Research Area: Metaheuristic Computing
To overcome the premature-convergence of standard bat algorithm in solving the multi-objective optimal power flow (MOOPF) problems, a novel hybrid bat algorithm (NHBA) is proposed in this paper. The suggested NHBA algorithm modifies the local search manner by a monotone random filling model based on extreme (MRFME) and improves the population-diversity by mutation and crossover mechanisms. To obtain the uniformly distributed Pareto optimal set (POS) with zero constraint-violation, an innovative non-dominated sorting method combined with the constrained Pareto fuzzy dominant (CPFD) strategy is put forward in this paper. To verify the superiority of the proposed NHBA-CPFD algorithm, which is federated by the NHBA algorithm and the CPFD strategy, ten MOOPF simulation cases considering the basic fuel cost, the fuel cost with value-point loadings, the total emission, and the active power loss are studied on the IEEE 30-node, IEEE 57-node, and IEEE 118-node systems. In contrast to the NHBA, MOPSO, and NSGA-III algorithms which adopt the constrain-prior Pareto-dominance method (CPM), numerous results validate the NHBA-CPFD algorithm that can achieve more superior compromise solutions and preferable Pareto fronts (PFs) even in the large-scale systems. Furthermore, two performance metrics of generational distance (GD) and hyper-volume (HV) also demonstrate that the NHBA-CPFD algorithm has great advantages to obtain the feasible POS with evenly distribution and favorable-diversity.
Keywords:
bat algorithm
multi-objective
Pareto fuzzy
monotone random filling model
performance metrics
Author(s) Name: Gonggui Chen, Jie Qian, Zhizhong Zhang Zhi Sun
Journal name: IEEE Access
Conferrence name:
Publisher name: IEEE
DOI: 10.1109/ACCESS.2019.2912643
Volume Information: Volume: 7
