A sophisticated PSO based on multi-level adaptation and purposeful detection
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Although particle swarm optimization (PSO) has successfully applied on many global optimization problems, it is prone to premature convergence due to its monotonic and static learning pattern for all individuals. Furthermore, few purposeful operator is proposed to help population jump out of potential local optimum. To address the drawbacks and improve the comprehensive performance of PSO, we propose a sophisticated PSO (SopPSO) based on multi-level adaptation and purposeful detection. In SopPSO, a particle not only updates its learning model according to its fitness landscape, but also periodically re-selects target dimensions that the particle learns from its neighbors. The adaptive strategy applied in multi-level (i.e., individual level and dimension level) endows PSO with a more accurate simulation on emergent collective behaviors. In addition, a tabu detecting and a local searching strategies based on some historical information are proposed to help the population to jump out of local optima and improve the accuracy of solutions, respectively. The extensive experimental results illustrate the effectiveness and efficiency of the proposed strategies. Furthermore, the comparison results between SopPSO and other peer algorithms on different problems verify its favorable performance on unimodal, multimodal and large-scale problems as well as some real applications.
KeywordsParticle swarm optimization Global optimization Multi-level adaptation Tabu detecting strategy Local learning strategy
This studywas funded by the NationalNatural Science Foundation of China (Nos. 61663009, 61602174, 61562028), the National Natural Science Foundation of Jiangxi Province (Nos. 20161BAB202064, 20161BAB212052, 20151BAB207022) and the National Natural Science Foundation of Jiangxi Provincial Department of Education (Nos. GJJ160469, GJJ150496).
Compliance with ethical standards
Conflict of interest
The authors claim that none of the material in the paper has been published or is under consideration for publication elsewhere. And all authors declare that they have no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
- Angeline PJ (1998) Using selection to improve particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation, pp 84–89Google Scholar
- Eberhart R, Shi Y (2000) Comparing inertia weights and constriction factors in particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation, CEC’00, San Diego, CA, USA, pp 84–88Google Scholar
- Higashi H, Iba H (2003) Particle swarm optimization with Gaussian mutation. In: Proceedings of IEEE symposium on swarm intelligence, pp 72–79Google Scholar
- Kennedy J (1999) Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. In: Proceedings of IEEE congress on evolutionary computation, pp 1931–1938Google Scholar
- Kennedy J, Eberhart RC (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on Micro machine and Human Science, pp 39–43Google Scholar
- Kennedy J, Eberhart RC (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural network. pp 1942–1948Google Scholar
- Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: Proceedings of IEEE congress on evolutionary computation, pp 1671–1676Google Scholar
- Latorre A, Muelas S, José-María Peña (2013) Large scale global optimization: experimental results with MOS-based hybrid algorithm. In: Proceedings of IEEE congress on evolutionary computation, pp 2742–2749Google Scholar
- Li C, Yang S, Nguyen TT (2012) A self-learning particle swarm optimizer for global optimization problems. IEEE Trans Syst Man Cybern B Cybern 42(2):627–646Google Scholar
- Liang JJ, Suganthan PN (2005) Dynamic multi-swarm particle swarm optimizer. In: Proceedings of IEEE symposium on swarm intelligence, pp 124–129Google Scholar
- Li X, Tang K, Omidvar M, Yang Z, Qin K (2013) Benchmark functions for the cec’2013 special session and competition on large scale global optimization. Technical report, Evolutionary Computation and Machine Learning Group, RMIT University, AustraliaGoogle Scholar
- Liu J, Tang K (2013) Scaling up covariance matrix adaptation evolution strategy using cooperative coevolution. In: Proceedings of the 14th international conference on intelligent data engineering and automated learning, pp 350–357Google Scholar
- Molina D, Herrera F (2015) Iterative hybrization of DE with local search for the CEC’2015 special session on large scale global optimization. In: Proceedings of IEEE congress on evolutionary computation, pp 1974–1978Google Scholar
- Peram T, Veeramachaneni K, Mohan CK (2003) Fitness-distance-ratio based particle swarm optimization. In: Proceedings of IEEE symposium on swarm intelligence, pp 174–181Google Scholar
- Shi Y, Eberhart RC (1998) A modified particle swarm optimizer. In: Proceedings of IEEE world congress on computational intelligence. pp 68–73Google Scholar
- Shi Y, Eberhart RC (2001) Fuzzy adaptive particle swarm optimization. In: Proceedings of IEEE congress on evolutionary computation, pp 101–106Google Scholar
- Swagatam D, Suganthan PN (2010) Problem definitions and evaluation criteria for the CEC 2011 competition on testing evolutionary algorithm on real world optimization problems. Nanyang Technological Univ., Singapore, Tech. RepGoogle Scholar
- Wei C, He Z, Zhang Y et al. (2002) Swarm directions embedded in fast evolutionary programming. In: Proceedings of IEEE congress on evolutionary computation, pp 1278–1283Google Scholar
- Zhao SZ, Liang JJ, Suganthan PN et al. (2008) Dynamic multi-swarm particle optimizer with local search for large scale global optimization. In: Proceedings of IEEE congress on evolutionary computation, pp 3845–3852Google Scholar
- Zheng YJ, Ling HF, Guan Q (2009) Adaptive parameters for a modified comprehensive learning particle swarm optimizer. Math Probl Eng 68(3):939–955Google Scholar