CCEO: cultural cognitive evolution optimization algorithm

Cognitive behavior is an indispensable factor in the history of human evolution; through cognitive behavior, human development has evolved from the Stone Age to the present high-tech information era. By simulating the process of cultural evolution, a cultural algorithm can reflect the evolutionary process of society accurately, which can speed up the process of solving problems. In this paper, cognitive behavior evolution and a cultural algorithm are combined to form the cultural cognitive evolution optimization (CCEO) algorithm. A cultural algorithm with the double-layer structure of the belief space and population space is transformed into a three-layer evolution mechanism in a CCEO so that the belief space improves on the next layer of careful guidance of the evolutionary process. Twenty-three benchmark test functions and two engineering problems were used to test the CCEO algorithm, and the results demonstrate that the culture cognitive evolution algorithm has a high convergence speed and calculation precision.

[1]  Yongquan Zhou,et al.  Lévy-Flight Moth-Flame Algorithm for Function Optimization and Engineering Design Problems , 2016 .

[2]  Amir Hossein Gandomi,et al.  Bat algorithm for constrained optimization tasks , 2012, Neural Computing and Applications.

[3]  Yue Shi,et al.  A modified particle swarm optimizer , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[4]  Tapabrata Ray,et al.  Society and civilization: An optimization algorithm based on the simulation of social behavior , 2003, IEEE Trans. Evol. Comput..

[5]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[6]  Min-Yuan Cheng,et al.  Symbiotic Organisms Search: A new metaheuristic optimization algorithm , 2014 .

[7]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[8]  Ardeshir Bahreininejad,et al.  Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems , 2013, Appl. Soft Comput..

[9]  Xin-She Yang,et al.  Cuckoo Search via Lévy flights , 2009, 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC).

[10]  Carlos A. Coello Coello,et al.  Useful Infeasible Solutions in Engineering Optimization with Evolutionary Algorithms , 2005, MICAI.

[11]  Hui Zhao,et al.  3D real-time path planning based on cognitive behavior optimization algorithm for UAV with TLP model , 2018, Cluster Computing.

[12]  Xuesong Yan,et al.  Contaminant source identification of water distribution networks using cultural algorithm , 2017, Concurr. Comput. Pract. Exp..

[13]  Robert G. Reynolds,et al.  CADE: A hybridization of Cultural Algorithm and Differential Evolution for numerical optimization , 2017, Inf. Sci..

[14]  Carlos A. Coello Coello,et al.  Use of a self-adaptive penalty approach for engineering optimization problems , 2000 .

[15]  Xin-She Yang,et al.  Flower Pollination Algorithm for Global Optimization , 2012, UCNC.

[16]  Yuhong Zhou,et al.  A new chaotic hybrid cognitive optimization algorithm , 2018, Cognitive Systems Research.

[17]  Amir Hossein Gandomi,et al.  Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems , 2011, Engineering with Computers.

[18]  Taha Mansouri,et al.  Learning Fuzzy Cognitive Maps with modified asexual reproduction optimisation algorithm , 2019, Knowl. Based Syst..

[19]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[20]  Xuesong Yan,et al.  An improved cultural algorithm and its application in image matching , 2017, Multimedia Tools and Applications.

[21]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[22]  Dervis Karaboga,et al.  Artificial bee colony algorithm for large-scale problems and engineering design optimization , 2012, J. Intell. Manuf..

[23]  R. Reynolds.  On Modeling the Evolution of Hunter‐Gatherer Decision‐Making Systems , 2010 .

[24]  Hui Zhao,et al.  Cognitive behavior optimization algorithm for solving optimization problems , 2016, Appl. Soft Comput..

[25]  Yongquan Zhou,et al.  Elite Opposition-Based Cognitive Behavior Optimization Algorithm for Global Optimization , 2019, J. Intell. Syst..

[26]  Mahamed G. H. Omran.  A novel cultural algorithm for real-parameter optimization , 2016, Int. J. Comput. Math..

[27]  Yafei Huang,et al.  A hybrid cuckoo search algorithm with feasibility-based rule for constrained structural optimization , 2014, Journal of Central South University.

[28]  Robert G. Reynolds,et al.  A balanced fuzzy Cultural Algorithm with a modified Levy flight search for real parameter optimization , 2018, Inf. Sci..

[29]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[30]  Seyed Mohammad Mirjalili,et al.  Multi-Verse Optimizer: a nature-inspired algorithm for global optimization , 2015, Neural Computing and Applications.