Computer Science ›› 2020, Vol. 47 ›› Issue (1): 219-230.doi: 10.11896/jsjkx.181102165

• Artificial Intelligence • Previous Articles     Next Articles

Improved Cuckoo Search Algorithm for Function Optimization Problems

LI Yu1,SHANG Zhi-yong2,LIU Jing-sen3   

  1. (Institute of Management Science and Engineering,Business School of Henan University,Kaifeng,Henan 475004,China)1;
    (Business School of Henan University,Kaifeng,Henan 475004,China)2;
    (Institute of Complex Intelligent Network System,Henan University,Kaifeng,Henan 475004,China)3
  • Received:2018-11-23 Published:2020-01-19
  • About author:LI Yu,born in 1969,Ph.D,professor.Her main research interests include intelligent algorithm,logistics management and e-commerce.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (71601071),Humanities and Social Sciences Youth Fund of the Ministry of Education (15YJC630079),Special Research and Development and Promotion Project of Henan Province (182102310886) and Key Project of Henan Province Science and Technology (162102110109).

Abstract: In engineering optimization,most problems are continuous optimization problems,that is function optimization problems.Aiming at the problems of slow convergence speed,low precision and easy to fall into local optimization in the later stage of Cuckoo algorithm,this paper proposed an improved Cuckoo search algorithm based on the logarithmic decline of nonlinear inertial weights and the random adjustment discovery probability.Firstly,in the update formula of the path and position of the cuckoo homing nest,a update method that inertia weight decreases nonlinearly with the number of evolutionary iterations is designed to improve the nest location and coordinate the abilities of exploration and exploitation.Secondly,the discovery probability with random adjustment is introduced to replace the discovery probability of fixed value to make the larger and smaller discovery probabi-lity appear randomly,which is beneficial to balancing the global exploration and local exploitation of the algorithm,accelerating the convergence speed,and increasing the diversity of the population.Finally,the logarithmic decreasing parameter and stochastic adjustment discovery probability are analyzed and tested,and the optimal parameter combination of logarithmic decrement and the optimal range of stochastic adjustment discovery probability are selected.At this time,the optimization effect of the function is the best.Compared with other evolutionary algorithms (BA,CS,PSO,ICS),DWCS greatly improves the precision of optimization,significantly reduces the number of iterations,and effectively improves the convergence speed and robustness.In 16 test functions,DWCS can converge to the global optimal solution,which proves that DWCS has a strong competitive power in solving the optimization problem of continuous complex functions.

Key words: Cuckoo search algorithm, Discovery probability, Function optimization, Logarithmic decreasing, Parameter selection

CLC Number: 

  • TP301.6
[1]WALTON S,HASSAN O,MORGAN K,et al.Modified cuckoo search:a new gradient free optimization algorithm[J].Chaos,Solitons and Fractals,2011,44(9):710-718.
[2]VALIAN E,MOHANNA S,TAVAKOLI S.Improved cuckoo search algorithm for global optimization[J].International Journal of Communications and Information Technology,2011,1(1):31-44.
[3]LI X T,WANG J A,YIN M H.Enhancing the performance of cuckoo search algorithm using orthogonal learning method[J].Neural Computing and Applications,2014,24(6):1233-1247.
[4]LONG W,CAI S H,JIAO J J,et al.Improved whale optimization algorithm for large scale optimization problems [J].Systems Engineering-Theory & Practice,2017,37(11):2983-2994.
[5]GOLDBERG D E,HOLLAND J H.Genetic algorithm in search,optimization and machine learning[M].Boston:Addison-Wesley Longman Publishing Co.Inc.1989.
[6]EBERHART R,KENNEDY J.A new optimizer using parti- cleswarm theory [C]∥Proceedings of the Sixth International Symposium on Micro Machine and Human Science.Nagoya:IEEE,1995:39-43.
[7]KIRKPATRICK S,GELATT C D,VECCHI M P.Optimization by simulated annealing [J].Science,1983,220:671-680.
[8]KOUDIL M,BENATCHBA K,TARABET A,et al.Using artificial bees to solve partitioning and scheduling problems in code sign [J].Applied Mathematics and Computation,2007,186(2):1710-1722.
[9]GEEM Z W,KIM J H,LOGANATHAN G V.A new heuristicoptimization algorithm:Harmony search[J].Simulation,2001,76(2):60-68.
[10]PAN W T.A new Fruit Fly Optimization Algorithm:Taking- the financial distress model as an example [J].Knowledge-Based Systems (0950-7051),2012,26(2):69-74.
[11]KRISHNANAND K N,GHOSE D.Detection of multiple source locations using a glowworm metaphor with applications to collective robotics[C]∥ Proceedings of IEEE Swarm Intelligence Symposium.Pasadena:IEEE,2005:84-91.
[12]YANG X S,DEB S.Cuckoo search via Levy flight [C]∥ Proceedings of World Congress on Nature & Bio-logically Inspired Computing.Coimbatore:IEEE,2009:210-214.
[13]YANG X S,DEB S.Engineering optimization by cuckoo search [J].International Journal of Mathematical Modeling and Numerical Optimization (2040-3607),2010,1(4):330-343.
[14]LI Y,MA L.A new metaheuristic cuckoo search algorithm [J].Systems Engineering,2012,30(8):64-69.
[15]LI Y,PEI Y H,LIU J S.Bat optimal algorithm combined uniform mutation with gaussian mutation[J].Control and Decision,2017,32(10):1775-1781.
[16]OUAARAB A,AHIOD B,YANG X S.Discrete cuckoo search algorithm for the travelling salesman problem[J].Neural Computing and Applications,2014,24(7/8):1659-1669.
[17]NAIK M K,PANDA R.A novel adaptive cuckoo search algorithm for intrinsic discriminant analysis based face recognition [J].Applied Soft Computing (1568-4946),2016,38(C):661-675.
[18]SETHI R,PANDA S,SAHOO B P.Cuckoo search algorithm based optimal tuning of PID structured TCSC controller[M].Odisha:Springer,2015:251-263.
[19]WANG J,ZHOU B.A hybrid adaptive cuckoo search optimization algorithm for the problem of chaotic systems parameter estimation [J].Neural Computing & Applications (0941-0643),2016,27(6):1511-1517.
[20]WANG L J,YIN Y L,ZHONG Y W.Cuckoo search algorithm with dimension by dimension improvement[J].Journal of Software,2013,24(11):2687-2698.
[21]MA W,SUN Z X.A global cuckoo optimization algorithm using coarse-to-fine search[J].Acta Electronica Sinica,2015,43(12):2429-2439.
[22]VALIA E,TAVAKOLI S,MOHANNA S.Improved cuckoo search for reliability optimization problems [J].Computers & Industrial Engineering (0360-8352),2013,64(1):459-468.
[23]ZHENG H Q,ZHOU Y Q.A novel cuckoo search optimization algorithm based on Gauss distribution[J].Journal of Computational Information Systems,2012,8(10):4193-4200.
[24]LI R Y,DAI R W.Adaptive step-size cuckoo search algorithm [J].Computer Science,2017,44(5):235-240.
[25]WANG L J,YIN Y L,ZHONG Y W.Cuckoo search with varied scaling factor[J].Frontiers of Computer Science,2015,9(4):623-635.
[26]JIN Q B,QI L F.Novel improved cuckoo search for PID controller design [J].Transactions of the Institute of Measurement & Control,2014,37(6):1-11.
[27]PRAJAPATI P P,SHAH M V.Performance Estimation of Differential Evolution,Particle Swarm Optimization and Cuckoo Search Algorithms[J].I.J.Intelligent Systems and Applications,2018,6:59-67.
[28]ZHANG Z C,HAN W,MAO B.Adaptive discrete cuckoo algorithm based on simulated annealing for solving TSP [J].Acta Electronica Sinica,2018,46(8):1849-1857.
[29]ZHANG M Q,WANG H,CUI Z H,et al.Hybrid multiob-jective cuckoo search with dynamical local search [J].Memetic Computing,2017,10(4):1-10.
[30]FU W Y.Equilibrium single evolution based cuckoo search algorithm [J].Acta Electronica Sinica,2019,47(2):282-288.
[31]MARELI M,TWALA B.An adaptive cuckoo search algorithm for optimization[J].Applied Computing and Informatics,2018,14(2):107-115.
[32]SALGOTRA R,SINGH U,SAHA S.New cuckoo search algorithms with enhanced exploration and exploitation properties [J].Expert Systems With Applications,2018,95:384-420.
[33]WANG Z,JIA C X,SUN Y H.Parasitized breeding and nestlings growth in oriental cuckoo[J].Chinese Journal of Zoology,2004,39(1):103-105.
[34]VISWANATHAN G M,AFANASYEV V,BULDYRE-V S V,et al.Lévy flights in random searches [J].Physica A:Statistical Mechanics and its Applications,2000,282(1/2):1-12.
[35]SHI Y H,EBERHART R C.Empirical study of particle swarm optimization [C]∥Proceedings of the 1999 Congress on Evolutionary Computation.Washington:IEEE,1999,3:1945-1949.
[36]SHI Y H,EBERHART R C.Fuzzy adaptive particle s-warm optimization [C]∥Proceedings the 2001 Congress on Evolutionary Computation.Seoul:IEEE,2001:101-106.
[37]EBERHART R C,SHI Y H.Tracking and optimizing dynamic systems with particle swarms [C]∥ Proceedings of the 2001 Congress on Evolutionary Computation.Seoul:IEEE,2001:94-100.
[38]PERAM T,VEERAMACHANENI K,MOHAN C K.-Fitness-distance-ratio based particle swarm optimization[C]∥Procee-dings of the 2003 IEEE Swarm Intelligence Symposium.Indiana-polis:IEEE,2003:174-181.
[39]FENG J M,LIU S Y.Particle swarm optimization algorithm based on inertia weight exponentially decreasing for solving absolute value equations[J].Journal of Jilin University (Science Edition),2016,54(6):1265-1269.
[40]ZHANG X,WANG P,XING J C,et al.Particle swarm optimization algorithms with decreasing inertia weight based on gaussian function[J].Application Research of Computers,2012,29(10):3710-3712,3724.
[41]DAI W Z,YANG X L.Particle swarm optimization algorithm based on inertia weight logarithmic decreasing.Computer Engineering and Applications,2015,51(17):14-19,52.
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