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A Multi Objective Teacher-Learning-Artificial Bee Colony (MOTLABC) Optimization for Software Requirements Selection
Background/Objectives: To select optimal software requirements by introducing Multi-objective Teacher-Learning- Artificial Bee Colony Optimization. Methods/Statistical Analysis: Teaching learning based optimization for the multi-objective software requirements selection has two objectives of minimized cost and maximum client satisfaction. Similarly the constraints namely interaction constraints and cost threshold constraints are considered. However, the efficiency of the software product development can be improved further when more efficient optimization techniques is used for the selection of software requirements along with consideration of more objectives and more constraints in larger real datasets. Findings: In this article, a hybrid optimization technique named Multi-Objective Teacher-Learning Artificial Bee Colony Optimization (MOTLABC) is proposed with set of multiple objectives and constraints. The objectives are minimum cost, maximum client satisfaction, minimum time consumption and maximum reliability. The constraints such as time threshold constraint, interaction constraints and cost threshold constraints are considered. The hybrid approach of MOTLABC with the above objectives improves the collection of set of needs for the development of the software. The Pareto optimal problem occurs in multi objective optimization solutions is resolved by the use of Pareto tournament function. Improvements/Applications: The experimental consequences prove that they obtained results perform improved than algorithms proposed in the literature.
Artificial Bee Colony Optimization, Interaction Constraints, Software Requirements Selection, Teaching Learning Based Optimization.
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