Hybrid brain storm optimisation and simulated annealing algorithm for continuous optimisation problems

Inspired by the brainstorming process of human beings, the brain storm optimisation algorithm, a new swarm intelligence algorithm, is proposed and has been applied in many fields in recent years. In this paper, a novel bio-inspired computation algorithm based on the brain storm optimisation algorithm and simulated annealing approach is proposed to solve continuous optimisation problems. The proposed algorithm integrates the simulated annealing process into the brain storm optimisation algorithm. The integrated part is in charge of creation of new individuals in later stages of evolution process, replacing the creation operator. The proposed algorithm is applied to solve 13 benchmark unconstrained continuous optimisation problems, and is compared with three state-of-the-art evolutionary algorithms: particle swarm optimisation, differential evolution, and brain storm optimisation algorithm. Experimental results show that the proposed algorithm produced a significant improvement over the brain storm optimisation algorithm and generally out performed the other three in terms of mean value, standard deviation, best fitness value ever found and convergence speed which can be seen from the evolution curve.

[1]  Yuhui Shi,et al.  Brain Storm Optimization Algorithm , 2011, ICSI.

[2]  Jun Zhang,et al.  Parameter investigation in brain storm optimization , 2013, 2013 IEEE Symposium on Swarm Intelligence (SIS).

[3]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[4]  Sudipta Mahapatra,et al.  An evolutionary programming algorithm for finding constrained optimal disjoint paths for multihop communication networks , 2010, Int. J. Metaheuristics.

[5]  Yuhui Shi,et al.  Predator–Prey Brain Storm Optimization for DC Brushless Motor , 2013, IEEE Transactions on Magnetics.

[6]  Yuhui Shi,et al.  An Optimization Algorithm Based on Brainstorming Process , 2011, Int. J. Swarm Intell. Res..

[7]  Juan Luis Fernández-Martínez,et al.  A Brief Historical Review of Particle Swarm Optimization (PSO) , 2012 .

[8]  Hai-Bin Duan,et al.  A Hybrid Artificial Bee Colony Optimization and Quantum Evolutionary Algorithm for Continuous Optimization Problems , 2010, Int. J. Neural Syst..

[9]  Yuhui Shi,et al.  ?Hybrid Particle Swarm Optimization and Genetic Algorithm for Multi-UAV Formation Reconfiguration , 2013, IEEE Computational Intelligence Magazine.

[10]  Zhi-hui Zhan,et al.  A modified brain storm optimization , 2012, 2012 IEEE Congress on Evolutionary Computation.

[11]  Swagatam Das,et al.  A novel genetic algorithm to solve travelling salesman problem and blocking flow shop scheduling problem , 2013, Int. J. Bio Inspired Comput..

[12]  Jun Zhang,et al.  Orthogonal Learning Particle Swarm Optimization , 2011, IEEE Trans. Evol. Comput..

[13]  Sandro Ridella,et al.  Minimizing multimodal functions of continuous variables with the “simulated annealing” algorithmCorrigenda for this article is available here , 1987, TOMS.

[14]  N. Metropolis,et al.  Equation of State Calculations by Fast Computing Machines , 1953, Resonance.

[15]  Yuhui Shi,et al.  Optimal Satellite Formation Reconfiguration Based on Closed-Loop Brain Storm Optimization , 2013, IEEE Computational Intelligence Magazine.

[16]  J. Gero,et al.  "Critical Mass of Ideas": A Model of Incubation in Brainstorming , 2012 .

[17]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[18]  Mariappan Kadarkarainadar Marichelvam,et al.  An improved hybrid Cuckoo Search (IHCS) metaheuristics algorithm for permutation flow shop scheduling problems , 2012, Int. J. Bio Inspired Comput..

[19]  Zhihua Cui,et al.  Social Emotional Optimization Algorithm with Gaussian Distribution for Optimal Coverage Problem , 2013 .

[20]  Chu Kiong Loo,et al.  Hybrid particle swarm optimization algorithm with fine tuning operators , 2009, Int. J. Bio Inspired Comput..

[21]  Bijaya K. Panigrahi,et al.  Modified biogeography-based optimisation (MBBO) , 2011, Int. J. Bio Inspired Comput..

[22]  B. V. Babu,et al.  Hybrid multi-objective differential evolution (H-MODE) for optimisation of polyethylene terephthalate (PET) reactor , 2010, Int. J. Bio Inspired Comput..

[23]  Ajith Abraham,et al.  Improved differential evolution algorithm with decentralisation of population , 2011, Int. J. Bio Inspired Comput..

[24]  V. Cerný.  Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm , 1985 .

[25]  Haibin Duan,et al.  Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning , 2014, Int. J. Intell. Comput. Cybern..

[26]  Hui Zhang,et al.  Multi-agent simulated annealing algorithm based on differential evolution algorithm , 2012, Int. J. Bio Inspired Comput..