Optimization flowchart of dung beetle optimization algorithm. by Xiangfei Tao (20775636)

Changes in railway expected revenue with passenger no-show rate. by Jing Xu (15337)

Variation of railway expected revenue with DB compensation multiplier. by Jing Xu (15337)

Variation of railway expected revenue with maximum DB rate. by Jing Xu (15337)

Comparison of key features of optimization algorithms. by Alireza Norouzpour Shahrbejari (21799964)

None - Optimization the stochastic optimal reactive power dispatch with renewable energy resources using a modified dandelion algorithm by Naima Agouzoul (21743513)

The railway expected revenue, revenue increase rate and calculation time under different numbers of scenarios. by Jing Xu (15337)

Variation of railway expected revenue with maximum overbooking rate under different passenger no-show rates. by Jing Xu (15337)

Description of algorithm parameters. by Naima Agouzoul (21743513)

“…In this regard, a modified Dandelion Optimizer (MDO) algorithm is introduced to optimize the SORPD solution with taking into consideration the stochastic fluctuations or the random variations of the load demand and the power produced by RERs. …”

Expected behavior system of ODEs. by Nicholas H. Vitale (20469289)

Performance comparison of SGINO algorithm with CGA-SG and GSPP-SG algorithms. by Zhenzhong Zhang (806974)

The flowchart of Algorithm 2. by Jing Xu (15337)

“…To solve this optimization model, a multi-level optimization algorithm is designed. …”

Bayesian Optimization Methods for Nonlinear Model Calibration by Montana N. Carlozo (22175927)

Subjects:

Bayesian Optimization Methods for Nonlinear Model Calibration by Montana N. Carlozo (22175927)

Subjects:

Bayesian Optimization Methods for Nonlinear Model Calibration by Montana N. Carlozo (22175927)

Subjects:

Bayesian Optimization Methods for Nonlinear Model Calibration by Montana N. Carlozo (22175927)

Subjects:

Statistical comparison for , and SEVS for different algorithms in the two case studies for 25 runs. by Naima Agouzoul (21743513)

Objective values of both algorithms for m = 5. by Zhenzhong Zhang (806974)

“…The method finds the optimal solution through an improved adaptive large-scale neighborhood search algorithm and uses a scenario generation technique to generate the scenarios required for evaluating candidate solutions to the high-dimensional stochastic optimization problem. …”

Objective values of both algorithms for m = 4. by Zhenzhong Zhang (806974)

“…The method finds the optimal solution through an improved adaptive large-scale neighborhood search algorithm and uses a scenario generation technique to generate the scenarios required for evaluating candidate solutions to the high-dimensional stochastic optimization problem. …”

Schematic representation of the relationship between expected profits of the retailer and supplier. by Minchao Zheng (20558832)