Ant Systems of Optimization: Introduction and Review of Applications

Transcription

1 International Journal of Electronics Engineering Research. ISSN Volume 9, Number 2 (2017) pp Research India Publications Ant Systems of Optimization: Introduction and Review of Applications Shipra 1 and Priyanka 2 and Vibha Aggarwal 3 1,2 Department of Applied Sciences, College of Engineering and Management, Punjabi University Neighbourhood Campus, Rampuraphul, Punjab, India. 3 Department of Electronics and Communication Engineering, College of Engineering and Management, Punjabi University Neighbourhood Campus, Rampuraphul, Punjab, India. Abstract This paper overviews origin and some applications of ant system of optimization. Ants live in colonies. The colony can be regarded as a multiagent organization where each agent performs independently as governed by goal of colony survival.. On observing the behavior of ants, Ant-Algorithm was proposed. The ant algorithms have been extensively studied by European researchers. These algorithms have successfully been applied to solving many complex combinatorial optimization problems. This paper reviews the applications of ant system of optimization to travelling salesman problem, water resource problems and problems related to engineering and science. 1. INTRODUCTION Ants are social insects and live in colonies. Their behavior is governed by the goal of colony survival rather than on the survival of individuals. When searching for food, ants firstly search the area surrounding their nest randomly. While moving, ants leave a chemical pheromone trail on the ground. Ants can smell pheromone. While choosing their path, they tend to choose, in probability, paths marked by strong pheromone concentrations. As soon as an ant finds a food source, it evaluates the quantity and the quality of the food and carries some of it back to the nest. During the return trip, the quantity of pheromone that an ant leaves on the ground may depend on the quantity and quality of the food. The pheromone trails will direct other ants to the food source and enables them to find shortest paths between their nest and food sources. Corresponding Author

2 276 Shipra, Priyanka and Vibha Aggarwal On observing the behavior of ants, Optimization algorithms imitating such a behavior were firstly proposed by Dorigo et al. in 1992 [1].The first ACO algorithm presented in the literature is called Ant System (AS) [2]. An improvement of AS is Ant Colony System (ACS) algorithm proposed by Dorigo and Gambardella [3]. Later on, introduces a new rank based version of the ant system and present results of a computational study [4]. Max-Min Ant (MMAS) algorithm was introduced in 1997 [5]. These algorithms show interesting results and are competitive with other state of the optimization methods. However, more research work is needed to improve the ant colony algorithms to solve large volume of combinatorial problems. 2. APPLICATIONS OF ANT SYSTEMS OF OPTIMIZATION ACO procedures have been applied to solve a broad set of problems. Ant system has been applied to combinatorial optimization problems such as the traveling salesman problem (TSP), the quadratic assignment problem Network Design Problems, vehicle Routing Problem, Location Problems, Assignment Problems, Facility Location Problems, Transportation Problems, Covering Problems, Network Flow Problems etc. 2.1 ANT COLONY OPTIMIZATION TO TRAVELING SALESMAN PROBLEM (TSP) The TSP has played a central role in ACO, because it was the application problem chosen when proposing the first ACO algorithm called Ant System and it was used as a test problem for almost all ACO algorithms proposed. Many researchers had used AS to solve travelling TSP problems. Johnson and McGeoch [6] surveyed a wide variety of approaches to the TSP and conclude that he best ones are all based on local search in one form or another. The researcher [7] used ant colony optimization to solve TSP problem and compare the quality of solution with optimal solution. He concluded that the quality of solutions depends on the number of ants. Hashim Ali et al. [8] compared ant algorithm with genetic algorithm. They conclude that whenever no. of iteration is less, ACO is better than genetic algorithm and calculate the optimal solution but whenever iteration increases, ACO the complexity of ant colony optimization increase as compare to genetic algorithm. 2.2 ANT COLONY OPTIMIZATION TO WATER RESOURCES PROBLEMS Water resources systems analysis is the science of developing and applying mathematical operations research techniques to water resources systems problems comprised of reservoirs, ground water, rivers, watersheds, and distribution systems for single or multiobjective problems, deterministic or stochastic Problems. ACO applications to water resources problems are quite few and there is a great potential to apply ACO in this field.

3 Ant Systems of Optimization: Introduction and Review of Applications 277 Kumar and Reddy [9] proposed an Ant Colony Optimization algorithm for a multipurpose reservoir system. They use potential of ACO technique to derive suitable operating policies for a multi-purpose reservoir system. Moeini and Afshar [10] used three max-min ant system formulations for optimal operation of reservoirs using two sets of decision variables storage and releases, and three graph form representations. It was shown that the max-min ant system formulation was successful in solving the problem of optimal operation of reservoirs with the releases settings been better than the others. Dariane and Moradi [11] used ant colony optimization for continuous domains (ACOR) to solve the optimal releases problem of reservoirs. Li et al. [12] developed a hybrid ant colony genetic algorithm model for groundwater long term monitoring to maximize sampling cost-effectiveness. Later on, Li et al. [13] developed a hybrid ant colony-simulated annealing method for groundwater parameter estimation. The inverse problem of parameter identification was formulated as an optimization problem. Maier et al. [14] applied traditional ant colony settings for optimizing two benchmark gravitational one loading conditions water distribution systems. 2.3 ANT COLONY OPTIMIZATION IN SCIENCE AND ENGINEERING Many researchers have used AS to solve many Engineering Problems. Saleh [15] explains how ants efficiently find their food using an indirect communication procedure and how their approach can be mimicked in designing GPS surveying networks. The researchers in 1999 used an ant colony meta-heuristic optimization method to solve the redundancy allocation problem (RAP) [16]. J. Eggers et al. [17] in their research paper describe the application of a standard Ant Colony optimization (ACO) algorithm to the problem of the arrangement of letters on a computer keyboard. They used ACO to find the best possible arrangement of letters on a keyboard. Another researcher used ACO for data allocation in memory of supercomputers [18]. Alena Shmygelska et al. [19] introduced an ant colony optimization (ACO) algorithm to address the non-deterministic polynomial-time hard (NP-hard) combinatorial problem of predicting a protein's conformation from its amino acid sequence under a widely studied, conceptually simple model the 2-dimensional (2D) and 3- dimensional (3D) hydrophobic-polar (HP) model. S. Fidanova et al. [20] showed that Ant Colony System approach can be successfully applied to the 3D protein folding problem. The folding achieved by his algorithm is very similar to the real protein folding when it is applied on short proteins. The obtained results were encouraging and the ability of the developed algorithm to generate rapidly high-quality solutions can be seen prediction of the protein folding by its amino-acid chains. 3. CONCLUSIONS There is currently a lot of ongoing activity in the scientific community to apply antbased algorithms to many different optimization problems. Recent applications cover

4 278 Shipra, Priyanka and Vibha Aggarwal problems like vehicle routing, sequential ordering, graph coloring, routing in communications networks, and so on. Among the emerged metaheuristic optimization techniques, ant colony optimization (ACO) has received considerable attentions in problems related to TSP, water resources and widely used in engineering, science management, arts fields. Different versions of ACO have proved to be useful in solving number of complex problems in discrete and continuous domains with single and multiple objectives. REFERENCES [1] Dorigo, M., 1992, Optimization, Learning, and Natural Algorithms, PhD Thesis, Dipartimento di Elettronica, Politechnico di Milano (Italy). [2] Dorigo, M., Maniezzo, V., and Colorni, A., 1996, The Ant System:Optimization by a Colony of Cooperating Agents, IEEE Trans. Systems, Man Cybernetics, 26(1), [3] Dorigo, M., Gambardella, L. M., 1997, Ant-colonies for the traveling salesman problem. Bio-Systems, 43(2), [4] Bullnheimer, B., Hartl, R. F. and Strauss, C., 1999, A new rank-based version of the Ant System: A computational study, Central European Journal for Operations Research and Economics, 7(1), [5] Stützle, T. and Hoosa, H., 1997, Improvements on the ant system, introducing the MAX-MIN ant system, Proceedings of ICANNGA97, Third International Conference on Artificial Neural Networks and Genetic Algorithms, University of East Anglia, Norwich, UK. [6] Johnson, D. S. and McGeoch, L. A., 1997, The travelling salesman problem: A case study in local optimization, In E. H. L. Aarts & J. K. Lenstra (Eds.), Local Search in Combinatorial Optimization,Chichester, UK, John Wiley & Sons, [7] Brezina, I.Jr., Čičková, Z., 2011, Solving the Travelling Salesman Problem Using the Ant Colony Optimization Management Information Systems, 6, [8] Ali, H., Haris, M., Hadi, F., Ahmadullah, Salman and Shah, Y., 2016, Solving Traveling Salesman Problem through Optimization Techniques Using Genetic Algorithm and Ant Colony Optimization, J. Appl. Environ. Biol. Sci., 6(4S), [9] Kumar, D. N. and Reddy, M. J., 2006, Ant colony optimization for multipurpose reservoir operation, Water Resources Management, 20, [10] Moeini, R. and Afshar, M. H., 2009, Application of an ant colony optimization algorithm for optimal operation of reservoirs: a comparative study of three proposed formulations, Transaction A: Civil Engineering, 16(4),

5 Ant Systems of Optimization: Introduction and Review of Applications 279 [11] Dariane, A. B. and Moradi, A. M., 2009, Reservoir operating by ant colony optimization for continuous domains (ACOR) case study: Dez reservoir, International Journal of Mathematical, Physical and Engineering Sciences, 3(2), [12] Li, Y., Chan Hilton, A. B. and Tong, L., 2004, Development of ant colony optimization for long-term groundwater monitoring, Proceedings of the 2004 World Water and Environmental Resources Congress, Salt Lake City, UT, [13] Li, S., Liu, Y., and Yu, H., 2006, Parameter estimation approach in groundwater hydrology using hybrid ant colony system, In Computational Intelligence and Bioinformatics, Lecture Notes in Computer Science, 4115/2006, [14] Maier, H. R., Simpson, A. R., Zecchin, A. C., Foong, W. K., Phang, K. Y., Seah, H. Y. and Tan, C. L., 2003, Ant colony optimization for design of water distribution systems, Journal of Water Resources Planning and Management Division, ASCE, 129(3), [15] Saleh, H. A., 2002, Ants Can Successfully Design GPS Surveying Networks, GPS World, 13(9), 50. [16] Liang, Y. C. and Smith, A.E., 1999, An Ant System Approach to Redundancy Allocation, Proc. Cong. Evolutionary Computation (CEC-99), 2, [17] Eggers, J., Feillet, D., Kehl, S., Wagner, M.O. and Yannou, B., 2003, Optimization of the Keyboard Arrangement Problem Using an Ant Colony Algorithm, Eur. J. Operational Res., 148, [18] Rodrigues, A., 2003, Application of Ant Colony Optimization to Data Distribution in Memory in Computer Systems, Proc. VII Annual Swarm Researchers Meeting, Swarm- Fest-2003, Notre Dame, USA. [19] Shmygelska, A., Hoos, H. H., 2005, An ant colony optimization algorithm for the 2D and 3D hydrophobic polar protein folding problem, BMC Bioinformatics, 6,30. [20] Fidanova, S. and Lirkov, I., 2008, Ant Colony approach to Protein Folding, Proceeding of the IMCSI, 3,

6 280 Shipra, Priyanka and Vibha Aggarwal