Locating Pareto optimal designs of antenna through parallel multiobjective evolutionary algorithm

2021 ◽  
Author(s):  
Dawei Ding ◽  
Xianheng Ding ◽  
Haibo Zhang ◽  
Shaoxu Ling
Keyword(s):  
Evolutionary Algorithm ◽  
Optimal Designs ◽  
Pareto Optimal ◽  
Multiobjective Evolutionary Algorithm

A parallel multiobjective evolutionary algorithm based on decomposition using MPI and OpenMP

2014 ◽  
Author(s):  
J. W. Liu ◽  
W. Q. Ying ◽  
Y. Wu ◽  
Y. H. Xie ◽  
D. M. Meng ◽  
...  
Keyword(s):  
Evolutionary Algorithm ◽  
Multiobjective Evolutionary Algorithm

Multiobjective evolutionary algorithm for long-term planning of the national energy and transportation systems

2011 ◽  
Vol 2 (2) ◽  
pp. 151-169 ◽  
Author(s):  
Eduardo Ibanez ◽  
James D. McCalley
Keyword(s):  
Evolutionary Algorithm ◽  
Transportation Systems ◽  
Multiobjective Evolutionary Algorithm

Decomposition-Based Multiobjective Evolutionary Algorithm With an Ensemble of Neighborhood Sizes

2012 ◽  
Vol 16 (3) ◽  
pp. 442-446 ◽  
Author(s):  
Shi-Zheng Zhao ◽  
Ponnuthurai Nagaratnam Suganthan ◽  
Qingfu Zhang
Keyword(s):  
Evolutionary Algorithm ◽  
Multiobjective Evolutionary Algorithm

scholarly journals Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

Processes ◽  
2018 ◽  
Vol 6 (12) ◽  
pp. 250 ◽  
Author(s):  
Yahui Li ◽  
Yang Li
Keyword(s):  
Decision Analysis ◽  
Evolutionary Algorithm ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Decision Makers ◽  
Pareto Optimal ◽  
Pareto Optimal Solutions ◽  
Optimal Solutions ◽  
Optimal Power ◽  
Knee Point

To coordinate the economy, security and environment protection in the power system operation, a two-step many-objective optimal power flow (MaOPF) solution method is proposed. In step 1, it is the first time that knee point-driven evolutionary algorithm (KnEA) is introduced to address the MaOPF problem, and thereby the Pareto-optimal solutions can be obtained. In step 2, an integrated decision analysis technique is utilized to provide decision makers with decision supports by combining fuzzy c-means (FCM) clustering and grey relational projection (GRP) method together. In this way, the best compromise solutions (BCSs) that represent decision makers’ different, even conflicting, preferences can be automatically determined from the set of Pareto-optimal solutions. The primary contribution of the proposal is the innovative application of many-objective optimization together with decision analysis for addressing MaOPF problems. Through examining the two-step method via the IEEE 118-bus system and the real-world Hebei provincial power system, it is verified that our approach is suitable for addressing the MaOPF problem of power systems.

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