Dynamic and random differential evolution solving constrained optimisation problems
Summary: A dynamic and random differential evolution (DRDE) is designed in this paper to solve constrained optimisation problems (COPs). The dynamic means that the COP is transformed into a dynamic constrained optimisation problem (DCOP), then a DRDE is designed to solve the DCOP for solving the original COP. The key issue is to maintain a feasible population since a feasible population would vanish the difficulty in constraint handling. A deliberate setting of the environment can achieve the maintenance as much as possible. The `random' in the DRDE means that the scaling factor of the DDE is randomly created in a range to increase search domain so as to enhance the performance of the DRDE. The DRDE is tested by a kit of widely used constraint benchmark problems. The experimental results suggest it outperforms or performs similarly to other state-of-the-art algorithms referred to in this paper.
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