Optimization of the Cropping Pattern Using Cuckoo Search Technique

Rath, Ashutosh; Samantaray, Sandeep; Swain, Prakash Chandra

doi:10.1007/978-3-030-03131-2_2

Cited by 8 publications

(3 citation statements)

References 8 publications

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“…A gap filled by swarm intelligent (SI) algorithms [6] is a low cost to search for a feature subset. Some examples of these algorithms are particle swarm optimization (PSO) algorithm [7], genetic algorithm (GA) [8], ant colony optimization (ACO) algorithm [9], artificial bee colony optimization (BCO) algorithm [10], bat search algorithm (BSA) [11], cuckoo optimization (CO) algorithm [12] and elephant herding optimization (EHO) algorithm [13]. The first two are the core of the approach proposed in the present article.…”

Section: ❒ Issn: 2088-8708mentioning

confidence: 99%

Feature selection of unbalanced breast cancer data using particle swarm optimization

Elnawasany

Makhlouf²,

Tawfik³

et al. 2022

IJECE

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<p>Breast cancer is one of the significant deaths causing diseases of women around the globe. Therefore, high accuracy in cancer prediction models is vital to improving patients’ treatment quality and survivability rate. In this work, we presented a new method namely improved balancing particle swarm optimization (IBPSO) algorithm to predict the stage of breast cancer using unbalanced surveillance epidemiology and end result (USEER) data. The work contributes in two directions. First, design and implement an improved particle swarm optimization (IPSO) algorithm to avoid the local minima while reducing USEER data’s dimensionality. The improvement comes primarily through employing the cross-over ability of the genetic algorithm as a fitness function while using the correlation-based function to guide the selection task to a minimal feature subset of USEER sufficiently to describe the universe. Second, develop an improved synthetic minority over-sampling technique (ISMOTE) that avoid overfitting problem while efficiently balance USEER. ISMOTE generates the new objects based on the average of the two objects with the smallest and largest distance from the centroid object of the minority class. The experiments and analysis show that the proposed IBPSO is feasible and effective, outperforms other state-of-the-art methods; in minimizing the features with an accuracy of 98.45%.</p>

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Section: ❒ Issn: 2088-8708mentioning

confidence: 99%

Feature selection of unbalanced breast cancer data using particle swarm optimization

Elnawasany

Makhlouf²,

Tawfik³

et al. 2022

IJECE

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“…With respect to applications, CS has been extensively applied to many domains, such as neural networks [87], image processing [88], nonlinear systems [89,90], network structural optimization [91], agriculture optimization [92], engineering optimization [93], and scheduling [94]. These applications indicate that CS algorithm is an effective and efficient optimizer for solving some real-world problems.…”

Section: Related Workmentioning

confidence: 99%

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Xiao

Lei

et al. 2020

Mathematics

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Cuckoo search (CS) algorithm is a novel swarm intelligence optimization algorithm, which is successfully applied to solve some optimization problems. However, it has some disadvantages, as it is easily trapped in local optimal solutions. Therefore, in this work, a new CS extension with Q-Learning step size and genetic operator, namely dynamic step size cuckoo search algorithm (DMQL-CS), is proposed. Step size control strategy is considered as action in DMQL-CS algorithm, which is used to examine the individual multi-step evolution effect and learn the individual optimal step size by calculating the Q function value. Furthermore, genetic operators are added to DMQL-CS algorithm. Crossover and mutation operations expand search area of the population and improve the diversity of the population. Comparing with various CS algorithms and variants of differential evolution (DE), the results demonstrate that the DMQL-CS algorithm is a competitive swarm algorithm. In addition, the DMQL-CS algorithm was applied to solve the problem of logistics distribution center location. The effectiveness of the proposed method was verified by comparing with cuckoo search (CS), improved cuckoo search algorithm (ICS), modified chaos-enhanced cuckoo search algorithm (CCS), and immune genetic algorithm (IGA) for both 6 and 10 distribution centers.

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“…Mohammadrezapour et al (2017) described a case study of Qazvin plain, Iran, using cuckoo optimization algorithm for crop planning and concluded that this algorithm is quite promising in cultivation area of crops' optimization problem because of its simple structure, excellent search efficiency and strong robustness. Rath et al (2019) formulated a cropping model for Hirakud command area in Odisha, India to optimize the cropping pattern by using the CS technique to maximize the net annual benefit. The processes of crop planning and crop rotation were given more emphasis, considering optimal allocation of scarce water resources highly necessary.…”

Section: Introductionmentioning

confidence: 99%

Optimization of water productivity in Bhagwanpur distributary command of India employing TLBO and cuckoo search algorithms

Upadhyaya

2021

Water Policy

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Efficient and judicious use of land and water is the need of the hour. In other words, evolving a cropping pattern, which optimizes productivity or net return considering prevailing constraints is quite useful to farmers, because such a cropping pattern is expected to be better than the existing cropping pattern in terms of yielding optimum productivity or net return. A single objective problem consisting of an objective function of optimization of water productivity with prevailing constraints was formulated and three optimization algorithms, namely (i) LINPROG, (ii) teaching learning based optimization (TLBO) and (iii) cuckoo search (CS) were employed to compute optimum water productivity corresponding to various affinity levels. It was observed that all three approaches yielded exactly the same values of water productivity at different affinity levels. TLBO showed better convergence capability as it reached the optimum value of objective function at a lower number of iterations than CS technique. Optimum water productivity at 20% affinity level seems quite practical and reasonable to be recommended in this distributary command for adoption because water productivity value is 2.57 times higher with 98.25 ha less cropped area as compared to the value of water productivity for the existing cropping pattern.

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Optimization of the Cropping Pattern Using Cuckoo Search Technique

Cited by 8 publications

References 8 publications

Feature selection of unbalanced breast cancer data using particle swarm optimization

Feature selection of unbalanced breast cancer data using particle swarm optimization

Using Cuckoo Search Algorithm with Q-Learning and Genetic Operation to Solve the Problem of Logistics Distribution Center Location

Optimization of water productivity in Bhagwanpur distributary command of India employing TLBO and cuckoo search algorithms

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