Differential evolution utilizing a handful top superior individuals with bionic bi-population structure for the enhancement of optimization performance

被引:17
作者
Meng, Zhenyu [1 ]
Pan, Jeng-Shyang [1 ,2 ]
Zheng, Wei-min [3 ]
机构
[1] Harbin Inst Technol, Shenzhen Grad Sch, Dept Comp Sci & Technol, Shenzhen, Peoples R China
[2] Fujian Univ Technol, Fujian Prov Key Lab Big Data Min & Applicat, Fuzhou, Peoples R China
[3] Shandong Univ Sci & Technol, Coll Comp Sci & Engn, Qingdao, Peoples R China
关键词
Bionic bi-population structure; differential evolution; global optimization; local optima; top superior individuals; GLOBAL OPTIMIZATION; ALGORITHM; PARAMETERS; MECHANISM; COLONY;
D O I
10.1080/17517575.2018.1491064
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we propose a new DE variant incorporating a new mutation strategy and a bionic bi-population structure to enhance the overall performance of the canonical DE algorithm. As it is known to all that the guidance of a handful top superior elites is usually better than the guidance of only one elite in many cases, therefore, a handful top superior individuals are employed in the newly proposed mutation strategy for individual evolution. Moreover, hybridization of two populations of a certain species can improve the adaptation to the environment, and business and information exchanges between tribes can help their economical and technological development, so a bionic bi-population structure is also employed in the new DE variant for information exchanges. Both of the two aspects make the new algorithm achieve an overall better performance, and experiment results show that a handful top superior individuals with bionic bi-population structure has better performances on optimization accuracy, convergence speed and success rate under these tested benchmark functions especially those other algorithms may be trapped into local optima.
引用
收藏
页码:221 / 242
页数:22
相关论文
共 42 条
[1]  
Ali M., 2000, Optimization in Computational Chemistry and Molecular Biology: Local and Global Approaches, P287
[2]  
[Anonymous], 2009, P 11 ANN C COMP GEN
[3]   Self-adapting control parameters in differential evolution: A comparative study on numerical benchmark problems [J].
Brest, Janez ;
Greiner, Saso ;
Boskovic, Borko ;
Mernik, Marjan ;
Zumer, Vijern .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2006, 10 (06) :646-657
[4]   JADE: adaptive differential evolution with a small population [J].
Brown, Craig ;
Jin, Yaochu ;
Leach, Matthew ;
Hodgson, Martin .
SOFT COMPUTING, 2016, 20 (10) :4111-4120
[5]   A bi-population PSO with a shake-mechanism for solving constrained numerical optimization [J].
Cagnina, Leticia C. ;
Esquivel, Susana C. ;
Coello Coello, Carlos A. .
2007 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION, VOLS 1-10, PROCEEDINGS, 2007, :670-+
[6]  
Chakraborti N, 2001, J PHASE EQUILIB, V22, P525, DOI 10.1007/s11669-001-0069-z
[7]   Recent advances in differential evolution - An updated survey [J].
Das, Swagatam ;
Mullick, Sankha Subhra ;
Suganthan, P. N. .
SWARM AND EVOLUTIONARY COMPUTATION, 2016, 27 :1-30
[8]   Differential Evolution: A Survey of the State-of-the-Art [J].
Das, Swagatam ;
Suganthan, Ponnuthurai Nagaratnam .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2011, 15 (01) :4-31
[9]   Differential Evolution Using a Neighborhood-Based Mutation Operator [J].
Das, Swagatam ;
Abraham, Ajith ;
Chakraborty, Uday K. ;
Konar, Amit .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2009, 13 (03) :526-553
[10]   Ant system: Optimization by a colony of cooperating agents [J].
Dorigo, M ;
Maniezzo, V ;
Colorni, A .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS, 1996, 26 (01) :29-41