataset by 11.13% and that of the synthetic dataset by 16.11%. It also reduces the number of neighbouring function calls by 16% for the real dataset and by 28% for the synthetic dataset.

蟻の経路選択行動に着目した機械構造物のトポロジー最適化

Ant Colony Optimization (ACO) was inspired by a behavior of ant swarm intelligence. In this study, the principal stress vector was introduced in Ant Colony Topology Optimization (ACTO). Generally, Topology Optimization has applied the volume constraint as a termination condition, and has optimized a mean compliance. On the other hand, as a product design, a weight minimization with the local stress constraint is the most important object to attain it. Moreover, in a mechanical structural design, usually, a weight minimization is performed under the maintaining stiffness through checking the flow of forces (principal stress vector). However, topology optimization for a weight minimization with the local stress constraint is difficult, since the optimization problem has many local optimal solutions. In order to overcome the difficulty, we propose the ACTO method by considering the flow direction of the principal stress as a route, since ACO is a suitable optimization algorithm for route generation like a travel salesman problem.

Hybrid Ant Algorithm Based Query Processing with Multiagents in Sensor Networks

The wireless sensor networks are usually deployed in various application-specific contexts, which can be treated as distributed databases with big data. The event-involved query responses can be obtained by issuing query requests to this kind of database. However, the constraints of the energy and delay have had a great impact on the operation of wireless sensor networks. How to design the query-involved network model and the corresponding query processing algorithms is extremely challenging. This work investigates query processing problem in resource-constrained wireless sensor networks with the two-tier architecture and multiple query agents, where the multiple nodes of query agents are configured in the networks and the corresponding source cluster-heads send collected events to only one optimum query agent. To reduce the energy consumption and shorten the delivery delay, an efficient query processing algorithm inspired by the swarm intelligence of ants is proposed, which takes advantage of the beneficial clustering and routing emerging in a hybrid self-organized way from the positive interaction of ants. The experimental results demonstrated that the proposed algorithm can deliver collected events to the optimum query agents efficiently. Not only is the energy cost reduced but also the delivery delay is shortened significantly when transmitting the named events to the appropriate query agents.

Ant intelligence inspired blind data detection for ultra-wideband radar sensors

Given the computational complexity and sophisticated implementation of traditionally parametric channel estimators, it has been gradually recognized that the existing data detection methodologies based on the finite impulse response (FIR) propagation channel modeling may become infeasible for ultra-wideband (UWB) radar sensors, especially in some large-scale distributed scenarios. By exploiting the implicit information involved in the received signals, in this investigation, we present a non-parametric UWB data detection scheme for the distributed radar sensor networks. A novel characteristic representation is suggested first. From a pattern classification point of view, a group of quantitative features are then extracted by making full use of the inherent property of UWB propagations. Thus, UWB data detection is formulated as a pattern classification problem in a multidimensional feature space. By thoroughly utilizing the self-similarity of the representative patterns, the ant swarm intelligence inspired clustering algorithm, with the new designed ant movement strategy, is adopted to perform unsupervised data detections. The developed scheme is independent of any a priori modeling information, which essentially avoids the expensive parametric estimators and thus enables practically feasible realizations. To alleviate the computational burden, the principle component analysis (PCA) is further employed to compress the feature space. The simulation results validate the new algorithm, which is superior to the other popular non-parametric data analysis schemes.

Ant-Based Reliable Multi-Constrained Anycast Routing for Sensor Networks

Wireless sensor networks (WSNs) are task-specific where the tasks are specified by the users or applications. Energy and delay constraints have had a great impact on the design and operation of the WSNs. As routing plays an important role in data-centric WSNs, there are numerous energy aware routing protocols proposed for WSNs with single sink, generally using multi-hop paths to use the energy more efficiently. But most of them do not consider the communication delay carefully. As many applications of WSNs require real-time communication, the end-to-end delivery delay is another point to be considered, especially for urgent events. When considering the multiple QoS constraints such as the delivery delay and the minimal energy required, the multi-constrained routing is NP-hard. Moreover, WSNs also suffer from a scalability problem as the total number of nodes can reach hundreds or thousands. Thus, sensors located near the sink consume large amounts of energy. The sink is then isolated once they deplete their batteries, and the network fails to function. To cope with the scalability problem, multi-sink configuration is adopted for large-scale WSNs. How to select the optimum sink for packets with different delay and energy constraints in large-scale WSNs is the main problem to be resolved in this paper, which is called the multi-constrained anycast (MCA) problem. Obviously, it is NP-hard. We investigate the MCA problem in resource-constrained WSNs with multiple sinks and two-tier architecture, where the source cluster-heads send collected data to only one optimum sink. To reduce the energy consumption and shorten the delivery delay, a distributed anycast algorithm ARMCA inspired by swarm intelligence of ants is proposed, which can also achieve reliable data gathering during the deployment period. The scheme takes advantage of beneficial clustering and routing emerging in a self-organized way from the positive interaction of ants. The idea of the ACO-based routing is adopted to achieve the good performance of energy-efficiency and delivery delay. Meanwhile, the ant-based clustering is utilized to ensure the reliability. By the combination of the ant-based routing and clustering models, the ARMCA protocol can solve the MCA problem efficiently and flexibly. The simulation results demonstrated that the proposed algorithm can reliably deliver collected events to the optimum sinks efficiently, and has self-recovery capabilities to deal with failures of sensors including the sinks. Not only is the energy cost reduced, but also the delivery delay is shortened significantly when transmitting packets of the named events to the sinks.

Binary Ant Colony Evolutionary Algorithm

Every insect is considered,from the viewpoint of biological evolution,to be a neural cell that constitutes a neural network in a casual and loose way of joint．Through simulating the ant swarm intelligence on the basis of human neural network,this paper advances a linear binary network．The binary code expects a low intelligence of each ant,and each path corresponds to a comparatively small storage space,thus considerably improving the efficiency of computation．The test of function optimization and multi-dimensional 0/1 Knapsack proves that the computation has a good speed of convergence,a high stability and a perfect solution．