Termite life cycle optimizer

被引:66
作者
Minh, Hoang-Le [1 ,2 ]
Sang-To, Thanh [2 ]
Theraulaz, Guy [3 ]
Wahab, Magd Abdel [4 ]
Cuong-Le, Thanh [2 ]
机构
[1] Univ Ghent, Fac Engn & Architecture, Dept Elect Energy Met Mech Construct & Syst, Soete Lab, B-9000 Ghent, Belgium
[2] Ho Chi Minh City Open Univ, Ctr Engn Applicat & Technol Solut, Ho Chi Minh City, Vietnam
[3] Univ Toulouse, Ctr Rech Cognit Anim CRCA, Ctr Biol Integrat CBI, CNRS,UPS, Toulouse, France
[4] Van Lang Univ, Fac Mech Elect & Comp Engn, Sch Engn & Technol, Ho Chi Minh City, Vietnam
关键词
CEC2005; Optimal engineering design; Optimization; Meta-heuristic; Stochastic optimization; Levy flight; Termite life cycle optimizer; PARTICLE SWARM OPTIMIZATION; ENGINEERING OPTIMIZATION; DIFFERENTIAL EVOLUTION; GENETIC ALGORITHMS; FIREFLY ALGORITHM; DESIGN; SEARCH; COLONY; INTELLIGENCE; STRATEGY;
D O I
10.1016/j.eswa.2022.119211
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper introduces a novel bio-inspired meta-heuristic optimization algorithm, named termite life cycle optimizer (TLCO), which is based on both the life cycle of a termite colony and the modulation of movement strategies used by many animal species in nature. Termite colonies are comprised of three distinct castes: the workers, the soldiers and the reproductive termites. Each caste undertakes a set of specific tasks that ensure the growth and survival of the colony. TLCO mimics the activities of these three castes that are implemented in a mathematical model. The model is then used to find the global optimum in classic optimization problems. First, the behaviors of the workers, soldiers and reproductive termites are used to simulate a balance between the tasks of exploration and exploitation. Second, the initial population securely records the information obtained at each iteration and transmits it to workers and soldiers at the next iteration. This process is repeated until the global optimum is found with the smallest error. Besides, a new proposed function combined with Le ' vy flight is used to modulate the movement of termites that increases its flexibility. Thus, TLCO can cover both long distances during the first iterations to improve the convergence rate and shorter distances during the last iterations to enhance the level of accuracy. We then compare the performances of TLCO with other well-known nature-inspired algorithms using 23 classical benchmark functions, CEC2005 benchmark functions, and five real engineering design prob-lems. The results demonstrate the effectiveness and reliability of TLCO in solving these optimization problems. Source codes of TLCO is publicly available at http://goldensolutionrs.com/termite-life-cycle-optimizer.html.
引用
收藏
页数:33
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