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Research of multi-objective modified bat algorithm on optimal power flow problems

机译：多目标改性BAT算法在最优功率问题上的研究

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摘要

To overcome the insufficiency of standard bat algorithm in solving the non-convex optimal power flow (OPF) problems, a novel multi-objective modified bat algorithm (MOMBA) is proposed in this paper. The superiority of MOMBA algorithm with constrained Pareto-dominant approach (CPA), which improves the global-exploration ability and population-diversity by nonlinear inertia weight, can be validated by four multi-objective OPF simulation trials. In contrast to the classical penalty function approach (PFA), the effective CPA method can make each obtained power flow solution satisfy all system constraints. The testing cases considering the quadratic fuel cost, emission and active power loss, are implemented on the IEEE 30-bus and IEEE 57-bus systems, including three dual-objective and one triple-objective optimisations. Numerous results demonstrate that the suggested MOMBA algorithm can obtain well distributed Pareto front (PF) and effectively handle the multi-objective OPF problems.
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