Cuckoo optimization algorithm in optimal water allocation and crop planning under various weather conditions (case study: Qazvin plain, Iran)

Mohammadrezapour, Omolbani; Yoosefdoost, Icen; Ebrahimi, Mahboube

doi:10.1007/s00521-017-3160-z

Cited by 40 publications

(19 citation statements)

References 28 publications

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“…Furthermore, the results also agreed to previous studies [13,16] that hybridization of meta-heuristic algorithms provides the maximum net benefits. Unlike the past studies [12,17] , In this study hybridization of three meta-heuristic algorithms (GA, BFO and ACO) for optimizing the cropping area, conjunctive water allocation and the net returns for different reaches. Net returns from GA + BFO + ACO model was found to be 21.64% higher than GA + BFO model.…”

Section: Discussionmentioning

confidence: 99%

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Hybrid Optimization Model for Conjunctive Use of Surface and Groundwater Resources in Water Deficit Irrigation System

MoothampalayamSampathkumar¹,

Ramasamy²,

Ramasubbu³

et al. 2021

Preprint

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Increasing demand for food production with limited available water resources pose the threat to agricultural activities. The conjunctive allocation of water resources maximizes the net benefit of farmers efficiently. In this study, a novel hybrid optimization model was developed based on a genetic algorithm (GA), bacterial foraging optimization (BFO) and ant colony optimization (ACO) to maximize the net benefit of water deficit Sathanur reservoir command. The GA-based opti-mization model considered crop-related physical and economical parameters to derive optimal cropping patterns for three different conjunctive use policies and further allocation of surface and groundwater for different crops are enhanced with the BFO. The allocation of surface and groundwater for the head, middle and tail reach obtained from BFO is considered as input to ACO as a guiding mechanism to attain an optimal cropping pattern. Comparing the average produc-tivity values Policy 3 (3.665 Rs/m3) has better values relating to Policy 1 (3.662 Rs/m3) and Policy 2 (3.440 Rs/m3). Thus, the developed novel hybrid optimization model (GA-BFO-ACO) is very promising to enhance the farmer's net income as well as for the command area water conservation and can be replicated in other irrigated regions of the globe to overcome chronic land and water problems.

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Section: Discussionmentioning

confidence: 99%

“…The optimized cropping produced from swarm techniques results in 97.92 billion rupees more than the existing cropping pattern. [17] Cuckoo optimization algorithm (COA) and Genetic algorithm (GA) [18] Genetic algorithm (GA)…”

Section: Introductionmentioning

confidence: 99%

Hybrid Optimization Model for Conjunctive Use of Surface and Groundwater Resources in Water Deficit Irrigation System

MoothampalayamSampathkumar¹,

Ramasamy²,

Ramasubbu³

et al. 2021

Preprint

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“…obtained some research results. According to the mechanism and technology used, the progress and attempts made by CS can be divided into the following categories: (1) research on the improvement of position update mechanism of CS [12], [17]- [23]; (2) improvement of the parameter adjustment method of CS [17], [18], [20]- [28]; (3) application research of CS [3], [8], [25], [29]- [40]. The improvement and research of these algorithms not only improve the optimization performance of CS, but also promote the development of CS theory and application.…”

Section: Many Scholars Have Conducted In-depth Research On Cs Andmentioning

confidence: 99%

Improvement and Application of Adaptive Hybrid Cuckoo Search Algorithm

et al. 2019

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Aiming at the problem of ease of falling into local optimum and low solution quality when solving optimization problems, this paper proposes an adaptive hybrid cuckoo search (AHCS) algorithm. AHCS improves the Lévy flight method and population evolution strategy of the cuckoo search (CS) algorithm, and introduces a mutation operation operator. Inspired by the idea of position update of particle swarm optimization (PSO) algorithm, this paper introduces the inertia weight w in the Lévy flight method of CS algorithm, and gives the new dynamic adjustment methods of parameters α and β respectively. In order to enhance the local search ability and optimization speed of the algorithm, this paper introduces the mutation operation operator, and presents a new evolution strategy of the hybrid cuckoo search algorithm. In addition, in order to verify the performance of AHCS, 30 benchmark functions and CEC 2017 optimization problems were selected. The calculation results of the 30 benchmark functions and CEC 2017 optimization problems show that compared with other algorithms, the number of winning cases of t-test values and the Friedman average ranking for AHCS are significantly better than other algorithms. Finally, AHCS and various intelligent optimization methods in the literature are used to optimize the structural parameters of the reducer and the cantilever beam. The optimization results show that the quality of AHCS solution is significantly better than other algorithms.

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“…The optimal solution is easy to wander near the final scheme and leads to the fast convergence rate. Compared with other intelligent algorithms, the advantages of standard cuckoo searching algorithm (SCSA) are relative few parameters, simple programming, few revised parameters in practical problems, and fast convergence (Mohammad et al 2019, Montalvo I,et al 2010,etc.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective Water Resources Optimum Allocation Scheme Based on Improved Standard Cuckoo Searching Algorithm (ISCSA)

Zhou¹

2021

Preprint

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The standard cuckoo searching algorithm(SCSA)is a population intelligent optimization algorithm, which is also a new heuristic searching algorithm. The advantages of SCSA (such as convenient operation, heuristic searching, etc.) make it easy to find the optimal solution and maintain wider searching range. However, SCSA also has some drawbacks, such as long searching time, easy to fall into local optimum. In order to solve the problems existed in SCSA, in this paper, the improved standard cuckoo searching algorithm (ISCSA) was studied, which includes chaotic initialization and Gaussian disturbed algorithm. As a case study, taking economic, social and ecological benefits as the objective function, the multi-objective water resources optimal allocation models were constructed in Xianxiang Region, China. The ISCSA was applied to solve the water allocation models and the multi-objective optimal water supply scheme for Xinxiang region was obtained. The water resources optimal allocation schemes in the planning level year (2025) for 12 water supply sub-regions were predicted. The desirable eco-environment and benefits were achieved using the studied methods. The results show that the ISCSA has obvious advantages in the solution of water resources optimal allocation and planning.

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Cuckoo optimization algorithm in optimal water allocation and crop planning under various weather conditions (case study: Qazvin plain, Iran)

Cited by 40 publications

References 28 publications

Hybrid Optimization Model for Conjunctive Use of Surface and Groundwater Resources in Water Deficit Irrigation System

Hybrid Optimization Model for Conjunctive Use of Surface and Groundwater Resources in Water Deficit Irrigation System

Improvement and Application of Adaptive Hybrid Cuckoo Search Algorithm

Multi-objective Water Resources Optimum Allocation Scheme Based on Improved Standard Cuckoo Searching Algorithm (ISCSA)

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