A multi-objective improved teaching-learning based optimization algorithm (MO-ITLBO)

This paper presents an efficient multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm for solving multi-objective optimization problems. The proposed algorithm uses a grid-based approach in order to keep diversity in the external archive. Pareto dominance is incorporated into the MO-ITLBO algorithm in order to allow this heuristic to handle problems with several objective functions. The qualities of the solution are computed based on the Pareto dominance notion. The performance of the MO-ITLBO algorithm is assessed by applying it on a set of standard test problems proposed for the Congress on Evolutionary Computation 2009 (CEC 2009) competition. The results obtained using the proposed algorithm is compared with the other state-of-the-art algorithms available in the literature. Moreover, the performance of the MO-ITLBO algorithm is also compared with the multi-objective version of the basic teaching-learning based optimization algorithm (MO-TLBO). The statistical analysis of the experimental work is also carried out by conducting Friedman's rank test and Holm post hoc procedure. The results show that the proposed approach is competitive and effective compared to other algorithms contemplated in this work and it can also find the result with greater precision.

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