Abstract
For obtaining superior search performance in particle swarm optimization (PSO), we proposed particle swarm optimization with diversive curiosity (PSO/DC). The mechanism of diversive curiosity in PSO can prevent premature convergence and ensure exploration. To clarify the characteristics of PSO/DC, we estimated the range for appropriate parameter values, and investigated the trade-off between exploration and exploitation. Applications of the proposed method to a two-dimensional multimodal optimization problem and a suite of five-dimensional benchmark problems well demonstrate its effectiveness. Our experimental results basically accord with the findings in psychology, i.e., diversive curiosity being prone to exploration and anxiety.
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Notes
Computing environment: Intel(R) Xeon(TM); CPU 3.40GHz; Memory 2.00GB RAM; Computing tool: Mathematica 5.2; Computing time: about 3 min.
Success means that the fitness value of the best particle is over 0.3950.
References
Berlyne D (1960) Conflict, arousal, and curiosity. McGraw-Hill, New York
Cohen JD, McClure SM, Yu AJ (2007) Should I stay or should I go? How the human brain manages the trade-off between exploitation and exploration. Philos Trans Roy Soc B 362:933–942
Day H (1982) Curiosity and the interested explorer. Perform Instr 21:19–22
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. Nagoya, Japan, pp 39–43
Kaplan F, Oudeyer PY (2006) Curiosity-driven development. In: Proceedings of international workshop on synergistic intelligence dynamics. Genova, Italy, pp 1–8
Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of the 1995 IEEE international conference on neural networks. Piscataway, New Jersey, USA, pp 1942–1948
Kennedy J (2006) In search of the essential particle swarm. In: Proceedings of the 2006 IEEE congress on evolutionary computations. Vancouver, BC, Canada, pp 6158–6165
Loewenstein G (1994) The psychology of curiosity: a review and reinterpretation. Psychol Bull 116(1):75–98
Parsopoulos KE, Vrahatis MN (2002) Recent approaches to global optimization problems through particle swarm optimization. Nat Comput 1(2):235–306
Storn R, Price K (1997) Differential evolution—a simple and efficient heuristic for global optimization over continuous space. J Glob Optim 11(4):341–359
Wohlwill JF (1981) A conceptual analysis of exploratory behavior in advances in intrinsic motivation and aesthetics. Plenum Press, New York
Xiao RB, Xu YC, Amos M (2007) Two hybrid compaction algorithms for the layout optimization problem. Biosystems 90(2):560–567
Zhang H, Ishikawa M (2005) A hybrid real-coded genetic algorithm with local search. In: Proceedings of the 12th international conference on neural information processing (ICONIP2005). Taipei, Taiwan, ROC, pp 732–737
Zhang H, Ishikawa M (2007) Evolutionary particle swarm optimization (EPSO)—estimation of optimal PSO parameters by GA. In: Proceedings of the international multiconference of engineers and computer scientists (IMECS 2007), IAENG, vol 1. Hong Kong, China, pp 13–18
Zhang H, Ishikawa M (2008) Designing particle swarm optimization—performance comparison of two temporally cumulative fitness functions in EPSO. In: Proceedings of the 26th IASTED international conference on artificial intelligence and applications (AIA 2008). Innsbruck, Austria, pp 301–306
Zhang H, Ishikawa M (2008) Improving the performance of particle swarm optimization with diversive curiosity. In: Proceedings of the international multiconference of engineers and computer scientists (IMECS 2008), IAENG, vol 1. Hong Kong, China, pp 1–6
Zhang H, Ishikawa M (2008) Evolutionary particle swarm optimization—metaoptimization method with GA for estimating optimal PSO methods. In: Castillo O et al (eds) Trends in intelligent systems and computer engineering, Lecture Notes in Electrical Engineering, vol 6. Springer, Heidelberg, pp 75–90
Zhang H, Ishikawa M (2008) Characteristics of particle swarm optimization with diversive curiosity. In: Fifth international symposium on neural networks (ISNN2008). Beijing, China, p 10
Acknowledgments
This research was supported by a COE program (#J19) granted to Kyushu Institute of Technology by the Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan. It was also supported by Grant-in-Aid Scientific Research(C)(18500175) from MEXT, Japan.
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Zhang, H., Ishikawa, M. Characterization of particle swarm optimization with diversive curiosity. Neural Comput & Applic 18, 409–415 (2009). https://doi.org/10.1007/s00521-009-0252-4
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DOI: https://doi.org/10.1007/s00521-009-0252-4