Bio-inspired computation: Where we stand and what's next

被引:438
作者
Del Ser, Javier [1 ,2 ,3 ]
Osaba, Eneko [2 ]
Molina, Daniel [4 ]
Yang, Xin-She [5 ]
Salcedo-Sanz, Sancho [6 ]
Camacho, David [7 ]
Das, Swagatam [8 ]
Suganthan, Ponnuthurai N. [10 ]
Coello Coello, Carlos A. [9 ]
Herrera, Francisco [4 ]
机构
[1] Univ Basque Country UPV EHU, Alameda Urquijo S-N, Bilbao 48013, Bizkaia, Spain
[2] TECNALIA, Derio 48160, Spain
[3] BCAM, Bilbao 48009, Spain
[4] Univ Granada, DaSCI Andalusian Inst Data Sci & Computat Intelli, E-18071 Granada, Spain
[5] Middlesex Univ London, London NW4 4BT, England
[6] Univ Alcala, Alcala De Henares 28871, Spain
[7] Univ Autonoma Madrid, E-28049 Madrid, Spain
[8] Indian Stat Inst, Elect & Commun Sci Unit, Kolkata 700108, India
[9] IPN, CINVESTAV, Mexico City 07360, DF, Mexico
[10] Nanyang Technol Univ, Singapore 639798, Singapore
关键词
Bio-inspired computation; Evolutionary computation; Swarm intelligence; Nature-inspired computation; Dynamic optimization; Multi-objective optimization; Many-objective optimization; Multi-modal optimization; Large-scale global optimization; Topologies; Ensembles; Hyper-heuristics; Surrogate model assisted optimization; Computationally expensive optimization; Distributed evolutionary computation; Memetic algorithms; Parameter tuning; Parameter adaptation; Benchmarks; MULTIOBJECTIVE EVOLUTIONARY ALGORITHM; PARTICLE SWARM OPTIMIZATION; MANY-OBJECTIVE OPTIMIZATION; OF-THE-ART; DIFFERENTIAL EVOLUTION; MULTIMODAL OPTIMIZATION; GLOBAL OPTIMIZATION; MEMETIC ALGORITHMS; GENETIC ALGORITHM; HYPER-HEURISTICS;
D O I
10.1016/j.swevo.2019.04.008
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In recent years, the research community has witnessed an explosion of literature dealing with the mimicking of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques.
引用
收藏
页码:220 / 250
页数:31
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