Crisscross differential evolution algorithm for constrained hydrothermal scheduling

Kaur, Manbir; Dhillon, J.S.; Kothari, D.P.

doi:10.1016/j.asoc.2020.106393

Cited by 21 publications

(10 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…23 benchmark function and three CHPED problems 5 [25] Improved genetic algorithm using novel crossover and mutation (IGA-NCM) -6 [32] Learner nondominated sorting genetic algorithm (NSGA-RL) 10 famous multi-objective functions 7 [33] Chaotic-crisscross differential evolution (CCDE) Generalized test functions and two practical hydrothermal system problems 8 [34] Differential evolution algorithm (DEA) IEEE-30 bus system 9 [35] Dynamic economic emission dispatching based on WEV system (WE_DEED) 10 unit systems.…”

Section: Sr Refmentioning

confidence: 99%

Improved Fitness-Dependent Optimizer for Solving Economic Load Dispatch Problem

Tahir

Rashid

Rauf

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Economic load dispatch depicts a fundamental role in the operation of power systems, as it decreases the environmental load, minimizes the operating cost, and preserves energy resources. The optimal solution to economic load dispatch problems and various constraints can be obtained by evolving several evolutionary and swarm-based algorithms. The major drawback to swarm-based algorithms is premature convergence towards an optimal solution. Fitness-dependent optimizer is a novel optimization algorithm stimulated by the decision-making and reproductive process of bee swarming. Fitness-dependent optimizer (FDO) examines the search spaces based on the searching approach of particle swarm optimization. To calculate the pace, the fitness function is utilized to generate weights that direct the search agents in the phases of exploitation and exploration. In this research, the authors have used a fitness-dependent optimizer to solve the economic load dispatch problem by reducing fuel cost, emission allocation, and transmission loss. Moreover, the authors have enhanced a novel variant of the fitness-dependent optimizer, which incorporates novel population initialization techniques and dynamically employed sine maps to select the weight factor for the fitness-dependent optimizer. The enhanced population initialization approach incorporates a quasi-random Sabol sequence to generate the initial solution in the multidimensional search space. A standard 24-unit system is employed for experimental evaluation with different power demands. The empirical results obtained using the enhanced variant of the fitness-dependent optimizer demonstrate superior performance in terms of low transmission loss, low fuel cost, and low emission allocation compared to the conventional fitness-dependent optimizer. The experimental study obtained 7.94E−12, the lowest transmission loss using the enhanced fitness-dependent optimizer. Correspondingly, various standard estimations are used to prove the stability of the fitness-dependent optimizer in phases of exploitation and exploration.

show abstract

Section: Sr Refmentioning

confidence: 99%

Improved Fitness-Dependent Optimizer for Solving Economic Load Dispatch Problem

Tahir

Rashid

Rauf

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Jian et al [14] used logarithmic scale mixed integer linear programming [15] to better describe STHTS. To solve this issue, Yin et al [15] used crisscross optimization [16]. Kaur et al [16], used crisscross differential evolution to perform hydrothermal scheduling.…”

Section: A Related Workmentioning

confidence: 99%

Scenario-Based Fuel Constrained Short-Term Hydrothermal Scheduling

et al. 2022

View full text Add to dashboard Cite

For electric power companies, the economic utilization of existing fossil fuel is currently primary issue because of the diminishing supply of fossil fuel. Thermal power plants have fuel limitations and contractual restrictions that must be adhered to. Therefore this work presented the scenario-based fuel constrained short-term hydrothermal scheduling problem with the incorporation of renewable energy sources. The elephant clan optimization (ECO) approach is offered for short-term hydro-thermal scheduling (STHTS) with thermal generators, cascaded hydro, solar PV plants, wind-turbinegenerators (WTG), and pumped-storage-hydro (PSH) with and without demand side management (DSM) for various scenarios. On a typical test system, the suggested approach is shown to be successful. Analysis of the typical test system's numerical results is compared to those produced via the HPSO-TVAC and grey wolf algorithms (GWO). The comparison shows that the suggested ECO is capable of providing a better solution.

show abstract

“…Vertical crossover is to exchange the dimensional information of a single search agent, which can facilitate escaping from the stagnancy in local optima without destroying other dimensions that may be the global optimum. Considering the advantages of CSO, some papers tried to apply this algorithm on handling complex problems [50][51][52][53][54][55][56][57]. In addition, the excellent search capability of the two crossover operators can exactly make up for the deficiencies of HHO mentioned above.…”

Section: Introductionmentioning

confidence: 99%

Crisscross Harris Hawks Optimizer for Global Tasks and Feature Selection

et al. 2022

View full text Add to dashboard Cite

Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it undergoes weak global search capability because of the levy distribution in its optimization process. In this paper, a variant of HHO is proposed using Crisscross Optimization Algorithm (CSO) to compensate for the shortcomings of original HHO. The novel developed optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which can effectively achieve high-quality solutions with accelerated convergence on a variety of optimization tasks. In the proposed algorithm, the vertical crossover strategy of CSO is used for adjusting the exploitative ability adaptively to alleviate the local optimum; the horizontal crossover strategy of CSO is considered as an operator for boosting explorative trend; and the competitive operator is adopted to accelerate the convergence rate. The effectiveness of the proposed optimizer is evaluated using 4 kinds of benchmark functions, 3 constrained engineering optimization issues and feature selection problems on 13 datasets from the UCI repository. Comparing with nine conventional intelligence algorithms and 9 state-of-the-art algorithms, the statistical results reveal that the proposed CCHHO is significantly more effective than HHO, CSO, CCNMHHO and other competitors, and its advantage is not influenced by the increase of problems’ dimensions. Additionally, experimental results also illustrate that the proposed CCHHO outperforms some existing optimizers in working out engineering design optimization; for feature selection problems, it is superior to other feature selection methods including CCNMHHO in terms of fitness, error rate and length of selected features. Supplementary Information The online version contains supplementary material available at 10.1007/s42235-022-00298-7.

show abstract

Crisscross differential evolution algorithm for constrained hydrothermal scheduling

Cited by 21 publications

References 61 publications

Improved Fitness-Dependent Optimizer for Solving Economic Load Dispatch Problem

Improved Fitness-Dependent Optimizer for Solving Economic Load Dispatch Problem

Scenario-Based Fuel Constrained Short-Term Hydrothermal Scheduling

Crisscross Harris Hawks Optimizer for Global Tasks and Feature Selection

Contact Info

Product

Resources

About