Regression line shifting mechanism for analyzing evolutionary optimization algorithms
- 218 Downloads
This work introduces a novel methodology to perform the comparative analysis of evolutionary optimization algorithms. The methodology relies simply on linear regression and quantile–quantile plots. The methodology is extrapolated as the one-to-one comparison, one-to-many comparison and many-to-many comparison of solution quality and convergence rate. Most of the existing approaches utilize both solution quality and convergence rate to perform comparative analysis. However, many-to-many comparison, i.e., ranking of algorithms is done only in terms of solution quality. The proposed method is capable of ranking algorithms in terms of both solution quality and convergence rate. Method is analyzed with well-established algorithms and real data obtained from 25 benchmark functions.
KeywordsEvolutionary optimization algorithms Linear regression Particle swarm optimization Differential evolution Visual analysis
Part of the work has been appeared at ISCBI 2014 (Biswas and Biswas 2014). Proper citations have been included for the same in the above work for the purpose transparency.
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Also informed consent was obtained from all individual participants included in the study.
Human and animal rights
This article does not contain any studies with human participants or animals performed by any of the authors.
- Biswas A, Biswas B (2014) Visual analysis of evolutionary optimization algorithms. In: 2014 2nd International symposium on computational and business intelligence (ISCBI), pp 81–84Google Scholar
- Biswas A, Gupta P, Modi M, Biswas B (2015) An empirical study of some particle swarm optimizer variants for community detection. In: El–Alfy E-SM, Thampi SM, Takagi H, Piramuthu S, Hanne T. (eds) Advances in intelligent informatics. Springer, Berlin, pp 511–520Google Scholar
- Carrano EG, Takahashi RH, Wanner EF (2008) An enhanced statistical approach for evolutionary algorithm comparison. In: Proceedings of the 10th annual conference on genetic and evolutionary computation (GECCO ’08). ACM, New York, NY, USA, pp 897–904Google Scholar
- García S, Molina D, Lozano M, and Herrera F (2007) An experimental study on the use of non-parametric tests for analyzing the behaviour of evolutionary algorithms in optimization problems. In: Proceedings of the Spanish congress on metaheuristics, evolutionary and bioinspired algorithms (MAEB2007), pp 275–285Google Scholar
- He J, Chen T (2013) Novel analysis of population scalability in evolutionary algorithms. CoRR abs/1108.4531. http://arxiv.org/abs/1108.4531
- Lockett A (2013) Measure-theoretic analysis of performance in evolutionary algorithms. In: 2013 IEEE congress on evolutionary computation (CEC), pp 2012–2019Google Scholar
- Lutton E, Fekete J-D (2011) Visual analytics and experimental analysis of evolutionary algorithms, Research Report RR-7605, INRIA. http://hal.inria.fr/inria-00587170
- Mersmann O, Preuss M, Trautmann H (2010) Benchmarking evolutionary algorithms: towards exploratory landscape analysis. Springer, BerlinGoogle Scholar
- Muhlenbein H, Mahnig T (2001) Mathematical analysis of evolutionary algorithms for optimization. In: Proceedings of the third international symposium on adaptive systems. La Havana, pp 166–185Google Scholar
- Moreno-Pérez J, Campos-Rodríguez C, Laguna M (2007) On the comparison of metaheuristics through non-parametric statistical techniques. In: Proceedings of the Spanish congress on metaheuristics, evolutionary and bioinspired algorithms (MAEB2007), pp 286–293Google Scholar
- Qin A, Suganthan P (2005) Self-adaptive differential evolution algorithm for numerical optimization. In: The 2005 IEEE congress on evolutionary computation, vol 2, pp 1785–1791Google Scholar
- Shi Y, Eberhart R (1999) Empirical study of particle swarm optimization, In: Proceedings of the 1999 congress on evolutionary computation, 1999 (CEC 99), vol 3, p 1950Google Scholar
- Suganthan PN, Hansen N, Liang JJ, Deb K, Chen Y-P, Auger A, Tiwari S (2005) Problem definitions and evaluation criteria for the cec 2005 special session on real-parameter optimization. KanGAL report 2005005Google Scholar
- Wu A, De Jong K, Burke D, Grefenstette J, Loggia Ramsey C (1999) Visual analysis of evolutionary algorithms. In: Proceedings of the 1999 congress on evolutionary computation, 1999 (CEC 99), vol 2, p 1425Google Scholar
- Yang X-S (2011) Metaheuristic optimization: algorithm analysis and open problems. In: Pardalos P, Rebennack S (eds) Experimental algorithms, vol 6630, lecture notes in computer science. Springer, Berlin, pp 21–32Google Scholar