Abstract
Particle swarm optimization is a metaheuristic, evolutionary global optimization method. The PSO algorithm is inspired from swarm behavior and categorized as swarm intelligence algorithm. It is a robust, simple, easy to understand, easy to implement, efficient, and popular algorithm. From the inception of the particle swarm optimization algorithm, it has undergone multiple changes. It has a remarkable ability to select significant data from the pool of infinite and inconsistent information. The variants of particle swarm optimization methods have applied on various academic, scientific, and industrial applications. The uses of swarm intelligence methods increase effectiveness of the application in a simple way. It improves the large application domain of engineering and scientific activities. Mainly, the paper analyzes particle swarm optimization algorithms that are used for software development related activity. Software effort is a process of calculating the amount of effort required to develop a software project. The software effort estimation activity includes analytical calculation for size, cost, time, and effort required to accomplish a software project.
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Kahndelwal, M.K., Sharma, N. (2023). A Survey on Applications of Particle Swarm Optimization Algorithms for Software Effort Estimation. In: Shukla, P.K., Mittal, H., Engelbrecht, A. (eds) Computer Vision and Robotics. CVR 2023. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-4577-1_32
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