##
**MOICA: a novel multi-objective approach based on imperialist competitive algorithm.**
*(English)*
Zbl 1291.65174

Summary: A novel multi-objective evolutionary algorithm (MOEA) is developed based on imperialist competitive algorithm (ICA), a newly introduced evolutionary algorithm (EA). Fast non-dominated sorting and the Sigma method are employed for ranking the solutions. The algorithm is tested on six well-known test functions each of them incorporate a particular feature that may cause difficulty to MOEAs. The numerical results indicate that MOICA shows significantly higher efficiency in terms of accuracy and maintaining a diverse population of solutions when compared to the existing salient MOEAs, namely fast elitism non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO). Considering the computational time, the proposed algorithm is slightly faster than MOPSO and significantly outperforms NSGA-II.

### Keywords:

multi-objective imperialist competitive algorithm; multi-objective optimization; Pareto front; evolutionary algorithm; numerical result; multi-objective particle swarm optimization### Software:

MOICA
PDF
BibTeX
XML
Cite

\textit{R. Enayatifar} et al., Appl. Math. Comput. 219, No. 17, 8829--8841 (2013; Zbl 1291.65174)

Full Text:
DOI

### References:

[1] | Ko, C.-H.; Wang, S.-F., Precast production scheduling using multi-objective genetic algorithms, Expert Systems with Applications, 38, 7, 8293-8302, (2011) |

[2] | Hakimi-Asiabar, M.; Ghodsypour, S. H.; Kerachian, R., Multi-objective genetic local search algorithm using kohonenâ€™s neural map, Computers & Industrial Engineering, 56, 4, 1566-1576, (2009) |

[3] | Guria, C.; Bhattacharya, P. K.; Gupta, S. K., Multi-objective optimization of reverse osmosis desalination units using different adaptations of the non-dominated sorting genetic algorithm (NSGA), Computers & Chemical Engineering, 29, 9, 1977-1995, (2005) |

[4] | Jia, J.; Chen, J.; Chang, G.; Tan, Z., Energy efficient coverage control in wireless sensor networks based on multi-objective genetic algorithm, Computers & Mathematics with Applications, 57, 11-12, 1756-1766, (2009) · Zbl 1186.90022 |

[5] | Deb, K.; Pratap, A.; Agarwal, S., A fast and elitist multi objective genetic algorithm: NSGA-II, IEEE Transaction and Evolutionary Computation, 182-197, (2002) |

[6] | Ramesh, S.; Kannan, S.; Baskar, S., Application of modified NSGA-II algorithm to multi-objective reactive power planning, Applied Soft Computing, 12, 2, 741-753, (2012) |

[7] | Poli, R.; Kennedy, J.; Blackwell, T., Particle swarm optimization, Swarm Intelligence, 1, 1, 33-57, (2007) |

[8] | Altinoz, O. T.; Yilmaz, A. E., Particle swarm optimization with parameter dependency walls and its sample application to the microstrip-like interconnect line design, AEU - International Journal of Electronics and Communications, 66, 2, 107-114, (2012) |

[9] | Liu, H.; Abraham, A.; Clerc, M., Chaotic dynamic characteristics in swarm intelligence, Applied Soft Computing, 7, 3, 1019-1026, (2007) |

[10] | Sundar, S.; Singh, A., A swarm intelligence approach to the early/tardy scheduling problem, Swarm and Evolutionary Computation, 4, 25-32, (2012) |

[11] | Chen, C.-Y.; Chang, K.-C.; Ho, S.-H., Improved framework for particle swarm optimization: swarm intelligence with diversity-guided random walking, Expert Systems with Applications, 38, 10, 12214-12220, (2011) |

[12] | Coello Coello, C. A.; Lechunga, M. S., MOPSO: a proposal for multiple objective particle swarm optimization, (IEEE World Congress on Computational Intelligence, (2000), IEEE Hawaii) |

[13] | H. Ali, W. Shahzad, F.A. Khan, Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization, Applied Soft Computing. |

[14] | Moslemi, H.; Zandieh, M., Comparisons of some improving strategies on MOPSO for multi-objective (r, Q) inventory system, Expert Systems with Applications, 38, 10, 12051-12057, (2011) |

[15] | Hu, P.; Rong, L.; Liang-Lin, C.; Li-xian, L., Multiple swarms multi-objective particle swarm optimization based on decomposition, Procedia Engineering, 15, 3371-3375, (2011) |

[16] | E. Atashpaz-Gargari, C. Lucas, Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition, in: IEEE Congress on Evolutionary Computation, CEC 2007. |

[17] | Nazari-Shirkouhi, S.; Eivazy, H.; Ghodsi, R.; Rezaie, K.; Atashpaz-Gargari, E., Solving the integrated product mix-outsourcing problem using the imperialist competitive algorithm, Expert Systems with Applications, 37, 12, 7615-7626, (2010) |

[18] | Yousefi, M.; Darus, A. N.; Mohammadi, H., Second law based optimization of a plate fin heat exchanger using imperialist competitive algorithm, International Journal of the Physical Sciences, 6, 20, 4749-4759, (2011) |

[19] | Lucas, C.; Nasiri-Gheidari, Z.; Tootoonchian, F., Application of an imperialist competitive algorithm to the design of a linear induction motor, Energy Conversion and Management, 51, 7, 1407-1411, (2010) |

[20] | Kaveh, A.; Talatahari, S., Optimum design of skeletal structures using imperialist competitive algorithm, Computers & Structures, 88, 21-22, 1220-1229, (2010) · Zbl 1223.90082 |

[21] | Talatahari, S.; Farahmand Azar, B.; Sheikholeslami, R.; Gandomi, A. H., Imperialist competitive algorithm combined with chaos for global optimization, Communications in Nonlinear Science and Numerical Simulation, 17, 3, 1312-1319, (2012) · Zbl 1241.90193 |

[22] | Li, X.; Wong, H.-S., Logic optimality for multi-objective optimization, Applied Mathematics and Computation, 215, 8, 3045-3056, (2009) · Zbl 1180.90292 |

[23] | Ashry, G. A., On globally convergent multi-objective optimization, Applied Mathematics and Computation, 183, 1, 209-216, (2006) · Zbl 1109.65053 |

[24] | Zitzler, E.; Deb, K.; Thiele, L., Comparison of multiobjective evolutionary algorithms: empirical results, Evolutionary Computation, 8, 2, 173-195, (2000) |

[25] | Deb, K., Multi-objective genetic algorithms: problem difficulties and construction of test functions, Evolutionary Computation, 7, 3, 205-230, (1999) |

This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.