Discussion mechanism based brain storm optimization algorithm

doi:10.3785/j.issn.1008-973X.2013.10.002

2013, Vol. 47

Issue (10): 1705-1711 DOI: 10.3785/j.issn.1008-973X.2013.10.002

Discussion mechanism based brain storm optimization algorithm

YANH Yu-ting1,2, SHI Yu-hui3, XIA Shun-ren1,2

1. Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou 310027, China; 2. Provincial Key Laboratory of Cardio-Cerebral Vascular Detection Technology and Medicinal Effectiveness
Appraisal,Hangzhou 310027, China; 3. Department of Electrical and Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou 215123, China

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Abstract

A discussion mechanism based brain storm optimization (DMBSO) algorithm was proposed in order to solve the problem that brain storm optimization (BSO) algorithm is likely to stagnate in the local optima and result in premature convergence. DMBSO used a new mechanism with inter-group discussion and intra-group discussion to replace the process of individual updating in the original BSO algorithm in order to respectively govern the ability of global search and local search. The ability of global search was enhanced at the beginning by linearly decreasing times of inter-group discussion and increasing times of intra-group discussion, while fine search was enhanced in the end to prevent premature convergence. Empirical studies were conducted to evaluate the performances of the DMBSO algorithm for the 10D, 20D, 30D problems of six popular benchmark functions (BFs). Experimental results demonstrate that the DMBSO algorithm can avoid being stagnated in the local optima, more effectively and steadily find the better results than the original BSO algorithm and standard particle swarm optimization (PSO) algorithm, and show stronger robustness with the increasing of BFs’ dimension.

Published: 01 October 2013

CLC:

TP 391.41

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Cite this article:

YANH Yu-ting, SHI Yu-hui, XIA Shun-ren. Discussion mechanism based brain storm optimization algorithm. J4, 2013, 47(10): 1705-1711.

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http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2013.10.002 OR http://www.zjujournals.com/eng/Y2013/V47/I10/1705

基于讨论机制的头脑风暴优化算法

为了克服头脑风暴优化(BSO)算法易陷入局部最优导致早熟收敛的问题,提出新型的基于讨论机制的头脑风暴优化(DMBSO)算法.该算法运用组内讨论和组间讨论这一新机制取代BSO算法中的个体更新过程,分别控制算法的全局搜索和局部搜索能力.通过线性递减和线性递增方式调整组间讨论和组内讨论次数,使算法搜索初期加强全局搜索能力,搜索后期加强局部细致搜索能力,有效地防止早熟问题.对6个经典测试函数(BFs)的10维、20维、30维问题分别进行测试来评估DMBSO的效果.结果表明,DMBSO算法与BSO算法和经典的粒子群(PSO)算法相比,可以有效地避免陷入局部最优,稳定地找到更好的最优值,而且随着问题维度的增加,DMBSO表现出更强的鲁棒性.

［1］ EBERHART R, KENNEDY J. A new optimizer using particle swarm theory ［C］∥ 6th International Symposium on Micro Machine and Human Science.

Nagoya: IEEE, 1995: 39-43.

［2］ DORIGO M, CARO G D. Ant colony optimization: a new metaheuristic ［C］∥Evolutionary Computation. London: McGrawHill, 1999: 1470-1477.

［3］ PASSINO K M. Biomimicry of bacterial foraging for distributed optimization and control ［J］. Control Systems, IEEE, 2002, 22(3): 52-67.

［4］ KARABOGA D. Artificial bee colony algorithm ［EB/OL］. \[20120801\]. http:∥www.scholarpedia.org/article/Artificial_bee_colony_algorithm.

［5］ BONABEAU E. Swarm Intelligence ［C］∥O’Reilly Emerging Technology Conference. Santa Clara: \[s.n.\]，2003.

［6］ CHU S C, HUANG H C, RODDICK J F, et al. Overview of algorithms for swarm intelligence ［J］. Computational Collective Intelligence Technologies

and Applications, 2011, 6922/2011L: 28-41.

［7］ SHI Y. Brain storm optimization algorithm ［J］. Advances in Swarm Intelligence, 2011, 6728： 303-309.

［8］ SHI Y. An optimization algorithm based on brainstorming process ［J］. International Journal of Swarm Intelligence Research, 2011, 2(4): 35-62.

［9］ ZHAN Z, ZHANG J, SHI Y, et al. A modified brain storm optimization ［C］∥IEEE Congress on Evolutionary Computation. Brisbane: IEEE, 2012: 18.

［10］ SHI Y, EBERHART R. A modified particle swarm optimizer ［C］∥IEEE World Congress on Computational Intelligence. Anchorage: IEEE, 1998: 69-73.

［11］ TING T, SHI Y, CHENG S, et al. Exponential inertia weight for particle swarm optimization ［J］. Lecture Notes in Computer Science, 2012,

7331: 8390.

［12］ RAGHAVENDRA R,DORIZZI B. A novel adaptive inertia particle swarm optimization (AIPSO) algorithm for improving multimodal biometric recognition

［C］∥International Conference on HandBased Biometrics. Hong Kong: IEEE, 2011: 16.

［13］ LI Y J, WU T J. An adaptive ant colony system algorithm for continuousspace optimization problems ［J］. Journal of Zhejiang University:

Science A, 2003, 4(1): 4046.

［14］ ALAM M S, ULKABIR M W,ISLAM M M. Selfadaptation of mutation step size in artificial bee colony algorithm for continuous function

optimization ［C］∥13th International Conference on Computer and Information Technology (ICCIT). Dhaka, Bangladesh: IEEE, 2010: 69-74.

［15］ YAO X, LIU Y, LIN G. Evolutionary programming made faster ［J］. IEEE Transactions on Evolutionary Computation, 1999, 3(2): 82-102.

［16］ TORN A, ZILINSKAS A. Global optimization ［M］. New York: SpringerVerlag, 1989.

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