MOICA: A novel multi-objective approach based on imperialist competitive algorithm
作者： Rasul Enayatifar , Moslem Yousefi , Abdul Hanan Abdullah , Amer Nordin Darus
DOI: 10.1016/J.AMC.2013.03.099
关键词:
摘要: A novel multi-objective evolutionary algorithm (MOEA) is developed based on imperialist competitive (ICA), a newly introduced (EA). Fast non-dominated sorting and the Sigma method are employed for ranking solutions. The tested six well-known test functions each of them incorporate particular feature that may cause difficulty to MOEAs. numerical results indicate MOICA shows significantly higher efficiency in terms accuracy maintaining diverse population solutions when compared existing salient MOEAs, namely fast elitism genetic (NSGA-II) particle swarm optimization (MOPSO). Considering computational time, proposed slightly faster than MOPSO outperforms NSGA-II.
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sci-hub.se 参考文章(26)
Chen-Yu Chen, Kuo-Chou Chang, Shing-Hua Ho, Improved framework for particle swarm optimization: Swarm intelligence with diversity-guided random walking Expert Systems With Applications. ,vol. 38, pp. 12214- 12220 ,(2011) , 10.1016/J.ESWA.2011.03.086
Peng Hu, Li Rong, Cao Liang-lin, Li Li-xian, None, Multiple Swarms Multi-Objective Particle Swarm Optimization Based on Decomposition Procedia Engineering. ,vol. 15, pp. 3371- 3375 ,(2011) , 10.1016/J.PROENG.2011.08.632
Shyam Sundar, Alok Singh, None, A swarm intelligence approach to the early/tardy scheduling problem Swarm and evolutionary computation. ,vol. 4, pp. 25- 32 ,(2012) , 10.1016/J.SWEVO.2011.12.002
Jie Jia, Jian Chen, Guiran Chang, Zhenhua Tan, Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm Computers & Mathematics With Applications. ,vol. 57, pp. 1756- 1766 ,(2009) , 10.1016/J.CAMWA.2008.10.036
S. Ramesh, S. Kannan, S. Baskar, Application of modified NSGA-II algorithm to multi-objective reactive power planning Applied Soft Computing. ,vol. 12, pp. 741- 753 ,(2012) , 10.1016/J.ASOC.2011.09.015
Hamid Ali, Waseem Shahzad, Farrukh Aslam Khan, Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization soft computing. ,vol. 12, pp. 1913- 1928 ,(2012) , 10.1016/J.ASOC.2011.05.036
Chien-Ho Ko, Shu-Fan Wang, Precast production scheduling using multi-objective genetic algorithms Expert Systems With Applications. ,vol. 38, pp. 8293- 8302 ,(2011) , 10.1016/J.ESWA.2011.01.013
Mehrdad Hakimi-Asiabar, Seyyed Hassan Ghodsypour, Reza Kerachian, Multi-objective genetic local search algorithm using Kohonen's neural map Computers & Industrial Engineering. ,vol. 56, pp. 1566- 1576 ,(2009) , 10.1016/J.CIE.2008.10.010
Xiang Li, Hau-San Wong, Logic optimality for multi-objective optimization Applied Mathematics and Computation. ,vol. 215, pp. 3045- 3056 ,(2009) , 10.1016/J.AMC.2009.09.053
S. Talatahari, B. Farahmand Azar, R. Sheikholeslami, A.H. Gandomi, Imperialist competitive algorithm combined with chaos for global optimization Communications in Nonlinear Science and Numerical Simulation. ,vol. 17, pp. 1312- 1319 ,(2012) , 10.1016/J.CNSNS.2011.08.021