Optimisation of Multiple Hydropower Reservoir Operation Using Artificial Bee Colony Algorithm

Choong, Shi Mei; El-Shafie, Ahmed; Mohtar, Wan Hanna Melini Wan

doi:10.1007/s11269-017-1585-x

Cited by 22 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results showed the ABC's superiority to GSA to the quicker rates of convergence, stability, greater efficiency and reduced vulnerability, while the GSA's resilience measurement was much better. Choong et al (2017) used an ABC algorithm for the optimization of multiple hydropower reservoirs operation. The tests showed that the weekly ABC optimization was superior to reliability and vulnerability leading to a better release policy for optimal operation.…”

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

A state-of-the-Art review of heuristic and metaheuristic optimization techniques for the management of water resources

Kumar

Yadav

2022

Water Supply

View full text Add to dashboard Cite

Water resource management is a complex engineering problem, due to the stochastic nature of inflow, various demands and environmental flow downstream. With the increase in water consumption for domestic use and irrigation, it becomes more challenging. Many more difficulties, such as non-convex, nonlinear, multi-objective, and discontinuous functions, exist in real-life. From the past two decades, heuristic and metaheuristic optimization techniques have played a significant role in managing and providing better performance solutions. The popularity of heuristic and metaheuristic optimization techniques has increased among researchers due to their numerous benefits and possibilities. Researchers are attempting to develop more accurate and efficient models by incorporating novel methods and hybridizing existing ones. This paper's main contribution is to show the state-of-the-art of heuristic and metaheuristic optimization techniques in water resource management. The research provides a comprehensive overview of the various techniques within the context of a thorough evaluation and discussion. As a result, for water resource management problems, this study introduces the most promising evolutionary and swarm intelligence techniques. Hybridization, modifications, and algorithm variants are reported to be the most successful for improving optimization techniques. This survey can be used to aid hydrologists and scientists in deciding the proper optimization techniques.

show abstract

Section: Artificial Bee Colony (Abc)mentioning

confidence: 99%

A state-of-the-Art review of heuristic and metaheuristic optimization techniques for the management of water resources

Kumar

Yadav

2022

Water Supply

View full text Add to dashboard Cite

show abstract

“…The ABC algorithm exhibited faster convergence rate, stability, higher reliability and lower vulnerability indexes; however, the GSA performed better with respect to the resiliency indicator measure. ABC was also used for water supply deficit purposed in another study by Choong et al (2017). They used ABC as an optimization tool to investigate the performance of both monthly and weekly release curve in the Chenderoh Reservoir, Malaysia.…”

Section: The Artificial Bee Colony (Abc) Algorithmmentioning

confidence: 99%

“…They also indicated that weekly ABC optimization model outperformed the monthly model in terms of reliability and vulnerability indexes. Hossain et al (2018) applied ABC to the Aswan High Dam, Egypt, (Chen et al, 2016) A single reservoir --• Applicability of ABC in nonlinear, high dimensional and complex problems to optimize multi-crop irrigation scheduling and operation policy ABC (Ahmad et al, 2016) A single reservoir GSA -• Superiority of the ABC in terms of faster convergence rate, stability, higher reliability and lower vulnerability indexes • Superiority of the GSA in terms of the resiliency indicator measure ABC (Choong et al, 2017) A single reservoir --• Applicability of the ABC in extracting weekly and monthly release curves ABC (Hossain et al, 2018) A single reservoir PSO, GA and NN-SDP ABC • Superiority of the ABC in terms of achieving minimum water deficit, less waste of water and capacity to handle critical situations to extract optimal water-release policies. The ABC, GA and PSO release policies were compared and it was concluded that ABC failed four times to meet the demand targets, compared to PSO, real coded GA and binary coded GA with 5, 18 and 63 times of failure in meeting the demand, respectively.…”

Section: The Artificial Bee Colony (Abc) Algorithmmentioning

confidence: 99%

A review of applications of animal‐inspired evolutionary algorithms in reservoir operation modelling

Jahandideh-Tehrani

Bozorg‐Haddad

Loáiciga

2020

Water & Environment J

View full text Add to dashboard Cite

Successful operation of reservoir systems to guarantee the optimal use of available water resources has been the subject of many studies. The advent and applications of evolutionary algorithms (EAs) in the field of reservoir operation have led to significant advances in our capacity to improve the planning and management of complex reservoir systems. This study reports a review of the applications of animal‐inspired EAs to reservoir operation optimization selected among a large number of available papers in this area of research. The animal‐inspired EAs herein identified concern algorithms that mimic biologic traits of animal (wild) species. Among the animal‐inspired EAs ant colony optimization (ACO), particle swarm optimization (PSO), shuffled frog leaping algorithm (SFLA), artificial bee colony (ABC), honey bee mating optimization (HBMO), firefly algorithm (FA), cuckoo search (CS) and the bat algorithm (BA) are the best‐known ones selected for this review. This paper presents a brief description of the algorithmic characteristics and various employed improved versions or varieties thereof of each of the stated EAs. Furthermore, the differences between the proposed animal‐inspired EAs and their improved versions are identified by comparing the performance of the implemented animal‐inspired EAs in the reviewed literature. PSO and its varieties have the largest number of reported applications. Our comparison results revealed that constrained, discrete and randomized varieties of the animal‐inspired EAs outperformed unconstrained, continuous and deterministic varieties, respectively because of larger feasible search space, better solution quality and shorter computational time. Moreover, all the animal‐inspired EAs outperformed traditional methods of reservoir optimization, such as nonlinear programming (NLP) and dynamic programming (DP).

show abstract

“…Modern intelligent optimization algorithms are widely used in reservoir optimal operation, such as genetic algorithm (GA) [19,20], particle swarm optimization (PSO) [21,22], artificial neural network (ANN) [23], simulated annealing algorithm (SA) [24] and ant colony optimization (ACO) [25,26]. In addition, new swarm intelligence optimization algorithms continue to emerge, such as wolf pack algorithm (WPA) [27], differential evolution algorithm (DEA) [28,29] and artificial bee colony algorithm (ABCA) [30]. In view of the flexibility of intelligent algorithms to solve optimization problems, many researchers have combined different intelligent algorithms and achieved good results in reservoir operation [31][32][33].…”

Section: Introductionmentioning

confidence: 99%

A Method of Multi-Objective Optimization and Multi-Attribute Decision-Making for Huangjinxia Reservoir

Wei

Feng

Lu³

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

The objectives of flood control, power generation, water supply and ecology for reservoir operation are neither completely coordinated nor completely opposed, and its optimal operation and decision-making is very complicated. This study proposed a method of multi-objective optimization and multi-attribute decision making for reservoir operation (MODRO). The correlation analysis method was used to analyze the competitive relationship among the extracted objectives, and the multi-objective optimal operation model was constructed. The NSGA-II-SEABODE algorithm was applied to solve the MODRO problem. The objective extraction, model construction, optimization solution and scheme selection were coupled to form a multi-objective optimization and multi-attribute decision making method with the whole process of “Objective-Modeling-Optimization-Selection”. Huangjinxia Reservoir, which is located in Shaanxi, China, was selected as the case study. The results show that: (1) Quantifying the degree of conflict among objectives makes the construction of the multi-objective optimal operation model more reasonable. (2) The NSGA-II-SEABODE algorithm are used to obtain the decision-making scheme, which provides decision-making basis for managers. (3) For Huangjinxia Reservoir, water diversion is negatively related to power generation and ecology, and power generation is positively related to ecology. The results can promote the efficient utilization of water resources, improve the comprehensive benefits of reservoirs, and provide decision-making support for actual reservoir operation.

show abstract

Optimisation of Multiple Hydropower Reservoir Operation Using Artificial Bee Colony Algorithm

Cited by 22 publications

References 14 publications

A state-of-the-Art review of heuristic and metaheuristic optimization techniques for the management of water resources

A state-of-the-Art review of heuristic and metaheuristic optimization techniques for the management of water resources

A review of applications of animal‐inspired evolutionary algorithms in reservoir operation modelling

A Method of Multi-Objective Optimization and Multi-Attribute Decision-Making for Huangjinxia Reservoir

Contact Info

Product

Resources

About