S
Shimpi Singh Jadon
Researcher at Indian Institutes of Information Technology
Publications - 23
Citations - 1400
Shimpi Singh Jadon is an academic researcher from Indian Institutes of Information Technology. The author has contributed to research in topics: Swarm intelligence & Artificial bee colony algorithm. The author has an hindex of 10, co-authored 18 publications receiving 1060 citations. Previous affiliations of Shimpi Singh Jadon include Indian Institute of Information Technology and Management, Gwalior.
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Proceedings ArticleDOI
Inertia Weight strategies in Particle Swarm Optimization
Jagdish Chand Bansal,Paramjeet Singh,Mukesh Saraswat,Abhishek Verma,Shimpi Singh Jadon,Ajith Abraham +5 more
TL;DR: 15 relatively recent and popular Inertia Weight strategies are studied and their performance on 05 optimization test problems is compared to show which are more efficient than others.
Journal ArticleDOI
Spider Monkey Optimization algorithm for numerical optimization
TL;DR: The proposed swarm intelligence approach is named as Spider Monkey Optimization (SMO) algorithm and can broadly be classified as an algorithm inspired by intelligent foraging behavior of fission–fusion social structure based animals.
Journal ArticleDOI
Artificial bee colony algorithm: a survey
TL;DR: A review on Artificial bee colony ABC developments, applications, comparative performance and future research perspectives is presented.
Journal ArticleDOI
Hybrid Artificial Bee Colony algorithm with Differential Evolution
TL;DR: A hybridization of ABC and DE algorithms to develop a more efficient meta-heuristic algorithm than ABC andDE is proposed and results indicate that HABCDE would be a competitive algorithm in the field of meta- heuristics.
Journal ArticleDOI
Accelerating Artificial Bee Colony algorithm with adaptive local search
TL;DR: Two modifications are proposed in the basic version of ABC to deal with these drawbacks: solution update strategy is modified by incorporating the role of fitness of the solutions and a local search based on greedy logarithmic decreasing step size is applied.