A New Stochastic Optimization Approach — Dolphin Swarm Optimization Algorithm

Accurate Software Effort Estimation is of high importance with regard to Software Project Management. It can be specified as the process for predicting Effort regarding costs, needed for developing software products. A lot of techniques related to software effort estimation were carried out for developing models that are generating optimal estimation accuracy. Swarm intelligence is one such technique. The process-related in selecting the optimum estimation algorithm is expert dependent and complex. The presented study optimizes the estimation using the COCOMO II models by two models: the first model applied the dolphin algorithm, the second model applied suggested hybrid dolphin and bat algorithm (DolBat). By applying the two models on two data set and evaluate with the use of Magnitude of Relative Error(MRE) and Mean Magnitude of Relative Error(MMRE). The results indicate that the dolphin algorithm has better than previous algorithms but the (DolBat) is the best to get the coefficient value of the COCOMO II model. INDEX TERMSBat algorithm, COCOMO II model, dolphin algorithm, effort estimation, echolocation, NASA project dataset.

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“…Dolphins are very smart animals. Also they have significant living habits and bio-logical characteristics, some of them are as follows [23]:…”

Section: Dolphin Swarm Algorithm (Dsa)mentioning

confidence: 99%

Software Cost Estimation Based on Dolphin Algorithm

2020

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“…The performance of the proposed approach has been evaluated using accuracy, sensitivity and specificity measures. Zhang et al [44] Paddy field algorithm 2009 CS [45] Cuckoo search 2009 GSO [46] Group search optimizer 2009 CGS [47] Consultant guided search 2010 BA [48] Bat algorithm 2010 TCO [49] Termite colony optimization 2010 HS [50] Hunting Search 2010 ES [51] Eagle strategy 2010 HSO [52] Hierarchical swarm optimization 2010 FA [53] Firefly algorithm 2010 FOA [54] Fruit fly optimization algorithm 2011 ECO [55] Eco inspired evolutionary algorithm 2011 WSA [56] Weightless swarm algorithm 2011 FPA [57] Flower pollination algorithm 2012 BMO [58] Bird mating optimizer 2012 ACS [59] Artificial cooperative search algorithm 2012 KH [60] Krill herd algorithm 2012 FROGSIM [61] Japanese tree frogs calling algorithm 2012 OptBees [62] The OptBees algorithm 2012 WSA [63] Wolf search algorithm 2012 TGSR [64] The great Salmon run algorithm 2012 DE [65] Dolphin echolocation 2013 SSO [66] Swallow swarm optimization algorithm 2013 EVOA [67] Egyptian vulture optimization algorithm 2013 CSO [68] Chicken swarm optimization 2014 AMO [69] Animal migration optimization 2014 GWO [22] Grey wolf optimization 2014 SSO [70] Shark smell optimization 2014 ALO [71] Ant lion optimizer 2015 BSA [72] Bird swarm algorithm 2015 VCS [73] Virus Colony search 2015 AAA [74] Artificial algae algorithm 2015 DA [75] Dragonfly algorithm 2015 DSOA [76] Dolphin swarm optimization algorithm 2016 CSA [77] Crow search algorithm 2016 WOA …”

Section: A Evolution Based Algorithmsmentioning

confidence: 99%

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

et al. 2021

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Feature selection is a critical and prominent task in machine learning. To reduce the dimension of the feature set while maintaining the accuracy of the performance is the main aim of the feature selection problem. Various methods have been developed to classify the datasets. However, metaheuristic algorithms have achieved great attention in solving numerous optimization problem. Therefore, this paper presents an extensive literature review on solving feature selection problem using metaheuristic algorithms which are developed in the ten years (2009-2019). Further, metaheuristic algorithms have been classified into four categories based on their behaviour. Moreover, a categorical list of more than a hundred metaheuristic algorithms is presented. To solve the feature selection problem, only binary variants of metaheuristic algorithms have been reviewed and corresponding to their categories, a detailed description of them explained. The metaheuristic algorithms in solving feature selection problem are given with their binary classification, name of the classifier used, datasets and the evaluation metrics. After reviewing the papers, challenges and issues are also identified in obtaining the best feature subset using different metaheuristic algorithms. Finally, some research gaps are also highlighted for the researchers who want to pursue their research in developing or modifying metaheuristic algorithms for classification. For an application, a case study is presented in which datasets are adopted from the UCI repository and numerous metaheuristic algorithms are employed to obtain the optimal feature subset.

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“…There are many challenges facing researchers in software project management issues, especially when conducting research on planning and scheduling, one of them is the lack of real data for the planning and scheduling phase of the project as the software companies reserve information related to employees, their salaries and their skills and do not provide any information on how to divide and distribute the work or any information related to management and leadership within the work team. Researchers have tried [24] to provide data For real projects in cooperation with a Jordanian software company, but that information is still few and limited, so researchers resort to using randomly generated data that simulate real projects when conducting research, and despite the good results achieved by the techniques and algorithms used, they are still not ready for use in tools and commercial applications [25].…”

Section: Proposed Workmentioning

confidence: 99%

Application project task scheduling using dolphin swarm technology

Mostafa

A.Alsalman²

2021

IJEECS

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Project task scheduling, which is part of software project management, has great importance in the success or failure of projects as 40% of projects are subject to failure due to poor planning and misuse of human resources, andthis greatly confirms that the wise decisions that are made at this stage leadto reduced Obstacles facing work and leading tosuccess. The work and decision-making is the responsibility of the project manager as he faces all the complexity and critical phases of this critical phase and deals with all its related processes which consist of defining the project tasks and assigning the employees responsible for each task and then preparing project plans. In this research we dealt with project scheduling using The smart dolphins warm algorithm, the project scheduling process is essential in the software engineering industry nowadays and the possibility of finding good solution swhich saves the software company a lot of time and money.<div> </div>

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A New Stochastic Optimization Approach — Dolphin Swarm Optimization Algorithm

Cited by 48 publications

References 32 publications

Software Cost Estimation Based on Dolphin Algorithm

Software Cost Estimation Based on Dolphin Algorithm

Metaheuristic Algorithms on Feature Selection: A Survey of One Decade of Research (2009-2019)

Application project task scheduling using dolphin swarm technology

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