Application of Ant Colony Optimization Techniques to Predict Software Cost Estimation

Venkataiah, V.; Mohanty, Ramakanta; Pahariya, J. S.; Nagaratna, M.

doi:10.1007/978-981-10-3226-4_32

Cited by 16 publications

(16 citation statements)

References 16 publications

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“…The main drawback of existing literature [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35] is that it is very difficult to figure out which meta-heuristic algorithm provides better accuracy in estimating software effort. The main reasons behind unpredictability in the performances of the meta-heuristic algorithms are as follows.…”

Section: A Problem Formulationmentioning

confidence: 99%

“…2) The results obtained from GWO and SB algorithms are compared with five other meta-heuristic algorithms used in the literature for software effort estimation. We selected five widely used nature-inspired algorithms (BAT [29,45], Cuckoo Optimization (CO) [35,53,54], Genetic Algorithm (GA) [22,30,33] and Ant Colony Optimization (ACO) [24,32], Particle Swarm Optimization (PSO) [27,34,46]) for comparison. In this work, for comparison analysis nature-inspired meta-heuristics algorithms are selected based on inspiration from: (i) Natural biological system (GA, SB), (ii) Theory of evolution (PSO), (iii) Insects activities (ACO), (iv) Group behavior of animals, and birds (GWO, CO, BAT).To validate the performances of these seven algorithms, a set of nine benchmark functions having wide dimensions is applied.…”

Section: B Contributionsmentioning

confidence: 99%

“…For example, Bee Colony Optimization (BCO) [20], Differential Evolution (DE) [21], Genetic Algorithm (GA) [22], Fire fly [25], Harmony Search (HS) [26], Particle Swarm Optimization (PSO) [27] improved effort estimation by optimizing COCOMO parameters. Venkataiah et al [24] exploited Ant Colony Optimization (ACO) technique to optimize the prediction of software cost estimation.…”

Section: State Of the Artmentioning

confidence: 99%

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Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

et al. 2021

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Effort estimation is the most critical activity for the success of overall solution delivery in software engineering projects. In this context, the paper's main contributions to the literature on software effort estimation are twofold. First, this paper examines the application of meta-heuristic algorithms to have a logical and acceptable parametric model for software effort estimation. Secondly, to unravel the benefits of nature-inspired meta-heuristic algorithms usage in optimizing Deep Learning (DL) architectures for software effort estimation, this paper presents a Deep Neural Network (DNN) model for software effort estimation based on meta-heuristic algorithms. In this paper, Grey Wolf Optimizer (GWO) and StrawBerry (SB) meta-heuristic algorithms are applied for having a logical and acceptable parametric model for software effort estimation. To validate the performances of these two algorithms, a set of nine benchmark functions having wide dimensions is applied. Results from GWO and SB algorithms are compared with five other meta-heuristic algorithms used in literature for software effort estimation. Experimental results showed that the GWO has comprehensive superiority in terms of accuracy in estimation. The proposed DNN model (GWDNNSB) using meta-heuristic algorithms for initial weights and learning rate selection, produced better results compared to existing work on using DNN for software effort estimation.

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Section: A Problem Formulationmentioning

confidence: 99%

Section: B Contributionsmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

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Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

et al. 2021

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“…The objective function used in this study is the root mean square error (RMSE). This objective function can be accounted for utilizing the following expression [14][15][16][17]:…”

Section: Objective Function Of Pso Modelsmentioning

confidence: 99%

Particle Swarm Optimization Based Approach for Estimation of Costs and Duration of Construction Projects

Khalaf

Çağlar

et al. 2020

Civ Eng J

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Cost and duration estimation is essential for the success of construction projects. The importance of decision making in cost and duration estimation for building design processes points to a need for an estimation tool for both designers and project managers. Particle swarm optimization (PSO), as the tools of soft computing techniques, offer significant potential in this field. This study presents the proposal of an approach to the estimation of construction costs and duration of construction projects, which is based on PSO approach. The general applicability of PSO in the formulated problem with cost and duration estimation is examined. A series of 60 projects collected from constructed government projects were utilized to build the proposed models. Eight input parameters, such as volume of bricks, the volume of concrete, footing type, elevators number, total floors area, area of the ground floor, floors number, and security status are used in building the proposed model. The results displayed that the PSO models can be an alternative approach to evaluate the cost and-or duration of construction projects. The developed model provides high prediction accuracy, with a low mean (0.97 and 0.99) and CoV (10.87% and 4.94%) values. A comparison of the models’ results indicated that predicting with PSO was importantly more precise.

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“…To give a precise estimated cost for project development, in 2019 Venkataiah et al [15] suggested the implementation of hybrid methodology for tuning parameters of COCOMO model. To check the efficiency of the presented model, they used IBMDPS, COCOMO NASA 2 and DESHARNAIS and COCOMO 81.…”

Section: Introductionmentioning

confidence: 99%

Parameter tuning of software effort estimation models using antlion optimization

2020

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In this work, the antlion optimization (ALO) is employed due to its efficiency and wide applicability to estimate the parameters of four modified models of the basic constructive cost model (COCOMO) model. Three tests are carried out to show the effectiveness of ALO: first, it is used with Bailey and Basili dataset for the basic COCOMO Model and Sheta's Model 1 and 2, and is compared with the firefly algorithm (FA), genetic algorithms (GA), and particle swarm optimization (PSO). Second, parameters of Sheta's Model 1 and 2, Uysal's Model 1 and 2 are optimized using Bailey and Basili dataset; results are compared with directed artificial bee colony algorithm (DABCA), GA, and simulated annealing (SA). Third, ALO is used with Basic COCOMO model and four large datasets, results are compared with hybrid bat inspired gravitational search algorithm (hBATGSA), improved BAT (IBAT), and BAT algorithms. Results of Test1 and Test2 show that ALO outperformed others, as for Test3, ALO is better than BAT and IBAT using MAE and the number of best estimations. ALO proofed achieving better results than hBATGSA for datasets 2 and 4 out of the four datasets explored in terms of MAE and the number of best estimates.

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Application of Ant Colony Optimization Techniques to Predict Software Cost Estimation

Cited by 16 publications

References 16 publications

Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

Metaheuristic Algorithms in Optimizing Deep Neural Network Model for Software Effort Estimation

Particle Swarm Optimization Based Approach for Estimation of Costs and Duration of Construction Projects

Parameter tuning of software effort estimation models using antlion optimization

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