A NEW MULTI-OBJECTIVE ARTIFICIAL BEE COLONY ALGORITHM FOR MULTI-OBJECTIVE OPTIMIZATION PROBLEMS
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Konak A., Coit D.W., and Smith A.E., Multi-objective optimization using genetic algorithms: A tutorial, Reliability Engineering & System Safety 2006; 91: 992-1007.
Karaboga D., An idea based on honey bee swarm for numerical optimization, Technical report-tr06, Erciyes university, engineering faculty, computer engineering department 2005.
Karaboga D., Gorkemli B., Ozturk C., and Karaboga N., A comprehensive survey: artificial bee colony (ABC) algorithm and applications, Artificial Intelligence Review 2014; 42: 21-57.
Gong D., Han Y., and Sun J., A novel hybrid multi-objective artificial bee colony algorithm for blocking lot-streaming flow shop scheduling problems, Knowledge-Based Systems 2018; 148: 115-130.
Sanchez-Gomez J.M., Vega-Rodríguez M.A., and Pérez C. J., Extractive multi-document text summarization using a multi-objective artificial bee colony optimization approach, Knowledge-Based Systems 2017; 159: 1-8.
Saad A., Khan S.A., and Mahmood A., A multi-objective evolutionary artificial bee colony algorithm for optimizing network topology design, Swarm and Evolutionary Computation 2018; 38: 187-201.
Pérez C.J., Vega-Rodríguez M.A., Reder K., and Flörke M., A Multi-Objective Artificial Bee Colony-based optimization approach to design water quality monitoring networks in river basins, Journal of Cleaner Production 2017; 166: 579-589.
Kishor A., Singh P.K., and Prakash J., NSABC: Non-dominated sorting based multi-objective artificial bee colony algorithm and its application in data clustering, Neurocomputing 2016; 216: 514-533.
Dwivedi K., Ghosh S., and Londhe N.D., Low power FIR filter design using modified multi-objective artificial bee colony algorithm, Engineering Applications of Artificial Intelligence 2016; 55: 58-69.
Ding M., Chen H., Lin N., Jing S., Liu F., Liang X., and Liu W., Dynamic population artificial bee colony algorithm for multi-objective optimal power flow, Saudi Journal of Biological Sciences 2017; 24: 703-710.
Coello C.A.C. and Lechuga M.S., MOPSO: a proposal for multiple objective particle swarm optimization, Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600), Honolulu, HI, USA, 1051-1056.
Deb K., Pratap A., Agarwal S., and Meyarivan T., A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation 2002; 6: 182-197.
Zitzler E., Deb K., and Thiele L., Comparison of Multiobjective Evolutionary Algorithms: Empirical Results, Evolutionary Computation 2000; 8: 173-195.
Zitzler E., Thiele L., Laumanns M., Fonseca C.M., and Fonseca V.G., Performance assessment of multiobjective optimizers: an analysis and review, IEEE Transactions on Evolutionary Computation 2003; 7: 117-132.
Tian Y., Cheng R., Zhang X., and Jin Y., PlatEMO: A MATLAB Platform for Evolutionary Multi-Objective Optimization [Educational Forum], IEEE Computational Intelligence Magazine 2017; 12: 73-87.
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