An Evolutionary Algorithm With Constraint Relaxation Strategy for Highly Constrained Multiobjective Optimization

Sun, Zhichao; Ren, Hang; Yen, Gary G.; Chen, Tianfu; Wu, Junjie; An, Hongyang; Yang, Jianyu

doi:10.1109/tcyb.2022.3151974

Cited by 23 publications

(5 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, this model suffers from slow convergence speed and long computation time. An optimization algorithm based on augmented electron constraints is established by Sun [15]. It implements the optimization search process by simulating the motion of particles in the electron cloud.…”

Section: Related Workmentioning

confidence: 99%

Day Ahead Optimal Scheduling of Smart Community Microgrid Based on Improved NSGA-III

Zhao,

Hao,

Pei

et al. 2024

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

Urban Microgrids have complex topologies and operating environments. This leads to extreme volatility and instability when high percentage of renewable energy is integrated into them. To address these problems, we propose a day-ahead optimal scheduling model for the smart community microgrid on the basis of the improved non-dominated sorting genetic algorithm III (NSGA-III). It minimizes the day-ahead operating cost of the microgrid while balancing electricity load. Firstly, we design a wind/solar/hydrogen storage self-consistent energy system inside the community microgrid to realize the efficient utilization of renewable energy. Secondly, we introduce an adaptive mutation-crossover strategy, and propose a fuzzy inference-based NSGA-III algorithm, to solve the slow convergence speed and poor convergence stability of the current multi-objective optimization algorithms. Finally, we design an optimal microgrid scheduling strategy based on typical day. In the day ahead operation cycle of the community microgrid, it maximizes the absorption of wind/solar unit outputs. This improves the operational efficiency of the microgrid and effectively reducing system operating costs. Extensive experiments demonstrate that the proposed model achieves superior performance compared with the original NSGA-III methods. The total daily operating costs of the proposed microgrid is reduced by 4.5%, and the renewable energy consumption rates is increased by 8.1%.

show abstract

Section: Related Workmentioning

confidence: 99%

Day Ahead Optimal Scheduling of Smart Community Microgrid Based on Improved NSGA-III

Zhao,

Hao,

Pei

et al. 2024

Advances in Transdisciplinary Engineering

View full text Add to dashboard Cite

show abstract

“…To date, co-evolutionary algorithms have shown their effectiveness in solving complex constrained optimization problems in many works (Li et al, 2019b;Tian et al, 2021;Qiao et al, 2022a;Sun et al, 2023). These algorithms often co-evolve multiple populations, where one population is used to solve the original problem and the other populations are used to solve helper problems.…”

Section: General Framework Of Tpcmaomentioning

confidence: 99%

Solving optimal power flow problems via a constrained many-objective co-evolutionary algorithm

Tian,

Shi,

Zhang

et al. 2023

Front. Energy Res.

View full text Add to dashboard Cite

The optimal power flow problem in power systems is characterized by a number of complex objectives and constraints, which aim to optimize the total fuel cost, emissions, active power loss, voltage magnitude deviation, and other metrics simultaneously. These conflicting objectives and strict constraints challenge existing optimizers in balancing between active power and reactive power, along with good trade-offs among many metrics. To address these difficulties, this paper develops a co-evolutionary algorithm to solve the constrained many-objective optimization problem of optimal power flow, which evolves three populations with different selection strategies. These populations are evolved towards different parts of the huge objective space divided by large infeasible regions, and the cooperation between them renders assistance to the search for feasible and Pareto-optimal solutions. According to the experimental results on benchmark problems and the IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus systems, the proposed algorithm is superior over peer algorithms in solving constrained many-objective optimization problems, especially the optimal power flow problems.

show abstract

“…In our experimental comparison, we used the inverted generational distance (IGD) [88] and the hypervolume (HV) [89] to evaluate the performance of different algorithms. The performance of the algorithm can be fully measured by the IGD and HV metrics [90,91].…”

Section: Performance Metricmentioning

confidence: 99%

Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

Chen

et al. 2022

Appl Intell

View full text Add to dashboard Cite

show abstract

An Evolutionary Algorithm With Constraint Relaxation Strategy for Highly Constrained Multiobjective Optimization

Cited by 23 publications

References 63 publications

Day Ahead Optimal Scheduling of Smart Community Microgrid Based on Improved NSGA-III

Day Ahead Optimal Scheduling of Smart Community Microgrid Based on Improved NSGA-III

Solving optimal power flow problems via a constrained many-objective co-evolutionary algorithm

Balancing the trade-off between cost and reliability for wireless sensor networks: a multi-objective optimized deployment method

Contact Info

Product

Resources

About