Negative effects of ant-plant interaction on pollination: costs of a mutualism

The mutualism of ants and extrafloral nectary (EFN)-bearing plants is known to reduce rates of herbivory. However, ants may have negative impacts on other mutualisms such as pollination, constituting an indirect cost of a facultative mutualism. For instance, when foraging on or close to reproductive plant parts ants might attack pollinators or inhibit their visits. We tested the hypothesis that ants on EFN-bearing plants may negatively influence pollinator behavior, ultimately reducing plant fitness (fruit set). The study was done in a reserve at Brazilian savannah using the EFN-bearing plant Banisteriopsis malifolia (Malpighiaceae). The experimental manipulation was carried out with four groups: control (free visitation of ants), without ants (ant-free branches), artificial ants (isolated branches with artificial ants on flowers) and plastic circles (isolated branches with plastic circles on flowers). We made observations on flower visitors and their interactions, and measured fruit formation as a proxy for plant fitness. Our results showed that pollinators hesitated to visit flowers with artificial ants, negatively affecting pollination, but did not hesitate to visit flowers with plastic circles, suggesting that they recognize the specific morphology of the ants. Pollinators spent more time per flower on the ant-free branches, and the fruiting rate was lower in the group with artificial ants. Our results confirm an indirect cost in this facultative mutualism, where the balance between these negative and positive effects of ants on EFN-bearing plants are not well known.

Ant colony optimization for feasible scheduling of step-controlled smart grid generation

The electrical energy grid is currently experiencing a paradigm shift in control. In the future, small and decentralized energy resources will have to responsibly perform control tasks like frequency or voltage control. For many use cases, scheduling of energy resources is necessary. In the multi-dimensional discrete case–e.g., for step-controlled devices–this is an NP-hard problem if some sort of intermediate energy buffer is involved. Systematically constructing feasible solutions during optimization, hence, becomes a difficult task. We prove the NP-hardness for the example of co-generation plants and demonstrate the multi-modality of systematically designing feasible solutions. For the example of day-ahead scheduling, a model-integrated solution based on ant colony optimization has already been proposed. By using a simulation model for deciding on feasible branches, artificial ants construct the feasible search graphs on demand. Thus, the exponential growth of the graph in this combinatorial problem is avoided. We present in this extended work additional insight into the complexity and structure of the underlying the feasibility landscape and additional simulation results.

River water quality management using a fuzzy optimization model and the NSFWQI Index

In this study, a novel multiple-pollutant waste load allocation (WLA) model for a river system is presented based on the National Sanitation Foundation Water Quality Index (NSFWQI). This study aims to determine the value of the quality index as the objective function integrated into the fuzzy set theory so that it could decrease the uncertainties associated with water quality goals as well as specify the river's water quality status rapidly. The simulation-optimization (S-O) approach is used for solving the proposed model. The QUAL2K model is used for simulating water quality in different parts of the river system and ant colony optimization (ACO) algorithm is applied as an optimizer of the model. The model performance was examined on a hypothetical river system with a length of 30 km and 17 checkpoints. The results show that for a given number of both the simulator model runs and the artificial ants, the maximum objective function will be obtained when the regulatory parameter of the ACO algorithm (i.e., q0) is considered equal to 0.6 and 0.7 (instead of 0.8 and 0.9). Also, the results do not depend on the exponent of the membership function (i.e., γ). Furthermore, the proposed methodology can find optimum solutions in a shorter time.

Ant colony algorithm for text classification in multicore-multithread environment

This paper presents about Ant Colony Algorithm (ACO) for Text Classification in Multicore-Multithread Environment in Artificial Intelligent domain. We had develop a software which assimilate concurrency concept to multiple artificial ants. Pheromone in ACO is the main concept used to solve the text classification problem. In regards to its role, pheromone value is changed depending on the solution finding that has been discovered at the pseudo random heuristic attempt in selecting path from text words. However, ACO can take up longer time to process larger training document. Based on the cooperative concept of ants living in colony, the ACO part is examined to work in multicore-multithread environment as to cater additional execution time benefit. In running multicore-multithread environment, the modification aims to make artificial ants actively communicate between multiple physical cores of processor. The execution time reduction is expected to show an improvement without compromising the original classification accuracy by the investment of trading on more processing power. The single and multicore-multithreaded version of ACO was compared statistically by conduction relevant test. It was found that the result shows a positive time reduction improvement.

Rough Sets and Colonies of Artificial Ants for the Improvement of Training Sets

Improving training sets is an area of active research within l to Artificial Intelligence. In particular, it is of particular interest in supervised classification systems, where the quality of training data is crucial. This paper presents a new method for the improvement of training sets, based on approximate sets and artificial ant colonies. The experimental study carried out with international databases allows us to guarantee the quality of the new algorithm, which has a high efficiency.

CREATING THE BEST ROUTING BY HEURISTIC ALGORITHM

In this study, Travelling Salesman Problem (TSP), an NP-hard problem, is addressed. In order to get the best results with a view to directing TSP heuristics, the ant colony algorithm was used for solution purposes. The purpose was to solve the problem of setting a course for the bread distribution trucks of Istanbul Halk Ekmek (Public Bread) Company using the ant colony algorithm on TSP. A liquid called Pheromone, which ants release in order to establish communication among them, is known as the most fundamental matter to provide this communication. In this research, artificial ants, which function with the logic of finding the shortest path in the area where they are located, were utilized. The purpose of our programme is to determine the shortest route for the arrival of the distribution trucks to the kiosks where bread is sold to the public. The route developed by the programme is displayed over Google maps. Motivation/Background: Explain the importance of the problem investigated in the paper. Include here a statement of the main research question. Method: Give a short account of the most important methods used in your investigation. Results: Present the main results reported in the paper. Conclusions: Briefly present the conclusions and importance of the results. Concisely summarize the study’s implications. Please do not include any citations in the abstract.

APPLICATION OF ANT OPTIMIZATION ALGORITHMS IN THE SOLUTION OF THE ROUTING PROBLEM

The article discusses current issues of using evolutionary algorithms to solve the routing problem. Ant algorithms (MA), like most types of evolutionary algorithms, are based on the use of a population of potential solutions and are designed to solve combinatorial optimization problems, first of all, search for various paths on graphs. The cooperation between individuals (artificial ants) is implemented on the basis of stigmetry modeling. In addition, each agent, called artificial ant, is looking for a solution to the problem. Artificial ants consistently build a solution to the problem, moving around the graph, lay the pheromone and, when choosing a further section of the path, take into account the concentration of this enzyme. The higher the concentration of pheromone in the subsequent section, the greater the likelihood of its choice. Since MA is based on the movement of ants along some paths, MAs are effective, first of all, in solving problems that can be interpreted in the form of a graph. Computer experiments showed that the efficiency of MA increases with increasing dimension of the problem and for tasks on high-dimensional graphs they work faster than other evolutionary algorithms. Good results were also noted in solving non-stationary problems on graphs with a changing environment. In connection with this, the implementation of the meta - heuristic method is proposed as a modification of ant optimization algorithms. The scheme of the system is presented. A software product specification is also provided

Improvement on Image Edge Detection Using a Novel Variant of the Ant Colony System

In this paper, an improved Ant Colony System algorithm applied to image edge detection is presented. During their movement on image, artificial ants establish pheromone graph which represents the image edge information. The ant movement is directed by the local variation of the image’s intensity values. To improve this method, supplementary behaviors are added to each ant in response to its local stimuli, i.e., the ant self-reproduces and directs its progenitors to an appropriate direction to explore more suitable areas. Moreover, it dies if it exceeds a specific iteration age and so the ineffective searches are eliminated. These additional behaviors allow diversifying the exploration performed by ants and also reinforcing the exploitation of these ants’ search experience. Proposed approach allows having more accurate and more complete edges. The performance is tested visually with various images and empirically with evaluation parameters.