MOEICA: Enhanced multi-objective optimization based on imperialist competitive algorithm
الموضوعات :Amirali Nazari 1 , Ali Deihimi 2
1 - Bu-Ali Sina University, Department of Electrical Engineering, Hamedan, Iran
2 - Bu-Ali Sina University, Department of Electrical Engineering, Hamedan, Iran
الکلمات المفتاحية: multi-objective ICA, Pareto front coverage, performance metrics, benchmark functions,
ملخص المقالة :
In this paper, a multi-objective enhanced imperialist competitive algorithm (MOEICA) is presented. The main structures of the original ICA are employed while some novel approaches are also developed. Other than the non-dominated sorting and crowding distance methods which are used as the main tools for comparing and ranking solutions, an auxiliary comparison approach called fuzzy possession is also incorporated. This new provision enables more countries to participate in guiding the population towards different searching routs. Moreover the computational burden of the algorithm is abated by carrying out the hefty sorting process not at each iteration but at some predefined intervals. The frequency of which is controlled by on optional parameter. Furthermore, the recreation of empires and imperialists several times during the optimization progress, encourages better exploration and less chance to get trapped in local optima. The eligibility of the algorithm is tested on fifteen benchmark functions in terms of different performance metrics. The results through the comparison with NSGA-II and MOPSO shows that the MOEICA is a more effective and reliable multi-objective solver with being able to largely cover the true Pareto fronts (PFs) for the test functions applied in this article
Atashpaz-Gargari, E., & Lucas, C. (2007, September). Imperialist competitive algorithm: an algorithm for optimization inspired by imperialistic competition. In Evolutionary computation, 2007. CEC 2007. IEEE Congress on (pp. 4661-4667). IEEE.
Chandrasekaran, S., Ponnambalam, S. G., Suresh, R. K., & Vijayakumar, N. (2007, September). Multi-objective particle swarm optimization algorithm for scheduling in flowshops to minimize makespan, total flowtime and completion time variance. In Evolutionary Computation, 2007. CEC 2007. IEEE Congress on (pp. 4012-4018). IEEE.
Coello Coello, C. A., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems
Coello, C. A. C., Lamont, G. B., & Van Veldhuizen, D. A. (2007). Evolutionary algorithms for solving multi-objective problems (Vol. 5). New York: Springer
Coello, C. C., Pulido, G. T., & Lechuga, M. S. (2002). An extension of particle swarm optimization that can handle multiple objectives. In Workshop on Multiple Objective Metaheuristics (pp. 1-5).
Deb, K. (1999). Multi-objective genetic algorithms: Problem difficulties and construction of test problems. Evolutionary computation, 7(3), 205-230.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. A. M. T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE transactions on evolutionary computation, 6(2), 182-197
Deb, K., Thiele, L., Laumanns, M., & Zitzler, E. (2001). Scalable Test Problems for Evolutionary Multi-Objective Optimization. Zurich. Switzerland, Tech. Rep. 112.
Durillo, J. J., Nebro, A. J., & Alba, E. (2010, July). The jmetal framework for multi-objective optimization: Design and architecture. In Evolutionary Computation (CEC), 2010 IEEE Congress on (pp. 1-8). IEEE
Enayatifar, R., Yousefi, M., Abdullah, A. H., & Darus, A. N. (2013). MOICA: A novel multi-objective approach based on imperialist competitive algorithm. Applied Mathematics and Computation, 219(17), 8829-8841.
Fan, S. K. S., Chang, J. M., & Chuang, Y. C. (2015). A new multi-objective particle swarm optimizer using empirical movement and diversified search strategies. Engineering Optimization, 47(6), 750-770.
Fattahi, M., Mahootchi, M., Mosadegh, H., & Fallahi, F. (2014). A new approach for maintenance scheduling of generating units in electrical power systems based on their operational hours. Computers & Operations Research, 50, 61-79.
Fonseca, C. M., & Fleming, P. J. (1995). Multiobjective genetic algorithms made easy: selection sharing and mating restriction
Ghiasi, H., Pasini, D., & Lessard, L. (2011). A non-dominated sorting hybrid algorithm for multi-objective optimization of engineering problems. Engineering Optimization, 43(1), 39-59.
Holland, J. H. (2000). Building blocks, cohort genetic algorithms, and hyperplane-defined functions. Evolutionary computation, 8(4), 373-391.
Huband, S., Hingston, P., Barone, L., & While, L. (2006). A review of multiobjective test problems and a scalable test problem toolkit. IEEE Transactions on Evolutionary Computation, 10(5), 477-506.
Idoumghar, L., Chérin, N., Siarry, P., Roche, R., & Miraoui, A. (2013). Hybrid ICA–PSO algorithm for continuous optimization. Applied Mathematics and Computation, 219(24), 11149-11170.
Khabbazi, A., Atashpaz-Gargari, E., & Lucas, C. (2009). Imperialist competitive algorithm for minimum bit error rate beamforming. International Journal of Bio-Inspired Computation, 1(1-2), 125-133.
