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CACO : Competitive Ant Colony Optimization, A Nature-Inspired Metaheuristic For Large-Scale Global Optimization

12/14/2013
by   M. A. El-Dosuky, et al.
0

Large-scale problems are nonlinear problems that need metaheuristics, or global optimization algorithms. This paper reviews nature-inspired metaheuristics, then it introduces a framework named Competitive Ant Colony Optimization inspired by the chemical communications among insects. Then a case study is presented to investigate the proposed framework for large-scale global optimization.

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1 Introduction

Large-scale problems are nonlinear problems that need metaheuristics, or global optimization algorithms (Deb1995 ,Glover1986 ).

This paper reviews nature-inspired metaheuristics, then it introduces a framework named Competitive Ant Colony Optimization inspired by the chemical communications among insects. Then a case study is presented to investigate the proposed framework. Finally, the paper concludes with a discussion of future works.

2 Nature-Inspired Metaheuristics

Large-scale problems are nonlinear problems that need metaheuristics, or global optimization algorithms (Deb1995 ,Glover1986 ). Many nature-inspired metaheuristic optimization algorithms are proposed to imitate the best behaviors in nature Yang2008 such as the artificial immune system Farmer1986

, genetic algorithms

Goldberg1989 , ant colony optimization (ACO) Dorigo1992

, particle swarm optimization (PSO)

Kennedy1995 , Artificial Bee Colony Algorithm (ABC) Karaboga2005 , and cuckoo search Yang2009 . Recently, many nature inspired algorithms are proposed(Eldosuky1 , Eldosuky2 , Eldosuky3 , Eldosuky4 ). A metaheuristic explores the search space by employing two components of intensification and diversification (Zapfel2010 , Gazi2004 ). Intensification strategy focuses on examining neighbors of elite solutions while diversification strategy encourages examining unvisited regions Glover1997 . Intensification is a deterministic component and diversification is a stochastic component Hoos2005 . Metaheuristic algorithms should be designed so that intensification and diversification play balanced roles Blum2003 .

2.1 Ant colony optimization (ACO)

Ant colony optimization (ACO) generates artificial ants that move on the problem graph depositing artificial pheromone so that the future artificial ants can build better solutions (Dorigo1992 , Dorigo2005 ). ACO has been successfully applied to an impressive number of optimization problems especially for routing and scheduling problems (Santos2010 , Bell2004 ).

ACO proves reliability in large-scale applications such as large-distorted fingerprint matching Cao2012 and solving the logistics problem arising in disaster relief activitiesYi2007 .

3 Competitive Ant Colony Optimization

It is probably safe to say that insects rely more heavily on chemical signals than on any other form of communication. These signals, often called semiochemicals or infochemicals, serve as a form of language that helps to mediate interactions between organisms. Insects may be highly sensitive to low concentrations of these chemicals in some cases, a few molecules may be enough to elicit a response. Semiochemicals can be divided into Pheromones and Allelochemicals based on who sends a message and who receives it

chemcomm . Pheromones are chemical signals that carry information from one individual to another member of the same species. These include sex attractants, trail marking compounds, alarm substances, and many other intraspecific messages. Allelochemicals are signals that travel from one animal to some member of a different species. These include defensive signals such as repellents, compounds used to locate suitable host plants, and a vast array of other substances that regulate interspecific behaviors.

Allelochemicals can be further subdivided into three groups based on who benefits from the message:

Allomones

benefit the sender such as a repellent, or defensive compound (e.g. cyanide) that deters predation..

Kairomones

benefit the receiver – such as an odor that a parasite uses to find its host.

Synomones

benefit both sender and receiver – such as plant volatiles that attract insect pollinators..

The diffusion equation of chemical signals is defined asHapp1974 :

where Q, D, and K are emission rate, diffusion coefficient, and threshold concentration, respectively, and where r is the radius of the active space (cm), t is the time from the beginning of emission, and where efrc(x) is the complementary error function.

3.1 Proposed Metaheuristic

In their search for food, ants use pheromones to communicate. Assuming there is a natural battle between ants and their enemy that produces allomones. The enemy can be other incest species or ants of different kind. Let assume that the enemy is a group of wasps. Based upon these assumptions, let us propose the following scenario.

First, Ants and Wasps are two groups of insects, competing in the same environment to search for food. Each group behaves like traditional ACO algorithm. Second, within the same group, communication is done using pheromones. Communication between the two groups is done using allomones. Third, Wasps can kill ants if they are close enough.

3.2 Implementation

Implementation of this modification is done in Microsoft Visual C# . The code listing is shown below.

    class Wasp : ACO.ACO
    {
        public Wasp(Dataset data, float evapore, float aging, float limit, bool useOptimize)
            : base(data, evapore, aging, limit, useOptimize)
        {
        }
        public override Graph Optimize(params float[] parameters)
        {
            return this.ThisGraph;
        }
    }
    class Ant : ACO.ACO
    {
        Wasp wasps;
        public Ant(Wasp wasps, Dataset data, float evapore, float aging, float limit, bool useOptimize)
            : base(data, evapore, aging, limit, useOptimize)
        {
            this.Optimize(1, 1, 1);
            this.wasps = wasps;
        }
        public override Graph Optimize(params float[] parameters)
        {
            Graph g = new Graph(this);
            float r = parameters[0];
            float d = parameters[1];
            float q = parameters[2];
            int k;
            double sum = 0;
            int iterations = 1000;
            for (int t = 1; t < iterations; t++)
            {
                sum = q / (2*Math.PI*r) * r / (Math.Sqrt(4*d*t));
                k =(int) sum * iterations;
               efrc( new Graph(this.wasps) , this.ThisGraph);
            }
            return this.ThisGraph;
        }
        public void efrc(Graph w, Graph a)
        {
            w.Complement(a);
        }
    }
    class Program
    {
        static void Main(string[] args)
        {
            Dataset d1 = Dataset.Load(Datasets.Audiology);
            Dataset d2 = Dataset.Load(Datasets.BreastCancer);
            Dataset d3 = Dataset.Load(Datasets.Mushroom);
            Dataset d4 = Dataset.Load(Datasets.Vote);
            Dataset d5 = Dataset.Load(Datasets.Wine);
            work(d1);
            work(d2);
            work(d3);
            work(d4);
            work(d5);
            Console.ReadKey();
        }
        static void work(Dataset dataset)
        {
            Wasp wasps = new Wasp(dataset, 0.2F, 1.0F, 1.0F, false);
            Ant ants = new Ant(wasps, dataset, 0.2F, 1.0F, 1.0F, true);
            Console.WriteLine(string.Join("\t", new string[]
                {
                dataset.name ,
                dataset.size.ToString(),
                dataset.feats.ToString(),
                wasps.feats.ToString(),
                ants.feats.ToString() }));
        }
    }

4 Evaluation

Experiments are carried out on five datasets which are all from UCI datasets (http://archive.ics.uci.edu/ml/datasets.html). In order to find whether our algorithm could find an optimal reduct, we compare algorithm of with traditional method . The experiments are summarized in Table 1.

Dataset Instants Features ACO Proposed CACO
Audiology 200 70 20 12
Breast Cancer 699 10 4 4
Mushroom 8124 23 6 5
Wine 178 14 6 5
Vote 435 17 12 10
Table 1: Comparison

5 Conclusion

This paper reviews Ant Colony optimization algorithms and describes a new heuristic optimization method based on swarm intelligence. It presents a mechanism for enhancing Ant colony optimization by introducing the natural battle between ants and their enemy that produces Allomones. It is very simple, easily implemented and it needs fewer parameters, which made it fully developed and applied for feature extraction task

References

  • [1] Ackley, D.H., Connectionist machine for genetic hill climbing, Kluwer, Boston, 1987.
  • [2] G. M. Happ, Chemical signals among animals: Allomones and pheromones, In: Humoral Control of Growth and Differentiation, Nonvertebrate Neuroendocrinology and Aging, Vol. II (J. LoBue and A. S. Gordon, eds.), pp. 149-190. Academic Press,NY. 1974.
  • [3] Arora, J., Introduction to Optimum Design, McGraw-Hill,1989
  • [4]

    Back, T., Schwefel, H.P., An overview of evolutionary algorithms for parameter optimization, Evolutionary Computation, Vol. 1, No. 1, PP 1-23, 1993.

  • [5] Back, T., Michalewiccz, Z., Test landscapes, In Back, T., Fogel, D.B., Michalewicz, Z., (Ed): Handbook of Evolutionary Computation, Chapter B2.7, PP 14-20, Institute of Physics Publishing and Oxford University Press, New York, 1997.
  • [6] Bartumeus, F. et al. Optimizing the encounter rate in biological interactions: Levy versus Brownian strategies. Phys. Rev. Lett. 88, 097901 , 2002.
  • [7] Bartumeus, F., et al., J. Animal search strategies: a quantitative random-walk analysis. Ecology 86, 3078-3087 2005.
  • [8]

    Blum, C. and Roli, A., ’Metaheuristics in combinatorial optimization: Overview and conceptural comparision’, ACM Comput. Surv., 35, 268-308, 2003

  • [9] De Jong, K. A., An analysis of the behavior of a class of genetic adaptive systems, Doctoral dissertation, University of Michigan, Ann Arbor, University Microfilms No 76-9381, 1975.
  • [10] Deb, K., Optimisation for Engineering Design, Prentice-Hall, New Delhi.1995.
  • [11] Dorigo, M,. Optimization, Learning and Natural Algorithms (Phd Thesis). Politecnico di Milano, Italie, 1992
  • [12]

    Farmer, J.D.; Packard, N.; Perelson, A., The immune system, adaptation and machine learning. Physica D 22 (1-3): 187-204. 1986

  • [13] Feduccia, Alan, The Bony Stapes in the Upupidae and Phoeniculidae: Evidence for Common Ancestry. The Wilson Bulletin 87 (3): 416-417, 1975.
  • [14] Gazi, K., and Passino, K. M., Stability analysis of social foraging swarms, IEEE Trans. Sys. Man. Cyber. Part B - Cybernetics, 34, 539-557, 2004.
  • [15]

    Glover, F. Future Paths for Integer Programming and Links to Artificial Intelligence. Computers and Operations Research 13 (5): 533-549, 1986

  • [16] Glover, F. and M. Laguna. Tabu Search. Kluwer Academic Publishers, 1997.
  • [17] Goldberg, D. E., Genetic Algorithms in Search, Optimisation and Ma- chine Learning, Reading, Mass., Addison Wesley, 1989..
  • [18] Hackett, Shannon J.; et al, ”A Phylogenomic Study of Birds Reveals Their Evolutionary History”. Science 320 (1763): 1763-1768, 2008
  • [19] Hoffmeister, F., Back. T., Genetic algorithms and Evolution strategies: Similarities and differences, In Shwefel, H-P., Manner. R., Parallel Problem Solving from Nature- PPSN 1 (Lecture Note in Computer Science; Vol. 496), Springer Verlag, Berlin, 1991.
  • [20] Hoos, H. H., T. Stützle. Stochastic Local Search. Morgan Kaufmann, 2005.
  • [21] Karaboga, D., An Idea Based On Honey Bee Swarm For Numerical Numerical Optimization. Technical Report-TR06 (Erciyes University, Engineering Faculty, Computer Engineering Department), 2005
  • [22]

    Kennedy, J.; Eberhart, R. Particle Swarm Optimization. Proceedings of IEEE International Conference on Neural Networks. IV. pp. 1942-1948, 1995

  • [23] Kristin, A. Family Upupidae (Hoopoes). In Josep, del Hoyo; Andrew, Elliott; Sargatal, Jordi. Handbook of the Birds of the World. Volume 6, Mousebirds to Hornbills. Barcelona: Lynx Edicions. pp. 396-411, 2001
  • [24] LaGrega, Michael D.; Buckingham, Phillip L.; and Evans, Jeffrey C. Hazardous Waste Management, 2nd edition. New York: McGraw-Hill, 2001
  • [25] Pavlyukevich, I.,. Levy flights, non-local search and simulated annealing, J. Computational Physics, 226, 1830-1844, 2007
  • [26] Rajabioun, R., Cuckoo Optimization Algorithm, Applied Soft Computing, Volume 11, Issue 8, Pages 5508-5518, 2011
  • [27] Rosenbrock, H.H., An Automatic method for finding the greatest or least value of a function, The Computer Journal, Vol. 3, No.3, PP 175-184, 1960.
  • [28] Schoen, F., A wide class of test functions for global optimization, J. Global Optimization, 3, 133-137, 1993
  • [29] Shang, Y. W., Qiu Y. H., A note on the extended rosenrbock function, Evolutionary Computation, 14, 119-126., 2006
  • [30] Torn, A., Zilinskas, A., Global Optimization, Lecture Note in Computer Science; Vol. 350, Springer Verlarg, Barlin, 1989.
  • [31] Viswanathan, G. M. et al. Optimizing the success of random searches. Nature 401, 911-914 ,1999.
  • [32] Walton, S., Hassan, O., Morgan, K., Brown. M.R., Modified cuckoo search: A new gradient free optimisation algorithm, Chaos, Solitons and Fractals, Volume 44, Issue 9, Pages 710-718, 2011
  • [33] Whitley, L. D., Mathias, K.E., Rana, S., Dzubera, J., Building better test functions, In Eshelman, L.J. (Ed), Proceeding of the Sixth International Conference on Genetic Algorithms, PP 239-246, Morgan Kaufmann, California, 1995.
  • [34] Yang, X. S., Nature-Inspired Metaheuristic Algorithms, Luniver Press, 2008
  • [35] Yang, X.-S.; Deb, S. Cuckoo search via Levy flights, in: World Congress on Nature and Biologically Inspired Computing (NaBIC 2009).. IEEE Publication, USA. pp. 210-214. 2009
  • [36] Zäpfel, G., Roland Braune, Michael Bögl, Metaheuristic Search Concepts: A Tutorial with Applications to Production and Logistics Books. Springer; 2010
  • [37] Leslie Lamport, A Document Preparation System. Addison Wesley, Massachusetts, 2nd Edition, 1994.
  • [38] aSusan E. Brennan and Eric A. Hulteen, Interaction and feedback in a spoken language system: A theoretical framework. Knowledge-Based Systems, 1995, volume 8, pages 143-151, issue 2.
  • [39] H. Clark and D. Wilkes-Gibbs, Referring as a collaborative process. Cognition, 1986, volume 22, pages 1-39, number 1.
  • [40] Herbert H. Clark, Using Language, Cambridge University Press, 1996.
  • [41] Herbert H. Clark and Meredyth A. Krych, Speaking while monitoring addressees for understanding, Journal of Memory and Language, 2004, volume 50, pages 62-81.
  • [42] Catherine Durnell Cramton, The Mutual Knowledge Problem and Its Consequences for Dispersed Collaboration, Organization Science, 2001, volume 12, pages 346-371, number 3.
  • [43] Darren Gergle and Robert E. Kraut and Susan R. Fussell, Language Efficiency and Visual Technology: Minimizing Collaborative Effort with Visual Information, Journal of Language and Social Psychology, 2004, volume 23, pages 491-517, number 4.
  • [44] Ellen A. Issacs and Herbert H. Clark, References in Conversation Between Experts and Novices, Journal of Experimental Psychology: General, 1987, volume 116, pages 26-37, number 1.
  • [45] Helen Keller, The Story of My Life, Dover Thrift, 1996.
  • [46] Sara Kiesler, Fostering Common Ground in Human-Robot Interaction, Proceedings of the IEEE International Workshop on Robots and Human Interactive Communication (RO-MAN), 2005, pages 729-734.
  • [47] R. M. Krauss and Susan R. Fussell, Social psychological models of interpersonal communication, Social psychology: Handbook of basic principles, Guilford Press, 1996, editors E. T. Higgins and A. Kruglanski, pages 655-701.
  • [48] Robert E. Kraut and Susan R. Fussell and Jane Siegel, Visual Information as a Conversational Resource in Collaborative Physical Tasks, Human-Computer Interaction, 2003, volume 18, pages 13-49.
  • [49] Shuyin Li and Britta Wrede and Gerhard Sagerer, A computational model of multi-modal grounding, Proceedings of the ACL SIGdial Workshop on Discourse and Dialog, in conjunction with COLING/ACL 2006, year 2006, pages 153-160, ACL Press.
  • [50] Tim Paek and Eric Horvitz, Uncertainty, utility, and misunderstanding: A decision-theoretic perspective on grounding in conversational systems, Psychological models of communication in collaborative systems, Papers from the AAAI Fall Symposium, November 5-7, North Falmouth, Massachusett, 1999, pages 85-92.
  • [51] Aaron Powers and Adam D. I. Kramer and Shirlene Lim and Jean Kuo and S. Lee and Sara Kiesler, Eliciting Information from People with a Gendered Humanoid Robot, Proceedings of the IEEE International Workshop on Robots and Human Interactive Communication (RO-MAN), 2005, pages 158-163.
  • [52] Abigail J. Sellen, Speech Patterns in Video-Mediated Conversations, Proceedings of the 1992 SIGCHI Conference on Human Factors in Computing Systems (CHI 1992), 1992, pages 49-59, ACM.
  • [53] Kerstin Severinson-Eklundh and Helge Huttenrauch and Anders Green, Social and collaborative aspects of interaction with a service robot., Robotics and Autonomous Systems, Special Issue on Socially Interactive Robots, 2003, volume 42, number 3.
  • [54] Kristen Stubbs and Pamela Hinds and David Wettergreen, Autonomy and Common Ground in Human-Robot Interaction: A Field Study, IEEE Intelligent Systems, Special Issue on Interacting with Autonomy, 2007, volume 22, pages 42-50, number 2.
  • [55] Kristen Stubbs and Pamela Hinds and David Wettergreen, Challenges to Grounding in Human-Robot Interaction: Sources of Errors and Miscommunications in Remote Exploration Robotics, Proceedings of the First International Conference on Human-Robot Interaction, ACM, 2006.
  • [56] Kristen Stubbs and Pamela Hinds and David Wettergreen, Challenges to Grounding in Human-Robot Collaboration: Errors and Miscommunications in Remote Exploration Robotics, Carnegie Mellon University , 2006.
  • [57] Cristen Torrey and Aaron Powers and Matthew Marge and Susan R. Fussell and Sara Kiesler, Effects of Adaptive Robot Dialogue on Information Exchange and Social Relations, Proceedings of the First Annual Conference on Human-Robot Interaction, pages 126-133, ACM, 2006.
  • [58] Veloso, Manuela M., Entertainment robotics, Commun. ACM, volume 45, number 3, 2002, pages 59-63.
  • [59] Parasuraman, R. and Sheridan, T. B. and Wickens, C. D., A model for types and levels of human interaction with automation, Trans. Sys. Man Cyber. Part A, volume 30, number 3, 2000, pages 286-297.
  • [60] Sinno Jialin Pan and Qiang Yang,

    A Survey on Transfer Learning

    , Knowledge and Data Engineering, IEEE Transactions on, 2010, volume 22, number 10, pages 1345-1359.
  • [61] http://www.cals.ncsu.edu/course/ent425/tutorial/Communication/chemcomm.html
  • [62] Wei Yi, Arun Kumar, Ant colony optimization for disaster relief operations, Transportation Research Part E: Logistics and Transportation Review, Volume 43, Issue 6, November 2007, Pages 660-672
  • [63]

    Kai Cao, Xin Yang, Xinjian Chen, Yali Zang, Jimin Liang, Jie Tian, A novel ant colony optimization algorithm for large-distorted fingerprint matching, Pattern Recognition, Volume 45, Issue 1, January 2012, Pages 151-161

  • [64] Marco Dorigo, Christian Blum, Ant colony optimization theory: A survey, Theoretical Computer Science, Volume 344, Issues 2 3, 17 November 2005, Pages 243-278
  • [65] Luís Santos, João Coutinho-Rodrigues, John R. Current, An improved ant colony optimization based algorithm for the capacitated arc routing problem, Transportation Research Part B: Methodological, Volume 44, Issue 2, February 2010, Pages 246-266
  • [66] John E. Bell, Patrick R. McMullen, Ant colony optimization techniques for the vehicle routing problem, Advanced Engineering Informatics, Volume 18, Issue 1, January 2004, Pages 41-48
  • [67] M. A. El-Dosuky, Ahmed El-Bassiouny, Taher Hamza, Magdy Rashad, New Heuristics for Interfacing Human Motor System using Brain Waves, CoRR abs/1211.6411 (2012).
  • [68] M. A. El-Dosuky, Ahmed El-Bassiouny, Taher Hamza, Magdy Rashad, Obesity Heuristic, New Way On Artificial Immune Systems, CoRR abs/1211.6409 (2012)
  • [69] M. A. El-Dosuky, Ahmed El-Bassiouny, Taher Hamza, Magdy Rashad, New Hoopoe Heuristic Optimization, CoRR abs/1211.6410 (2012)
  • [70] M. A. El-Dosuky, M. Z. Rashad, T. T. Hamza, A. H. El-Bassiouny, Collaborating Robotics Using Nature-Inspired Meta-Heuristics, CoRR abs/1212.5777 (2012)
  • [71] M. A. El-Dosuky, Ahmed EL-Bassiouny, Taher Hamza, Magdy Rashad, ,Food Recommendation Using Ontology and Heuristics, A. Ell Hassanien et al. (Eds.): AMLTA 2012, CCIS 322, pp. 423-429, 2012. Springer-Verlag Berlin Heidelberg 2012