many-objective optimisation problems (MaOPs) widely exist in real-world applications. Though two-archive2 evolutionary algorithm (Two Arch2) showed good performance in solving MaOPs, its performance highly depends on ...
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many-objective optimisation problems (MaOPs) widely exist in real-world applications. Though two-archive2 evolutionary algorithm (Two Arch2) showed good performance in solving MaOPs, its performance highly depends on the update methods of convergence archive (CA) and diversity archive (DA). To further improve the efficiency of updating two archives, this paper proposes a modified two-archive evolutionary algorithm (called MTaEA). Firstly, MTaEA adopts two different strategies to update CA. Then, a new update strategy based on radial projection and parallel distance is designed for DA. To validate the performance of MTaEA, two benchmark sets (DTLZ and MaF) with 3, 5, 10, 15, and 20 objectives are tested. Results show MTaEA obtains competitive performance when compared with six other state-of-the-art approaches. Finally, the proposed MTaEA is applied to many-objective ecological cascade reservoir operation in central China. Simulation results indicate MTaEA still achieves promising performance.
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