ABSTRACT
It is a hard problem to understand the search process of particle swarm optimization over high-dimensional domain. The visualization depicts the total search process and then it will allow better understanding of how to tune the algorithm. For the investigation, we adopt Sammon's mapping, which is a well-known distance-preserving mapping. We demonstrate the usefulness of the proposed methodology by applying it to some function optimization problems.
- R. Burke, S. Gustafson, and G. Kendall. A survey and analysis of diversity measures in genetic programming. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 716--723, 2002. Google ScholarDigital Library
- M. Clerc. The swarm and the queen: towards a deterministic and adaptive particle swarm optimization. In Proceedings of the IEEE Congress on Evolutionary Computation, pages 1951--1957, 1999.Google ScholarCross Ref
- T. D. Collins. Genotypic-space mapping: Population visualization for genetic algorithms. The Knowledge Media Institute, The Open University, Milton Keynes, UK, Technical Report KMI-TR-39, 30th September 1996.Google Scholar
- D. De Ridder and R. Duin. Sammon's mapping using neural networks: a comparison. Pattern Recognition Letters, 18(11-13):1307--1316, 1997. Google ScholarDigital Library
- R. Dybowski, T. Collins, and P. Weller. Visualization of binary string convergence by Sammon mapping. In Proceedings of the Fifth Annual Conference on Evolutionary Programming, pages 377--383, 1996.Google Scholar
- W. Dzwinel. How to make Sammon mapping useful for multidimensional data structures analysis. Pattern Recognition, 27(7):949--959, 1994.Google ScholarCross Ref
- R. C. Eberhart and Y. Shi. Comparison between genetic algorithms and particle swarm optimization. In Proceedings of the 7th International Conference on Evolutionary Programming VII, pages 611--616, 1998. Google ScholarDigital Library
- R. C. Eberhart and Y. Shi. Particle swarm optimization: Developments, applications and resources. In Proceedings of the IEEE International Conference on Evolutionary Computation, pages 81--86, 2001.Google ScholarCross Ref
- R. C. Eberhart, Y. Shi, and J. Kennedy. Swarm Intelligence (The Morgan Kaufmann Series in Artificial Intelligence). Morgan Kaufmann, 2001.Google Scholar
- E. Hart and P. Ross. GAVEL - a new tool for genetic algorithm visualization. IEEE Transactions on Evolutionary Computation, 5(4):335--348, 2001. Google ScholarDigital Library
- N. Higashi and H. Iba. Particle swarm optimization with Gaussian mutation. In Proceedings the IEEE Swarm Intelligence Symposium, pages 72--79, 2003.Google ScholarCross Ref
- J. Kennedy and R. C. Eberhart. Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks, pages 1942--1948, 1995.Google ScholarCross Ref
- J. Kennedy and W. M. Spears. Matching algorithms to problems: An experimental test of the particle swarm and some genetic algorithms on the multimodal problem generator. In Proceedings of the IEEE International Conference on Evolutionary Computation, pages 78--83, 1998.Google ScholarCross Ref
- Y.-H. Kim and B.-R. Moon. New usage of Sammon's mapping for genetic visualization. In Proceedings of the Genetic and Evolutionary Computation Conference, volume 1, pages 1136--1147, 2003. Google ScholarDigital Library
- B. Liua, L. Wanga, Y.-H. Jina, F. Tangb, and D.-X. Huanga. Improved particle swarm optimization combined with chaos. Chaos, Solitons&Fractals, 25(5):1261--1271, 2005.Google ScholarCross Ref
- R. W. Morrison and K. A. D. Jong. Measurement of population diversity. In Proceedings of the 5th European Conference on Artificial Evolution, pages 31--41, 2001. Google ScholarDigital Library
- E. Pekalska, D. De Ridder, R. Duin, and M. Kraaijveld. A new method of generalizing Sammon mapping with application to algorithm speed-up. In Proceedings of the Fifth Annual Conference of the Advanced School for Computing and Imaging, pages 221--228, 1999.Google Scholar
- H. Pohlheim. Visualization of evolutionary algorithms - set of standard techniques and multidimensional visualization. In Proceedings of the Genetic and Evolutionary Computation Conference, pages 533--540, 1999.Google Scholar
- J. W. Sammon, Jr. A non-linear mapping for data structure analysis. IEEE Transactions on Computers, 18:401--409, 1969. Google ScholarDigital Library
- B. R. Secrest and G. B. Lamont. Visualizing particle swarm optimization - Gaussian particle swarm optimization. In Proceedings of the IEEE Swarm Intelligence Symposium, pages 198--204, 2003.Google ScholarCross Ref
- Y. Shi and R. C. Eberhart. A modified particle swarm optimizer. In Proceedings of the IEEE International Conference on Evolutionary Computation, pages 69--73, 1998.Google ScholarCross Ref
- Y. Shi and R. C. Eberhart. Parameter selection in particle swarm optimization. In Proceedings of the 7th International Conference on Evolutionary Programming, pages 591--600, 1998. Google ScholarDigital Library
- C. Solnon and S. Fenet. A study of ACO capabilities for solving the maximum clique problem. Journal of Heuristics, 12(3):155--180, 2006. Google ScholarDigital Library
- S. Tsutsui and D. E. Goldberg. Search space boundary extension method in real-coded genetic algorithms. Information Sciences, 133(3-4):229--247, 2001. Google ScholarDigital Library
- Z. Wu and J. Zhou. A self-adaptive particle swarm optimization algorithm with individual coefficients adjustment. In Proceedings the International Conference on Computational Intelligence and Security, pages 133--136, 2007. Google ScholarDigital Library
Index Terms
- Visualizing the search process of particle swarm optimization
Recommendations
An improved cooperative quantum-behaved particle swarm optimization
Particle swarm optimization (PSO) is a population-based stochastic optimization. Its parameters are easy to control, and it operates easily. But, the particle swarm optimization is a local convergence algorithm. Quantum-behaved particle swarm ...
An Improved Particle Swarm Algorithm for Search Optimization
GCIS '09: Proceedings of the 2009 WRI Global Congress on Intelligent Systems - Volume 01To address the problem of space locus searching, a slowdown particle swarm optimization (SPSO) is proposed to improve the convergence performance of particle swarm from the position viewpoint. The particle swarm in SPSO is divided into many independent ...
An enhanced particle swarm optimization with levy flight for global optimization
Enhanced PSO with levy flight.Random walk of the particles.High convergence rate.Provides solution accuracy and robust. Hüseyin Haklı and Harun Uguz (2014) proposed a novel approach for global function optimization using particle swarm optimization with ...
Comments