Performance analysis of artificial bee colony (ABC) algorithm in optimizing release policy of Aswan High Dam

Hossain, Shabbir; El-Shafie, Ahmed

doi:10.1007/s00521-012-1309-3

Cited by 39 publications

(8 citation statements)

References 14 publications

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“…They concluded that ABC can potentially solve this nonlinear, high‐dimensional and complex optimization problem in terms of fast convergence and reaching global optimal value. Hossain and El‐Shafie (2014) and Ahmad et al (2016) compared the ABC algorithm with the GA in obtaining monthly release curves of a reservoir in Egypt. The total estimated RMSEs for obtained rule curves by ABC were 9.81, 4.78 and 5.90 billion cubic meters (BCM) for high, medium and low flow conditions, respectively.…”

Section: Algorithmsmentioning

confidence: 99%

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

Jahandideh-Tehrani

Bozorg‐Haddad

Loáiciga

2020

Water & Environment J

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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).

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Section: Algorithmsmentioning

confidence: 99%

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

Jahandideh-Tehrani

Bozorg‐Haddad

Loáiciga

2020

Water & Environment J

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“…However, no comparison was performed in the stated research to identify the most efficient algorithm. The artificial bee colony (ABC) algorithm was compared with the GA in a reservoir release optimization problem by Hossain and El-Shafie (2014), who reported that ABC outperformed GA due to lower RMSE values for monthly release curves. According to their results, ABC obtained efficient reliability, resiliency, and vulnerability in release curves.…”

Section: The Binary-coded Gamentioning

confidence: 99%

Application of non-animal–inspired evolutionary algorithms to reservoir operation: an overview

Jahandideh-Tehrani

Bozorg‐Haddad

Loáiciga

2019

Environ Monit Assess

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Evolutionary algorithms (EAs) have been widely used to search for optimal strategies for the planning and management of water resources systems, particularly reservoir operation. This study provides a comprehensive diagnostic assessment of state of the art of the non-animal-inspired EA applications to reservoir optimization. This type of EAs does not mimic biologic traits and group strategies of animal (wild) species. A search of pertinent papers was applied to the journal citation reports (JCRs). A bibliometric survey identified 14 pertinent non-animal-inspired EAs, such as the genetic algorithm (GA), simulated annealing (SA), and differential evolution (DE) algorithms, most of which have a number of modified versions. The characteristics of non-animal-inspired EAs and their modified versions were discussed to identify the difference between EAs and how each EA was improved. Additionally, the type of application of non-animal-inspired EAs to different case studies was investigated, and comparisons were made between the performance of the applied EAs in the studied literature. The survey revealed that the GA is the most frequently applied algorithm, followed by the DE algorithm. Non-animal-inspired EAs are superior to the classical methods of reservoir optimization (e.g., the non-linear programming and dynamic programming) due to faster convergence, diverse solution space, and efficient objective function evaluation. Several non-animal-inspired EAs of recent vintage have been shown to outperform the classic GA, which was the first evolutionary algorithm applied to reservoir operation.

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“…The authors satisfied with its ability to get out of local minimum and its effective application on multivariable function optimization. Hossain et al (2013) proposed ABC algorithm to derive operating policies for a multi-purpose reservoir system and demonstrated its applicability and low computation complexity through a case study of Aswan High Dam, Egypt. On the other hand, Zhou et al (2014) employed the new multi-objective ABC algorithm and applied for shortterm scheduling of hydrothermal system.…”

Section: Introductionmentioning

confidence: 99%

“…The existing nonlinear swarm optimization algorithm, ABC is ready to adopt and incorporate with the known (historical reservoir inflow) input for the model to provide possible solutions (release policy). An optimal water distribution relation needs to be maintained between the water release and reservoir storage in every operation time period (Hossain & El-shafie, 2013). Therefore, the main objective of this paper is to minimize the water deficit in the operating system.…”

Section: Introductionmentioning

confidence: 99%

An Application of Artificial Bee Colony Algorithm for Reservoir Optimization: A Case Study of Chenderoh Dam, Malaysia

2016

IJAAEE

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Abstract-Reservoir operators commonly manage and regulate the downstream water flow and the demands over time and space. It becomes a challenging task for the decision maker to evaluate the trade-off during high inflow and low inflow season especially for multipurpose hydropower system. The main goal of the present study is to develop a set of release policy under three different inflow scenarios (high, medium and low) for the Chenderoh hydropower reservoir using the Artificial Bee Colony algorithm. Due to its capability to identify different possible events happened in the reservoir, the proposed algorithm provided promising solutions which minimize the water deficit in the operating system. Based on the thought of refined performance by ABC model, three indices criteria (reliability, resilience and vulnerability) are used to evaluate its effectiveness. ABC model has provided good results in terms of reliability (63.9% meeting the demand, 11.8% more than the demand and 24.3% less than the demand) and small vulnerability (1.31) leading to develop an outstanding release policy for optimal operation.

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Performance analysis of artificial bee colony (ABC) algorithm in optimizing release policy of Aswan High Dam

Cited by 39 publications

References 14 publications

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

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

Application of non-animal–inspired evolutionary algorithms to reservoir operation: an overview

An Application of Artificial Bee Colony Algorithm for Reservoir Optimization: A Case Study of Chenderoh Dam, Malaysia

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