Cloud Particles Differential Evolution Algorithm: A Novel Optimization Method for Global Numerical Optimization

We propose a new optimization algorithm inspired by the formation and change of the cloud in nature, referred to as Cloud Particles Differential Evolution (CPDE) algorithm. The cloud is assumed to have three states in the proposed algorithm. Gaseous state represents the global exploration. Liquid state represents the intermediate process from the global exploration to the local exploitation. Solid state represents the local exploitation. The best solution found so far acts as a nucleus. In gaseous state, the nucleus leads the population to explore by condensation operation. In liquid state, cloud particles carry out macrolocal exploitation by liquefaction operation. A new mutation strategy called cloud differential mutation is introduced in order to solve a problem that the misleading effect of a nucleus may cause the premature convergence. In solid state, cloud particles carry out microlocal exploitation by solidification operation. The effectiveness of the algorithm is validated upon different benchmark problems. The results have been compared with eight well-known optimization algorithms. The statistical analysis on performance evaluation of the different algorithms on 10 benchmark functions and CEC2013 problems indicates that CPDE attains good performance.

[1]  Nor Ashidi Mat Isa,et al.  Teaching and peer-learning particle swarm optimization , 2014, Appl. Soft Comput..

[2]  Martin Middendorf,et al.  A hierarchical particle swarm optimizer and its adaptive variant , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[3]  Hendra Hermawan,et al.  Metals for Biomedical Applications , 2011 .

[4]  Ying Lin,et al.  Particle Swarm Optimization With an Aging Leader and Challengers , 2013, IEEE Transactions on Evolutionary Computation.

[5]  Xiaodong Li,et al.  Solving Rotated Multi-objective Optimization Problems Using Differential Evolution , 2004, Australian Conference on Artificial Intelligence.

[6]  Tapabrata Ray,et al.  An adaptive hybrid differential evolution algorithm for single objective optimization , 2014, Appl. Math. Comput..

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

[8]  Nor Ashidi Mat Isa,et al.  Bidirectional teaching and peer-learning particle swarm optimization , 2014, Inf. Sci..

[9]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[10]  Jouni Lampinen,et al.  A Fuzzy Adaptive Differential Evolution Algorithm , 2005, Soft Comput..

[11]  Leandro dos Santos Coelho,et al.  A self-adaptive chaotic differential evolution algorithm using gamma distribution for unconstrained global optimization , 2014, Appl. Math. Comput..

[12]  M. Montaz Ali,et al.  Population set-based global optimization algorithms: some modifications and numerical studies , 2004, Comput. Oper. Res..

[13]  Konstantinos E. Parsopoulos,et al.  UPSO: A Unified Particle Swarm Optimization Scheme , 2019, International Conference of Computational Methods in Sciences and Engineering 2004 (ICCMSE 2004).

[14]  P. N. Suganthan,et al.  Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[15]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[16]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[17]  Weicheng Xie,et al.  A binary differential evolution algorithm learning from explored solutions , 2014, Neurocomputing.

[18]  Ivanoe De Falco,et al.  An adaptive invasion-based model for distributed Differential Evolution , 2014, Inf. Sci..

[19]  Jing J. Liang,et al.  Dynamic multi-swarm particle swarm optimizer with local search for Large Scale Global Optimization , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[20]  Mehmet Fatih Tasgetiren,et al.  Differential evolution algorithm with ensemble of parameters and mutation strategies , 2011, Appl. Soft Comput..

[21]  José Neves,et al.  The fully informed particle swarm: simpler, maybe better , 2004, IEEE Transactions on Evolutionary Computation.

[22]  B. Basturk.  An artificial bee colony (ABC) algorithm for numeric function optimization , 2006 .

[23]  Yu-Jun Zheng,et al.  A hybrid fireworks optimization method with differential evolution operators , 2015, Neurocomputing.

[24]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[25]  Changhe Li,et al.  A Self-Learning Particle Swarm Optimizer for Global Optimization Problems , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[26]  A. Rezaee Jordehi,et al.  Enhanced leader PSO (ELPSO): A new PSO variant for solving global optimisation problems , 2015, Appl. Soft Comput..

[27]  Xuefeng Yan,et al.  Self-adaptive differential evolution algorithm with discrete mutation control parameters , 2015, Expert Syst. Appl..

[28]  Maurice Clerc,et al.  The particle swarm - explosion, stability, and convergence in a multidimensional complex space , 2002, IEEE Trans. Evol. Comput..

[29]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[30]  Wei Li,et al.  Cloud Particles Evolution Algorithm , 2015 .

[31]  M.M.A. Salama,et al.  Opposition-Based Differential Evolution , 2008, IEEE Transactions on Evolutionary Computation.

[32]  J. Miller,et al.  Guidelines: From artificial evolution to computational evolution: a research agenda , 2006, Nature Reviews Genetics.

[33]  G. Theraulaz,et al.  Inspiration for optimization from social insect behaviour , 2000, Nature.

[34]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[35]  Deyun Wang,et al.  Differential evolution improved with self-adaptive control parameters based on simulated annealing , 2014, Swarm Evol. Comput..

[36]  Arthur C. Sanderson,et al.  JADE: Adaptive Differential Evolution With Optional External Archive , 2009, IEEE Transactions on Evolutionary Computation.

[37]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[38]  Ruhul A. Sarker,et al.  A self-adaptive combined strategies algorithm for constrained optimization using differential evolution , 2014, Appl. Math. Comput..