Enhancing Facility Layout via Ant Colony Technique (Act)

Cellular manufacturing systems optimization is investigated and manipulated using artificial intelligent (AI) approach combining facility layout and group technology scope. This research applied the ANT COLONY technique  (ACT) optimization where this process was inspired by the real ants and how they move and build colonies by avoiding obstacle and simulate the process to get a procedure that can be adopted on this optimization process. In this research the problem goes in two way first the theory that take account the positions of machines inside the plant and its equations of controlling and second is the routing of part during product life cycle then execute results and applying it on factory configuration. The application of Ants system was carried out on industrial factory of electrical motor where all data was taken from the factory depending on the position and sequence of operations took place. Results were carried out in a way that depending on the showing site plan configurations for each stage and studying the iteration curve response to the parameters changes while testing the system during different environments. The results show high flexibility in ACS (Ant colony system) with fast response and high reduction in the distance crossed by the product part that reached 500m. The ratio of the reduction is 0.625. Keyword: Artificial intelligent (AI), Ant colony (AC), pheromone, genetic algorithm, facility layout, cell manufacturing (CM)

Heterogeneous Ant Colony Optimisation Methods and their Application to the Travelling Salesman and PCB Drilling Problems

Ant Colony Optimization (ACO) is an optimization algorithm that is inspired by the foraging behaviour of real ants in locating and transporting food source to their nest. It is designed as a population-based metaheuristic and have been successfully implemented on various NP-hard problems such as the well-known Traveling Salesman Problem (TSP), Vehicle Routing Problem (VRP) and many more. However, majority of the studies in ACO focused on homogeneous artificial ants although animal behaviour researchers suggest that real ants exhibit heterogeneous behaviour thus improving the overall efficiency of the ant colonies. Equally important is that most, if not all, optimization algorithms require proper parameter tuning to achieve optimal performance. However, it is well-known that parameters are problem-dependant as different problems or even different instances have different optimal parameter settings. Parameter tuning through the testing of parameter combinations is a computationally expensive procedure that is infeasible on large-scale real-world problems. One method to mitigate this is to introduce heterogeneity by initializing the artificial agents with individual parameters rather than colony level parameters. This allows the algorithm to either actively or passively discover good parameter settings during the search. The approach undertaken in this study is to randomly initialize the ants from both uniform and Gaussian distribution respectively within a predefined range of values. The approach taken in this study is one of biological plausibility for ants with similar roles, but differing behavioural traits, which are being drawn from a mathematical distribution. This study also introduces an adaptive approach to the heterogeneous ant colony population that evolves the alpha and beta controlling parameters for ACO to locate near-optimal solutions. The adaptive approach is able to modify the exploitation and exploration characteristics of the algorithm during the search to reflect the dynamic nature of search. An empirical analysis of the proposed algorithm tested on a range of Travelling Salesman Problem (TSP) instances shows that the approach has better algorithmic performance when compared against state-of-the-art algorithms from the literature

CLOSE NESTING ASSOCIATION OF TWO ANT SPECIES IN ARTIFICIAL SHELTERS: RESULTS FROM A LONG-TERM EXPERIMENT

Close nesting (real or quasi plesiobiosis) is the simplest form of spatial associations of heterospecific ant colonies that share the same microhabitat, but remain separate as individual units. We studied the tendency to close nesting between two ant species, Lasius psammophilus and Plagiolepis taurica, under slate plates used as artificial nesting sites during a 34 years long study period. 2410 nest records of 11 ant species were made under the plates, and a total of 181 close nesting associations were observed, most of which between L. psammophilus and P. taurica. The hypothesis of the weak antagonism between the two species was supported by (1) the rate of nesting associations, which was lower than expected from random probabilities; (2) the maximum of the index of avoidance, at intermediate densities; (3) the negative relationship between the unoccupied nesting shelters and the frequency of close nesting; (4) the tendency of individual and group level avoidance and (5) the low rate of interspecific aggression. The benefit of choosing favorable nesting sites and the risk of interspecific competition are in trade-off relation and the attractiveness of nesting shelters is the stronger constraint; therefore it can be regarded as the primary driver of the formation of spatial associations between the colonies of the two studied species

Towards Improving Clustering Ants: An Adaptive Ant Clustering Algorithm

Among the many bio-inspired techniques, ant-based clustering algorithms have received special attention from the community over the past few years for two main reasons. First, they are particularly suitable to perform exploratory data analysis and, second, they still require much investigation to improve performance, stability, convergence, and other key features that would make such algorithms mature tools for diverse applications. Under this perspective, this paper proposes both a progressive vision scheme and pheromone heuristics for the standard ant-clustering algorithm, together with a cooling schedule that improves its convergence properties. The proposed algorithm is evaluated in a number of well-known benchmark data sets, as well as in a real-world bio informatics dataset. The achieved results are compared to those obtained by the standard ant clustering algorithm, showing that significant improvements are obtained by means of the proposed modifications. As an additional contribution, this work also provides a brief review of ant-based clustering algorithms.292143154Abraham, A., Ramos, V., Web usage mining using artificial ant colony clustering and genetic programming (2003) Proc. of the Congress on Evolutionary Computation (CEC 2003), pp. 1384-1391. , Canberra, IEEE PressBezdek, J.C., (1981) Pattern Recognition with Fuzzy Objective Function Algorithm, , Plenum PressBonabeau, E., Dorigo, M., Théraulaz, G., (1999) Swarm Intelligence from Natural to Artificial Systems, , Oxford University PressCamazine, S., Deneubourg, J.-L., Franks, N.R., Sneyd, J., Theraulaz, G., Bonabeau, E., (2001) Self-organization in Biological Systems, , Princeton University PressDe Castro, L.N., Von Zuben, F.J., (2004) Recent Developments in Biologically Inspired Computing, , Idea Group IncDeneubourg, J.L., Goss, S., Sendova-Franks, N.A., Detrain, C., Chrétien, L., The dynamics of collective sorting: Robot-like ant and ant-like robot (1991) Simulation of Adaptive Behavior: from Animals to Animats, pp. 356-365. , J. A. Meyer and S. W. Wilson (eds.). MIT Press/Bradford BooksEveritt, B.S., Landau, S., Leese, M., (2001) Cluster Analysis, , Arnold Publishers, LondonGutowitz, H., Complexity-seeking ants (1993) Proceedings of the Third European Conference on Artificial LifeHandl, J., Knowles, J., Dorigo, M., On the performance of ant-based clustering (2003) Proc. of the 3rd International Conference on Hybrid Intelligent Systems, Design and Application of Hybrid Intelligent Systems, pp. 204-213. , IOS PressHandl, J., Meyer, B., Improved ant-based clustering and sorting in a document retrieval interface (2002) Lecture Notes in Computer Science, 2439, pp. 913-923. , J.J. Merelo, J.L.F. Villacañas, H.G. Beyer, P. Adamis Eds.: Proceedings of the PPSN VII - 7th Int. Conf. on Parallel Problem Solving from Nature, Granada, Spain, Springer-Verlag, BerlinKanade, P., Hall, L.O., Fuzzy ants as a clustering concept (2003) Proc. of the 22nd International Conference of the North American Fuzzy Information Processing Society (NAFIPS), pp. 227-232Kaufman, L., Rousseeuw, P.J., (1990) Finding Groups in Data - An Introduction to Cluster Analysis, Wiley Series in Probability and Mathematical Statistics, , John Wiley & Sons IncKeim, D.A., (2002) Information Visualization and Visual Data Mining: IEEE Transactions on Visuali Zation and Computer Graphics, 7 (1), pp. 100-107Kennedy, J., Eberhart, R., Shi, Y., (2001) Swarm Intelligence, , Morgan Kaufmann PublishersLabroche, N., Monmarché, N., Venturini, G., A new clustering algorithm based on the chemical recognition system of ants (2002) Proc. of the 15th European Conference on Artificial Intelligence, pp. 345-349. , France, IOS PressLumer, E.D., Faieta, B., Diversity and adaptation in populations of clustering ants (1994) Proceedings of the Third International Conference on the Simulation of Adaptive Behavior: from Animals to Animats, 3, pp. 499-508. , MIT PressMonmarché, N., Slimane, M., Venturini, G., On improving clustering in numerical databases with artificial ants. Advances in artificial life (1999) Lecture Notes in Computer Science, 1674, pp. 626-635. , D. Floreano, J.D. Nicoud, and F. Mondala Eds., Springer-Verlag, BerlinPaton, R., (1994) Computing with Biological Metaphors, , Chapman & HallRamos, V., Merelo, J.J., Self-organized stigmergic document maps: Environment as a mechanism for context learning (2002) AEB'2002, First Spanish Conference on Evolutionary and BioInspired Algorithms, pp. 284-293. , E. Alba, F. Herrera, J.J. Merelo et al. Eds., SpainRamos, V., Muge, F., Pina, P., Self-organized data and image retrieval as a consequence of inter-dynamic synergistic relationships in artificial ant colonies (2002) Soft-Computing Systems - Design, Management and Applications, Frontiers in Artificial Intelligence and Applications, 87, pp. 500-509. , J. Ruiz-del-Solar, A. Abrahan and M. Köppen Eds. IOS Press, AmsterdamRitter, H., Kohonen, T., Self-organizing semantic maps (1989) Biol. Cybern., 61, pp. 241-254Sherafat, V., De Castro, L.N., Hruschka, E.R., TermitAnt: An ant clustering algorithm improved by ideas from termite colonies (2004) Lecture Notes in Computer Science, 3316, pp. 1088-1093. , Proc. of ICONIP 2004, Special Session on Ant Colony and Multi-Agent SystemsSherafat, V., De Castro, L.N., Hruschka, E.R., The influence of pheromone and adaptive vision on the standard ant clustering algorithm (2004) Recent Developments in Biologically Inspired Computing, pp. 207-234. , L. N. de Castro and F. J. Von Zuben, Chapter IX. Idea Group IncVizine, A.L., De Castro, L.N., Gudwin, R.R., Text document classification using swarm intelligence (2005) Proc. of KIMAS 2005, , CD ROMYeung, K.Y., Medvedovic, M., Bumgarner, R.E., Clustering gene-expression data with repeated measurements (2003) Genome Biology, 4 (5), pp. R34. , articl

Learning Bayesian network equivalence classes using ant colony optimisation

Bayesian networks have become an indispensable tool in the modelling of uncertain knowledge. Conceptually, they consist of two parts: a directed acyclic graph called the structure, and conditional probability distributions attached to each node known as the parameters. As a result of their expressiveness, understandability and rigorous mathematical basis, Bayesian networks have become one of the first methods investigated, when faced with an uncertain problem domain. However, a recurring problem persists in specifying a Bayesian network. Both the structure and parameters can be difficult for experts to conceive, especially if their knowledge is tacit.To counteract these problems, research has been ongoing, on learning both the structure and parameters of Bayesian networks from data. Whilst there are simple methods for learning the parameters, learning the structure has proved harder. Part ofthis stems from the NP-hardness of the problem and the super-exponential space of possible structures. To help solve this task, this thesis seeks to employ a relatively new technique, that has had much success in tackling NP-hard problems. This technique is called ant colony optimisation. Ant colony optimisation is a metaheuristic based on the behaviour of ants acting together in a colony. It uses the stochastic activity of artificial ants to find good solutions to combinatorial optimisation problems. In the current work, this method is applied to the problem of searching through the space of equivalence classes of Bayesian networks, in order to find a good match against a set of data. The system uses operators that evaluate potential modifications to a current state. Each of the modifications is scored and the results used to inform the search. In order to facilitate these steps, other techniques are also devised, to speed up the learning process. The techniques includeThe techniques are tested by sampling data from gold standard networks and learning structures from this sampled data. These structures are analysed using various goodnessof-fit measures to see how well the algorithms perform. The measures include structural similarity metrics and Bayesian scoring metrics. The results are compared in depth against systems that also use ant colony optimisation and other methods, including evolutionary programming and greedy heuristics. Also, comparisons are made to well known state-of-the-art algorithms and a study performed on a real-life data set. The results show favourable performance compared to the other methods and on modelling the real-life data

Computational Chemotaxis in Ants and Bacteria over Dynamic Environments

Chemotaxis can be defined as an innate behavioural response by an organism to a directional stimulus, in which bacteria, and other single-cell or multicellular organisms direct their movements according to certain chemicals in their environment. This is important for bacteria to find food (e.g., glucose) by swimming towards the highest concentration of food molecules, or to flee from poisons. Based on self-organized computational approaches and similar stigmergic concepts we derive a novel swarm intelligent algorithm. What strikes from these observations is that both eusocial insects as ant colonies and bacteria have similar natural mechanisms based on stigmergy in order to emerge coherent and sophisticated patterns of global collective behaviour. Keeping in mind the above characteristics we will present a simple model to tackle the collective adaptation of a social swarm based on real ant colony behaviors (SSA algorithm) for tracking extrema in dynamic environments and highly multimodal complex functions described in the well-know De Jong test suite. Later, for the purpose of comparison, a recent model of artificial bacterial foraging (BFOA algorithm) based on similar stigmergic features is described and analyzed. Final results indicate that the SSA collective intelligence is able to cope and quickly adapt to unforeseen situations even when over the same cooperative foraging period, the community is requested to deal with two different and contradictory purposes, while outperforming BFOA in adaptive speed. Results indicate that the present approach deals well in severe Dynamic Optimization problems.Comment: 8 pages, 6 figures, in CEC 07 - IEEE Congress on Evolutionary Computation, ISBN 1-4244-1340-0, pp. 1009-1017, Sep. 200

DESIGN OF OPTIMAL PID CONTROLLER FOR THREE PHASE INDUCTION MOTOR BASED ON ANT COLONY OPTIMIZATION

Speed control of an induction motor is an important part of the operation of an induction motor. One method of regulating motor speed is the addition of a PID controller. PID parameters must be tuned properly to get the optimal speed. In this study, the PID controller tuning method uses an artificial intelligence method based on Ant Colony Optimization (ACO). ACO algorithm in an intelligent algorithm that is inspired by the behavior of ants looking for food sources in groups with traces of feromone left behind. In this study, food sources are represented as optimal parameters of PID. From the computational results obtained optimal parameters respectively, P (Proportional) 0.5359, I (Integral) 0.1173, D (Derivative) 0.0427. ACO computing found the optimal parameters in the 21st iteration with a minimum fitness function of 11.8914. Case studies are used with two variations of the speed of the induction motor input. With optimal tuning, the performance of the induction motor is increasing, marked by a minimum overshoot of 1.08 pu and a speed variation of both overshoots of 1,201 pu, whereas without control 1.49 pu and 1.28 pu, as well as with PID trial control of 1.22 pu and 1.23 pu respectively. The benefits of this research can be used as a reference for the operation of induction motors, by tuning the Ant Colony intelligent method for the PID controller in real-time with the addition of microcontroller components