Reliability Based Optimum Reservoir Design by Hybrid ACO-LP Algorithm

Afshar, Abbas; Masoumi, Fariborz; Solis, Sam Sandoval

doi:10.1007/s11269-015-0927-9

Cited by 23 publications

(8 citation statements)

References 23 publications

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“…This approach was introduced by Afshar et al (2015). They combined ACO optimizer with a virtual LP model to handle constraints in the solution methodology.…”

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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“…This approach was introduced by Afshar et al (2015). They combined ACO optimizer with a virtual LP model to handle constraints in the solution methodology.…”

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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“…They argued that the hybrid algorithm can effectively increase the computing efficiency, is easy to improve the stagnation and could not fall into the local optima. In a most recent work, Afshar et al (2015) introduced a hybrid ACO-LP formulation for reservoir design under reliability constraints. They successfully applied the model to solve the reliability based model for a 480-period problem.…”

Section: Reservoir Operation and Surface Water Managementmentioning

confidence: 99%

State of the Art Review of Ant Colony Optimization Applications in Water Resource Management

Afshar

Massoumi

Mariño

2015

Water Resour Manage

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104

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Among the emerged metaheuristic optimization techniques, ant colony optimization (ACO) has received considerable attentions in water resources and environmental planning and management during last decade. Different versions of ACO have proved to be flexible and powerful in solving number of spatially and temporally complex water resources problems in discrete and continuous domains with single and/or multiple objectives. Reviewing large number of peer reviewed journal papers and few valuable conference papers, we intend to touch the characteristics of ant algorithms and critically review their state-of-the-art applications in water resources and environmental management problems, both in discrete and continuous domains. The paper seeks to promote Opportunities, advantages and disadvantages of the algorithm as applied to different areas of water resources problems both in research and practice. It also intends to identify and present the major and seminal contributions of ant algorithms and their findings in organized areas of reservoir operation and surface water management, water distribution systems, urban drainage and sewer systems, groundwater managements, environmental and watershed management. Current trends and challenges in ACO algorithms are discussed and called for increased attempts to carry out convergence analysis as an active area of interest.

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“…To improve risk estimation efficiency, different stochastic methods have been proposed in recent years. Though the estimation of dam overtopping probability has become an important topic in stochastic hydrology, use of stochastic simulation–optimization for the optimal stochastic‐based design of dam projects is still very limited (Afshar, Masoumi, & Solis, 2015; Kuo, Hsu, Tung, Yeh, & Wu, 2008; Mousavi, Anzab, Asl‐Rousta, & Kim, 2017).…”

Section: Introductionmentioning

confidence: 99%

A novel simulation–optimization strategy for stochastic‐based designing of flood control dam: A case study of Jamishan dam

Sharafati

Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Shahid

2020

J Flood Risk Management

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This study presents a novel stochastic simulation–optimization approach for optimum designing of flood control dam through incorporation of various sources of uncertainties. The optimization problem is formulated based on two objective functions, namely, annual cost of dam implementation and dam overtopping probability, as those are the two major concerns in designing flood control dams. The nondominated solutions are obtained through a multi‐objective particle swarm optimization (MOPSO) approach. Results indicate that stochastic sources have a significant impact on Pareto front solutions. The distance index (DI) reveals the rainfall depth (DI = 0.41) as the most significant factor affecting the Pareto front and the hydraulic parameters (DI = 0.02) as the least. The dam overtopping probability is found to have a higher sensitivity to the variability of stochastic sources compared to annual cost of dam implementation. The values of interquartile range (IQR) indicate that the dam overtopping probability is least uncertain when all stochastic sources are considered (IQR = 0.25%). The minimum annual cost of dam implementation (2.79 M$) is also achieved when all stochastic sources are considered in optimization process. The results indicate the potential of the proposed method to be used for better designing of flood control dam through incorporation of all sources of uncertainty.

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Reliability Based Optimum Reservoir Design by Hybrid ACO-LP Algorithm

Cited by 23 publications

References 23 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

State of the Art Review of Ant Colony Optimization Applications in Water Resource Management

A novel simulation–optimization strategy for stochastic‐based designing of flood control dam: A case study of Jamishan dam

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