Breeding swarms: a GA/PSO hybrid

ABSTRACT

In this paper we propose a novel hybrid (GA/PSO) algorithm, Breeding Swarms, combining the strengths of particle swarm optimization with genetic algorithms. The hybrid algorithm combines the standard velocity and position update rules of PSOs with the ideas of selection, crossover and mutation from GAs. We propose a new crossover operator, Velocity Propelled Averaged Crossover (VPAC), incorporating the PSO velocity vector. The VPAC crossover operator actively disperses the population preventing premature convergence. We compare the hybrid algorithm to both the standard GA and PSO models in evolving solutions to five standard function minimization problems. Results show the algorithm to be highly competitive, often outperforming both the GA and PSO.

REFERENCES

• 1: Peter J. Angeline, Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences, Proceedings of the 7th International Conference on Evolutionary Programming VII, p.601-610, March 25-27, 1998
• 2: M. Clerc. The swarm and the queen: Towards a determininistic and adaptive particle swarm optimization. In Congress on Evolutionary Computation (CEC99), pages 1951--1957, 1999.
• 3: Russell C. Eberhart , Yuhui Shi, Comparison between Genetic Algorithms and Particle Swarm Optimization, Proceedings of the 7th International Conference on Evolutionary Programming VII, p.611-616, March 25-27, 1998
• 4: L. Eshelman and J. Schaffer. Real-coded genetic algorithms and interval-schemata. In L. D. Whitley, editor, Foundations of Genetic Algorithms 2, pages 187--202. Morgan Kaufmann, San Mateo, CA, 1993.
• 5: D. Fogel and H. Beyer. A note on the empirical evaluation of intermediate recombination. Evolutionary Computation, 3(4):491--495, 1996.
• 6: John H. Holland, Adaptation in natural and artificial systems, MIT Press, Cambridge, MA, 1992
• 7: James Kennedy , Russell C. Eberhart, Swarm intelligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, 2001
• 8: M. Lvbjerg, T. Rasmussen, and T. Krink. Hybrid particle swarm optimiser with breeding and subpopulations. In Proceedings of the Genetic and Evolutionary Computation Conference -- GECCO-2001, LNCS. Springer-Verlag, 2001.
• 9: G. Mahinthakumar and M. Sayeed. Hybrid genetic algorithm - local search approaches for groundwater source identification problems. Special Issue on Evolutionary Computation, ASCE Jounal of Water Resources Planning and Management, In Press, 2004.
• 10: J. Robinson, S. Sinton, and Y. Rahmat-Samii. Particle swarm, genetic algorithm, and their hybrids: Optimization of a profiled corrugated horn antenna. In IEEE Antennas and Propagation Society International Symposium and URSI National Radio Science Meeting, San Antonio, TX, 2002.
• 11: M. Settles, B. Rodebaugh, and T. Soule. Comparison of genetic algorithm and particle swarm optimizer when evolving a recurrent neural network. In E. Cantú-Paz and et. al., editors, Genetic and Evolutionary Computation -- GECCO-2003, volume 2723 of LNCS, pages 148--149, Chicago, 12-16 July 2003. Springer-Verlag.
• 12: M. Settles and T. Soule. A hybrid ga/pso to evolve artificial recurrent neural networks. In C. Dagli and et. al., editors, Intelligent Engineering Systems Through Aritificial Neural Networks (ANNIE-2003), volume 13, pages 51--56, St. Louis, 2-5 Nov. 2003. ASME Press.
• 13: Yuhui Shi , Russell C. Eberhart, Parameter Selection in Particle Swarm Optimization, Proceedings of the 7th International Conference on Evolutionary Programming VII, p.591-600, March 25-27, 1998

INDEX TERMS

Primary Classification:
  I. Computing Methodologies
  I.2 ARTIFICIAL INTELLIGENCE
      I.2.8 Problem Solving, Control Methods, and Search
          Subjects: Heuristic methods

General Terms:
Algorithms

Keywords:
breeding swarms, function minimization, genetic algorithms, particle swarm optimization