Abstract
Currently, one of the most popular research topics is the development of a new meta-heuristic algorithm for solving multi-objective optimization problems. However, few of the proposed algorithms have been successful in finding sets that represent feasible solutions. One reason for this failure is that algorithms originally designed for single-objective problems do not provide sufficient exploitation and exploration capabilities for the multi-objective optimization process. Another reason is that a strong harmony is lacking between the Pareto-based archiving approach and the meta-heuristic search (MHS) method (the two basic elements of multi-objective optimization). This study presents the Multi-Objective Adaptive Guided Differential Evolution (MOAGDE) as a powerful and stable algorithm. The MOAGDE can effectively find Pareto optimal solutions for multi-objective optimization problems with different types of high-complexity decision/objective spaces. The proposed MOAGDE was developed by redesigning the adaptive guided differential evolution algorithm for multi-objective optimization. In addition, in the MOAGDE, non-dominated sorting strategy was integrated with crowding distance to archive Pareto optimal solutions of multiple conflicting functions. The CEC 2020, the most recent and advanced benchmark problem suite, was used along with the strongest competitors in the literature to test and verify the performance of the MOAGDE. Finally, using the proposed method, the best Pareto optimal solutions in the literature were applied to a real-world engineering problem: the multi-objective-alternating current optimal power flow (MO-ACOPF) problem involving wind/PV/tidal energy sources. Although the multi-objective version of this problem is rarely studied, the optimal power flow (OPF) is one of the major problems encountered in the planning and operation of modern power systems. The performance of the proposed approach was studied and tested for various objective functions on an IEEE 30-bus test system and the simulation results were compared with the results of the OMNI and MO_Ring_PSO_SCD algorithms.
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