Evolutionary Ant Colony Optimization Research Articles

Robust Model Selection for Plant Leaf Image Recognition Based on Evolutionary Ant Colony Optimization With Learning Rate Schedule

Robust Model Selection for Plant Leaf Image Recognition Based on Evolutionary Ant Colony Optimization With Learning Rate Schedule

Detecting Dynamic Communities in Vehicle Movements Using Ant Colony Optimization

Detecting dynamic community structure in vehicle movements is helpful for revealing urban structures and human mobility patterns. Despite the fruitful research outcomes of community detection, the discovery of irregular-shaped and statistically significant dynamic communities in vehicle movements is still challenging. To overcome this challenge, we developed an evolutionary ant colony optimization (EACO) method for detecting dynamic communities in vehicle movements. Firstly, a weighted, spatially embedded graph was constructed at each time snapshot. Then, an ant-colony-optimization-based spatial scan statistic was upgraded to identify statistically significant communities at each snapshot by considering the effects of the communities discovered at the previous snapshot. Finally, different rules defined based on the Jaccard coefficient were used to identify the evolution of the communities. Experimental results on both simulated and real-world vehicle movement datasets showed that EACO performs better than three representative dynamic community detection methods: FacetNet (a framework for analyzing communities and evolutions in dynamic networks), DYNMOGA (dynamic multi-objective genetic algorithm), and RWLA (random-walk-based Leiden algorithm). The dynamic communities identified by EACO may be useful for understanding the dynamic organization of urban structures.

SP-ant: An ant colony optimization based operator scheduler for high performance distributed stream processing on heterogeneous clusters

A key feature of distributed stream processing (DSP) systems is the scheduling of operators on clustered computers. In scheduling, the assignment plan of operators to nodes of the cluster, requirements of operators, and the computational power of each worker node must be considered with the goal of finding a tradeoff between the communication latency of operators and the utilization of worker nodes to minimize the overall system response time. To reach this goal is quite challenging, especially in heterogeneous clusters, because there are no accurate estimations about the loads of worker nodes at run time. To address this challenge, we propose a novel stream processing scheduling using ant colony algorithm (SP-Ant). SP-Ant finds the best operator assignment plan considering the inter-node communication latencies of operators by initially collocating highly communicative operators on the same worker nodes using the bin-packing algorithm and iteratively (re-)scheduling only the less communicative operators using the exploration and exploitation phases of the evolutionary ant colony optimization (ACO) algorithm in order to reduce its convergence time. SP-Ant is implemented on the standard Apache Storm. Using several standard benchmark topologies of Storm, it is shown that SP-Ant outperforms the R-Storm and Storm default schedulers by at least 50% in reducing the response time.

Intelligence-Aware Batch Processing for TMA with Bearings-Only Measurements.

This paper develops a framework to track the trajectory of a target in 2D by considering a moving ownship able to measure bearing measurements. Notably, the framework allows one to incorporate additional information (e.g., obtained via intelligence) such as knowledge on the fact the target’s trajectory is contained in the intersection of some sets or the fact it lies outside the union of other sets. The approach is formally characterized by providing a constrained maximum likelihood estimation (MLE) formulation and by extending the definition of the Cramér–Rao lower bound (CRLB) matrix to the case of MLE problems with inequality constraints, relying on the concept of generalized Jacobian matrix. Moreover, based on the additional information, the ownship motion is chosen by mimicking the Artificial Potential Fields technique that is typically used by mobile robots to aim at a goal (in this case, the region where the target is assumed to be) while avoiding obstacles (i.e., the region that is assumed not to intersect the target’s trajectory). In order to show the effectiveness of the proposed approach, the paper is complemented by a simulation campaign where the MLE computations are carried out via an evolutionary ant colony optimization software, namely, mixed-integer distributed ant colony optimization solver (MIDACO-SOLVER). As a result, the proposed framework exhibits remarkably better performance, and in particular, we observe that the solution is less likely to remain stuck in unsatisfactory local minima during the MLE computation.

A Cooperative Autonomous Scheduling Approach for Multiple Earth Observation Satellites With Intensive Missions

Autonomous mission scheduling of multiple earth observation satellites (multi-EOSs) is considered as a complicated combinatorial optimization problem, which requires simultaneous consideration of imaging needs, resource constraints (electricity and memory) and possible emergencies. However, EOS resources are extremely scarce relative to intensive mission observation demands and most of the existing algorithms seldom consider emergencies. To address these challenges, this paper proposes a complete multi-EOSs scheduling scheme composed of two coupling stages, including mission pre-planning and mission replanning. We aim to obtain the optimal scheduling scheme for each EOS at the same time by maximizing the observation profits and balancing the resource consumption of each EOS. In this study, the roles of solar energy and ground stations in multi-EOSs mission scheduling are also considered. In the first stage, based on the cooperation and competition mechanism as well as the dynamic adjustment approach, an evolutionary ant colony optimization (EACO) method is developed to obtain the optimal solution for multi-EOSs pre-planning. In the second stage, using the results produced by EACO, we propose an interactive replanning approach to replan the missions that cannot be performed by faulty EOS in the event of unexpected accidents. Finally, several target scenarios are designed and numerical experiments are performed to show that the proposed algorithm presents better performance for large-scale multi-EOSs missions than other state-of-the-art algorithms.

Improving efficacy of Cr (VI) adsorption process on sustainable adsorbent derived from waste biomass (sugarcane bagasse) with help of ant colony optimization

A hexavalent form of chromium, which is a major pollutant found in water bodies, has lethal impacts on aquatic life and human beings. Among various effluent treatment techniques, the adsorption process was considered as an effective technique, but the cost of adsorbents hinders the viability of this process. In this research study, sugarcane bagasse based agricultural biomass waste in the form of activated granular carbon is used as an adsorbent to study its sustainability for chromium removal from effluent water in a batch process. The efficacy of the process was evaluated by validating various equilibrium isotherm and kinetic models. As the contact temperature is increased, resulted in a higher percentage of chromium removal for all three different initial concentrations. Adsorbent prepared at higher activation resulted in lower chromium removal. A novel inverse modeling technique integrated with hybrid evolutionary ant colony optimization technique was employed to estimate the isotherm/kinetic parameters. The systematic analysis revealed the mechanisms, inherent characteristics in the adsorption process, which forms the base in the design of industrial adsorption process and thus optimize the process parameters.

Design Property Network-Based Change Propagation Prediction Approach for Mechanical Product Development

Design changes are unavoidable during mechanical product development; whereas the avalanche propagation of design change imposes severely negative impacts on the design cycle. To improve the validity of the change propagation prediction, a mathematical programming model is presented to predict the change propagation impact quantitatively. As the foundation of change propagation prediction, a design change analysis model(DCAM) is built in the form of design property network. In DCAM, the connections of the design properties are identified as the design specification, which conform to the small-world network theory. To quantify the change propagation impact, change propagation intensity(CPI) is defined as a quantitative and much more objective assessment metric. According to the characteristics of DCAM, CPI is defined and indicated by four assessment factors: propagation likelihood, node degree, long-chain linkage, and design margin. Furthermore, the optimal change propagation path is searched with the evolutionary ant colony optimization(ACO) algorithm, which corresponds to the minimized maximum of accumulated CPI. In practice, the change impact of a gear box is successfully analyzed. The proposed change propagation prediction method is verified to be efficient and effective, which could provide different results according to various the initial changes.

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.