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Ajith Abraham
Researcher at Technical University of Ostrava
Publications - 1168
Citations - 36150
Ajith Abraham is an academic researcher from Technical University of Ostrava. The author has contributed to research in topics: Particle swarm optimization & Artificial neural network. The author has an hindex of 86, co-authored 1113 publications receiving 31834 citations. Previous affiliations of Ajith Abraham include Dalian Maritime University & Oklahoma State University–Tulsa.
Papers
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Differential Evolution Using a Neighborhood-Based Mutation Operator
TL;DR: A family of improved variants of the DE/target-to-best/1/bin scheme, which utilizes the concept of the neighborhood of each population member, and is shown to be statistically significantly better than or at least comparable to several existing DE variants as well as a few other significant evolutionary computing techniques over a test suite of 24 benchmark functions.
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Automatic Clustering Using an Improved Differential Evolution Algorithm
TL;DR: Differential evolution has emerged as one of the fast, robust, and efficient global search heuristics of current interest as mentioned in this paper, which has been applied to the automatic clustering of large unlabeled data sets.
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Feature deduction and ensemble design of intrusion detection systems
TL;DR: This study investigated the performance of two feature selection algorithms involving Bayesian networks and Classification and Regression Trees and an ensemble of BN and CART and proposed an hybrid architecture for combining different feature selection algorithm for real world intrusion detection.
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.
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A hybrid genetic algorithm and bacterial foraging approach for global optimization
TL;DR: A hybrid approach involving genetic algorithms (GA) and bacterial foraging algorithms for function optimization problems and results clearly illustrate that the proposed approach is very efficient and could easily be extended for other global optimization problems.