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A tournament-based competitive-cooperative multiagent architecture for real parameter optimization

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Abstract

Real parameter optimization is an important task in almost all engineering applications. This paper introduces a novel multiagent architecture and agent interaction mechanism for the solution of single objective type real-parameter optimization problems. The proposed multiagent system includes several metaheuristics as problem-solving agents that act on a common population containing the frontiers of search process and a common archive keeping the promising solutions extracted so far. Each session of the proposed architecture includes two phases: a tournament among all agents to determine the currently best performing agent and a search procedure conducted by the winner. In the tournament phase, each agent performs a fixed number of fitness evaluations over the common population and gets a success score in terms the fitness improvements it achieved by itself. The agent with the best score is the winner of the tournament. Then, the winner agent is allowed to conduct its search algorithm using the common population until its procedure gets stuck at a locally optimal solution or maximum fitness evaluations per session is reached. Afterwards, the procedure restarts with another tournament to determine the next winner. In all phases and iterations of the proposed framework, all agents use the same population and archive in conducting their search procedures. This way, agents compete with each other in terms of their fitness improvements achieved over a fixed number of fitness evaluations in tournaments, and they cooperate by sharing their search experiences through accumulating them in a common population and a common archive. The proposed multiagent system is experimentally evaluated using the well-known CEC2005 benchmark problems set. Analysis of the obtained results exhibited that the proposed framework performs significantly better than its state-of-the-art competitors in almost all problem instances.

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References

  • Abuhamdah A (2012) Modified great deluge for medical clustering problems. Int J Emerg Sci 2:345–360

    Google Scholar 

  • Auger A, Hansen N (2005) A restart CMA evolution strategy with increasing population size. In: IEEE congress on evolutionary computation, vol 1. IEEE, Scotland, pp 1769–1776

  • Auger A, Hansen N (2005) Performance evaluation of an advanced local search evolutionary algorithm. In: IEEE congress on evolutionary computation, vol 1. IEEE, Scotland, pp 1777–1784

  • Aydemir FB, Gunay A, Oztoprak F, Birbil SE, Yolum P (2013) Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies. J Glob Optim 57(2):499–519 Springer

    Article  MathSciNet  MATH  Google Scholar 

  • Aydin ME (2013) Coordinating metaheuristic agents with swarm intelligence. J Intell Manufact 23(4):991–999

    Article  MathSciNet  Google Scholar 

  • Ballester PJ, Stephenson J, Carter JN, Gallagher K (2005) Real-parameter optimization performance study on the CEC-2005 benchmark with SPC-PNX. In: IEEE congress on evolutionary computation, vol 1. IEEE, Scotland, pp 498–505

  • Benchmark functions (CEC’2005) (2005) Special session on real parameter optimization, IEEE, Sept. 2–5, UK. http://sci2s.ugr.es/eamhco/cec2005_values.xls. Accessed Aug 2014

  • Bertsimas D, Tsitsiklis J (1993) Simulated annealing. Stat Sci 8(1):10–15

    Article  MATH  Google Scholar 

  • Cadenas JM, Garrido MC, Munoz E (2008) Construction of a cooperative metaheuristic system based on data mining and soft-computing: methodological issues. In: Proceedings of IPMU’08, pp 1246–1253

  • Caruana R, Eshelman LJ, Schaffer JD (1989) Representation and hidden bias II: eliminating defining length bias in genetic search via shuffle crossover: IJCAI, Michigan, pp 750–755

  • Dekkers A, Aarts E (1991) Global optimization and simulated annealing. Math Program Ser A B 50:367–393

    Article  MathSciNet  MATH  Google Scholar 

  • Derrac J, García S, Molina D, Herrera F (2011) A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm Evolut Comput 1:3–18

    Article  Google Scholar 

  • Dorigo D, Prize K, Glover F (1999) An introduction to differential evolution: new ideas in optimization. McGraw-Hill, London

    Google Scholar 

  • Dueck G (1993) New optimization heuristics, the great deluge algorithm and the record-to-record travel. J Comput Phys 104(1):86–92

    Article  MathSciNet  MATH  Google Scholar 

  • Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micro machine and human science, Japan

  • Garcia-Martinez C, Lozano M (2005) Hybrid real-coded genetic algorithms with female and male differentiation. In: IEEE congress on evolutionary computation, vol 1. IEEE, Scotland, pp 896–903

  • Goldberg DE (1989) Genetic algorithms in search, optimization and machine learning. Addison-Wesley Longman Publishing Co., Boston

    MATH  Google Scholar 

  • Haupt RL, Haupt SE (2004) Practical genetic algorithms. Wiley, New Jersey

    MATH  Google Scholar 

  • Kennedy I, Eberhart R (1995) Particle swarm optimization: IEEE international conference on neural networks, pp 1942–1948

  • Kruisselbrink JW (2012) Evolution strategies for robust optimization. In: Leiden institute of advanced computer science (LIACS). Leiden university, Leiden

  • Liang JJ, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer with local search. In: IEEE congress on evolutionary computation, vol 1. IEEE, Scotland, pp 522–528

  • Meignan D, Creput JC, Koukam A (2008) An organizational view of metaheuristics. In: Proceedings of frist international workshop on optimization on multi-agent systems, pp 77–85

  • Milano M, Roli A (2004) MAGMA: a multi-agent architecture for metaheuristics. IEEE Trans Syst Man Cybern 34(2):925–941

    Article  Google Scholar 

  • Molina D, Herrea F, Lozano M (2005) Adaptive local search parameters for real-coded memetic algorithms. In: IEEE congress on evolutionary computation, vol 1. IEEE, Scotland, pp 888–895

  • Panait L, Luke S (2005) Cooperative multi-agent learning: the state of the art. Auton Agents Multi-agent Syst 11(3):387–434

    Article  Google Scholar 

  • Posik P (2005) Real-parameter optimization using the mutation step co-evolution. In: IEEE congress on evolutionary computation, vol 1. IEEE, Scotland, pp 872–879

  • Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. In: IEEE congress on evolutionary computation, vol 1. IEEE, Scotland, pp 1785–1791

  • Ronkkonen J, Hukkonen S, Price KV (2005) Real-parameter optimization with differential evolution. In: IEEE congress on evolutionary computation, vol 1. IEEE, Scotland, pp 506–513

  • Sinha A, Tiwari S, Deb K (2005) A population-based, steady-state procedure for real-parameter optimization. In: IEEE congress on evolutionary computation, vol 1. IEEE, Scotland, pp 514–521

  • Sivanandam SN, Deepa SN (2008) Introduction to genetic algorithms. Springer, Berlin

    MATH  Google Scholar 

  • Stone P, Veloso M (2000) Multiagent systems: a survey from a machine learning perspective. Auton Robot 8

  • Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11(4):341–359

    Article  MathSciNet  MATH  Google Scholar 

  • Stuart R, Norvig P (2003) Artificial intelligence: a modern approach, 2nd edn. chpt. 2, Prentice Hall, Saddle River, pp 0–13. ISBN-790395-2

  • Suganthan PN, Hansen N, Liang JJ, Deb K, Chen, YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. In: Technical report. Nanyang Technological University, Singapore

  • Suganthan PN, Hansen N, Liang JJ, Deb K, Chen YP, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the CEC2005 special session on real-parameter optimization. Nanyang Technological University, Singapore

    Google Scholar 

  • Sycara KP (1998) Multi-agent systems: American association for artificial intelligence. AI Maga 19(2)

  • Taillard ED, Gambardella LM, Gendrau M, Potvin JY (2001) Adaptive memory programming: a unified view of metaheuristics. Eur J Oper Res 135:1–16

    Article  MathSciNet  MATH  Google Scholar 

  • Teodorovic D, Lucic P, Markovic G, Orco MD (2006) Bee colony optimizations: principles and applications. In: Neural network applications in electrical engineering. IEEE, Serbia

  • Tillet J, Rao TM, Sahin F, Rao R (2008) Darwinian particle swarm optimization. University of Rochester, USA

    Google Scholar 

  • Yuan B, Gallagher M (2005) Experimental results for the special session on real-parameter optimization at CEC 2005: a simple, continuous EDA. In: IEEE congress on evolutionary computation, vol 1. IEEE, Scotland, pp 1792–1799

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Authors and Affiliations

  1. Computer Engineering Department, Eastern Mediterranean University, Famagusta, Cyprus

    Nasser Lotfi & Adnan Acan

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  1. Nasser Lotfi
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  2. Adnan Acan
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Correspondence to Adnan Acan.

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Communicated by V. Loia.

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Lotfi, N., Acan, A. A tournament-based competitive-cooperative multiagent architecture for real parameter optimization. Soft Comput 20, 4597–4617 (2016). https://doi.org/10.1007/s00500-015-1768-4

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  • DOI: https://doi.org/10.1007/s00500-015-1768-4

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