Decomposition-based modern metaheuristic algorithms for multi-objective optimal power flow - A comparative study

Abstract This article presents multi-objective variants of two popular metaheuristics, namely, the artificial bee colony algorithm (ABC) and the teaching learning based optimization algorithm (TLBO). Both of them are used to solve an optimal power flow problem. The proposed multi-objective variants are based on a decomposition approach, where the multi-objective optimization problem is decomposed into a number of scalar optimization sub-problems which are simultaneously optimized. The proposed algorithms are tested on the IEEE 30-bus system with different objectives. In addition, an algorithm based on fuzzy set theory is used to select the best committed solution. The proposed approaches are compared with others metaheuristic algorithms available in the specialized literature. Results indicate that the proposed approaches are highly competitive and also able to generate a well-distributed set of non-dominated solutions for the optimal power flow problem.

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