Abstract
This paper proposes the scale factor local search differential evolution (SFLSDE). The SFLSDE is a differential evolution (DE) based memetic algorithm which employs, within a self-adaptive scheme, two local search algorithms. These local search algorithms aim at detecting a value of the scale factor corresponding to an offspring with a high performance, while the generation is executed. The local search algorithms thus assist in the global search and generate offspring with high performance which are subsequently supposed to promote the generation of enhanced solutions within the evolutionary framework. Despite its simplicity, the proposed algorithm seems to have very good performance on various test problems. Numerical results are shown in order to justify the use of a double local search instead of a single search. In addition, the SFLSDE has been compared with a standard DE and three other modern DE based metaheuristic for a large and varied set of test problems. Numerical results are given for relatively low and high dimensional cases. A statistical analysis of the optimization results has been included in order to compare the results in terms of final solution detected and convergence speed. The efficiency of the proposed algorithm seems to be very high especially for large scale problems and complex fitness landscapes.
Similar content being viewed by others
References
Moscato P, Norman M (1989) A competitive and cooperative approach to complex combinatorial search. Technical Report 790
Moscato P (1989) On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms. Technical Report 826
Krasnogor N, Blackburne B, Burke E, Hirst J (2002) Multimeme algorithms for proteine structure prediction. In: Proceedings of parallel problem solving in nature VII. Lecture notes in computer science springer, Berlin
Krasnogor N (2002) Studies in the theory and design space of memetic algorithms, Ph.D. thesis. University of West England
Ong YS, Keane AJ (2004) Meta-lamarkian learning in memetic algorithms. IEEE Trans Evol Comput 8(2): 99–110
Krasnogor N (2004) Toward robust memetic algorithms. In: Hart WE, Krasnogor N, Smith JE (eds) Recent advances in memetic algorithms. Studies in fuzzines and soft computing. Springer, Berlin, pp 185–207
Ong YS, Lim MH, Zhu N, Wong KW (2006) Classification of adaptive memetic algorithms: a comparative study. IEEE Trans Syst Man Cybern B 36(1): 141–152
Caponio A, Cascella GL, Neri F, Salvatore N, Sumner M (2007) A fast adaptive memetic algorithm for on-line and off-line control design of pmsm drives. IEEE Trans Syst Man Cybern B Memet algorithms 37(1): 28–41
Neri F, Toivanen J, Mäkinen RAE (2007) An adaptive evolutionary algorithm with intelligent mutation local searchers for designing multidrug therapies for HIV. Appl Intell 27: 219–235
Neri F, Toivanen J, Cascella GL, Ong YS (2007) An adaptive multimeme algorithm for designing HIV multidrug therapies. IEEE/ACM Trans Comput Biol Bioinform 4(2): 264–278
Tirronen V, Neri F, Kärkkäinen T, Majava K, Rossi T (2007) A memetic differential evolution in filter design for defect detection in paper production. In: Applications of evolutionary computing. Lectures notes in computer science, 4448. Springer, Berlin, pp 320–329
Tirronen V, Neri F, Kärkkäinen T, Majava K, Rossi T (2008) An enhanced memetic differential evolution in filter design for defect detection in paper production. Evol Comput 16: 529–555
Tang J, Lim MH, Ong YS (2006) Parallel memetic algorithm with selective local search for large scale quadratic assignment problems. Int J Innov Comput Inf Control 2(6): 1399–1416
Tang J, Lim MH, Ong YS (2007) Diversity-adaptive parallel memetic algorithm for solving large scale combinatorial optimization problems. Soft Comput Fusion Found Methodol Appl 11(9): 873–888
Ishibuchi H, Yoshida T, Murata T (2003) Balance between genetic search and local search in memetic algorithms for multiobjective permutation flow shop scheduling. IEEE Trans Evol Comput 7: 204–223
Ishibuchi H, Hitotsuyanagi Y, Nojima Y (2007) An empirical study on the specification of the local search application probability in multiobjective memetic algorithms. In: Proceedings of the IEEE congress on evolutionary computation. September 2007, pp 2788–2795
Krasnogor N, Smith J (2005) A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Trans Evol Comput 9: 474–488
Wolpert D, Macready W (1997) No free lunch theorems for optimization. IEEE Trans Evol Comput 1(1): 67–82
Caponio A, Neri F, Tirronen V (2008) Super-fit control adaptation in memetic differential evolution frameworks. Soft Comput Fusion Found Methodol Appl (in press)
Brest J, Greiner S, Bošković B, Mernik M, Žumer V (2006) Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems. IEEE Trans Evol Comput 10(6): 646–657
Price KV, Storn R, Lampinen J (2005) Differential evolution: a practical approach to global optimization. Springer, Berlin
Lampinen J, Zelinka I (2000) On stagnation of the differential evolution algorithm. In: Oŝmera P (ed) Proceedings of 6th international mendel conference on soft computing, pp 76–83
Storn R, Price K (1995) Differential evolution—a simple and efficient adaptive scheme for global optimization over continuous spaces. Technical Report TR-95-012, ICSI
Qin AK, Suganthan PN (2005) Self-adaptive differential evolution algorithm for numerical optimization. In: Proceedings of the IEEE congress on evolutionary computation. vol 2, pp 1785–1791
Eiben AE, Smith JE (2003) Introduction to evolutionary computation. Springer, Berlin
Storn R, Price K (1997) Differential evolutiona simple and efficient heuristic for global optimization over continuous spaces. J Glob Optim 11: 341–359
Liu J, Lampinen J (2002) A fuzzy adaptive differential evolution algorithm. In: Proceedings of the 17th IEEE region 10th international conference on computer, communications, control and power engineering. vol I, pp 606–611
Price K, Storn R (1997) Differential evolution: a simple evolution strategy for fast optimization. Dr Dobbs J Softw Tools 22(4): 18–24
Zielinski K, Weitkemper P, Laur R, Kammeyer K-D (2006) Parameter study for differential evolution using a power allocation problem including interference cancellation. In: Proceedings of the IEEE congress on evolutionary computation, pp 1857–1864
Gämperle R, Müller SD, Koumoutsakos P (2002) A parameter study for differential evolution. In: Proceedings of the conference in neural networks and applications (NNA), fuzzy sets and fuzzy systems (FSFS) and evolutionary computation (EC), WSEAS, pp 293–298
Liu J, Lampinen J (2002) On setting the control parameter of the differential evolution algorithm. In: Proceedings of the 8th international mendel conference on soft computing, pp 11–18
Ali MM, Törn A (2004) Population set based global optimization algorithms: Some modifications and numerical studies. Comput Oper Res 31: 1703–1725
Rechemberg I (1973) Evolutionstrategie: Optimierung Technisher Systeme nach prinzipien des Biologishen Evolution. Fromman-Hozlboog Verlag
Rahnamayan S, Tizhoosh HR, Salama MM (2008) Opposition-based differential evolution. IEEE Trans Evol Comput 12(1): 64–79
Noman N, Iba H (2008) Accelerating differential evolution using an adaptive local search. IEEE Trans Evol Comput 12(1): 107–125
Tsutsui S, Yamamura M, Higuchi T (1999) Multi-parent recombination with simplex crossover in real coded genetic algorithms. In: Proceedings of the genetic evolution computer conference (GECCO):657–664
Yang Z, Tang K, Yao X (2007) Differential evolution for high-dimensional function optimization. In: Proceedings of the IEEE congress on evolutionary computation, pp 3523–3530
Noman N, Iba H (2005) Enhancing differential evolution performance with local search for high dimensional function optimization. In: Proceedings of the 2005 conference on Genetic and evolutionary computation ACM, pp 967–974
Gao Y, Wang Y-J (2007) A memetic differential evolutionary algorithm for high dimensional functions’ optimization. In: Proceesings of the third international conference on natural computation, pp 188–192
Zamuda A, Brest J, Bošković B, Žumer V (2008) Large scale global optimization using differential evolution with self-adaptation and cooperative co-evolution. In: Proceedings of the IEEE world congress on computational intelligence, pp 3719–3726
Brest J, Žumer V, Maucec M (2006) Self-adaptive differential evolution algorithm in constrained real-parameter optimization. In: Proceedings of the IEEE congress on evolutionary computation, pp 215–222
Lozano M, Herrera F, Krasnogor N, Molina D (2004) Real-coded memetic algorithms with crossover hill-climbing. Evol Comput Memet algorithms 12(3): 273–302
Kiefer J (1953) Sequential minimax search for a maximum. Proc Am Math Soc 4: 502–506
Russell SJ, Norvig P (2003) Artificial intelligence: a modern approach, 2nd edn. Prentice-Hall, Englewood Cliffs, pp 111–114
Hart WE, Krasnogor N, Smith JE (2004) Memetic evolutionary algorithms. In: Hart WE, Krasnogor N, Smith JE (eds) Recent advances in memetic algorithms. Springer, Berlin, pp 3–27
NIST/SEMATECH, e-handbook of statistical methods. http://www.itl.nist.gov/div898/handbook/
Feoktistov V (2006) Differential evolution in search of solutions. Springer, Berlin, pp 83–86
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Neri, F., Tirronen, V. Scale factor local search in differential evolution. Memetic Comp. 1, 153–171 (2009). https://doi.org/10.1007/s12293-009-0008-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12293-009-0008-9