Rank-Based Nondomination Set Identification with Preprocessing. - Vikas Palakonda; Rammohan Mallipeddi (ICSI (2), 2016)

Nondominated sorting based on sum of objectives. - Vikas Palakonda; Trinadh Pamulapati; Rammohan Mallipeddi; Partha P. Biswas; Kalyana Chakravarthy Veluvolu (SSCI, 2017)

Pareto Dominance-Based Algorithms With Ranking Methods for Many-Objective Optimization. - Vikas Palakonda; Rammohan Mallipeddi (IEEE Access, 2017)

Differential Evolution with Stochastic Selection for Uncertain Environments: A Smart Grid Application. - Vikas Palakonda; Noor H. Awad; Rammohan Mallipeddi; Mostafa Z. Ali; Kalyana C. Veluvolu; Ponnuthurai N. Suganthan (CEC, 2018)

Ensemble of Pareto-based Selections for Many-objective optimization. - Samira Ghorbanpour; Vikas Palakonda; Rammohan Mallipeddi (SSCI, 2018)

Pareto Dominance-based MOEA with Multiple Ranking methods for Many-objective Optimization. - Vikas Palakonda; Samira Ghorbanpour; Rammohan Mallipeddi (SSCI, 2018)

KnEA with Ensemble Approach for Parameter Selection for Many-Objective Optimization. - Vikas Palakonda; Rammohan Mallipeddi (BIC-TA (1), 2019)

Differential Evolutionary (DE) Based Interactive Recoloring Based on YUV Based Edge Detection for Interior Design. - Fitria Wulandari Ramlan; Vikas Palakonda; Rammohan Mallipeddi (ICTC, 2019)

MOEA with Approximate Nondominated Sorting Based on Sum of Normalized Objectives. - Vikas Palakonda; Rammohan Mallipeddi (SEMCCO/FANCCO, 2019)

An Evolutionary Algorithm for Multi and Many-Objective Optimization With Adaptive Mating and Environmental Selection. - Vikas Palakonda; Rammohan Mallipeddi (IEEE Access, 2020)

Multi-objective Evolutionary Algorithm based on Ensemble of Initializations for Overlapping Community Detection. - Jamshid Yusupov; Vikas Palakonda; Rammohan Mallipeddi; Kalyana Chakravarthy Veluvolu (ICEIC, 2021)

An Generational SDE based Indicator for Multi and Many-objective optimization. - Jamshid Yusupov; Vikas Palakonda; Samira Ghorbanpour; Rammohan Mallipeddi; Kalyana Chakravarthy Veluvolu (ICAIIC, 2021)

An ensemble approach with external archive for multi- and many-objective optimization with adaptive mating mechanism and two-level environmental selection. - Vikas Palakonda; Rammohan Mallipeddi; Ponnuthurai Nagaratnam Suganthan (Inf. Sci., 2021)

An adaptive neighborhood based evolutionary algorithm with pivot- solution based selection for multi- and many-objective optimization. - Vikas Palakonda; Jae-Mo Kang; Heechul Jung (Inf. Sci., 2022)

Many-Objective Real-World Engineering Problems: A Comparative Study of State-of-the-Art Algorithms. - Vikas Palakonda; Jae-Mo Kang (IEEE Access, 2023)

An effective ensemble framework for Many-Objective optimization based on AdaBoost and K-means clustering. - Vikas Palakonda; Jae-Mo Kang; Heechul Jung (Expert Syst. Appl., 2023)

Pre-DEMO: Preference-Inspired Differential Evolution for Multi/Many-Objective Optimization. - Vikas Palakonda; Jae-Mo Kang (IEEE Trans. Syst. Man Cybern. Syst., 2023)

Erratum to "Pre-DEMO: Preference-Inspired Differential Evolution for Multi/Many-Objective Optimization". - Vikas Palakonda; Jae-Mo Kang (IEEE Trans. Syst. Man Cybern. Syst., 2023)

Benchmarking Real-World Many-Objective Problems: A Problem Suite With Baseline Results. - Vikas Palakonda; Jae-Mo Kang; Heechul Jung (IEEE Access, 2024)

Clustering-Aided Grid-Based One-to-One Selection-Driven Evolutionary Algorithm for Multi/Many-Objective Optimization. - Vikas Palakonda; Jae-Mo Kang; Heechul Jung (IEEE Access, 2024)

RandMixAugment: A Novel Unified Technique for Region- and Image-Level Data Augmentations. - Yosoeb Shin; Vikas Palakonda; Sangseok Yun; Il-Min Kim 001; Seon-Gon Kim; Sang-Mi Park; Jae-Mo Kang (IEEE Access, 2024)

OCR-Diff: A Two-Stage Deep Learning Framework for Optical Character Recognition Using Diffusion Model in Industrial Internet of Things. - Chaewon Park; Vikas Palakonda; Sangseok Yun; Il-Min Kim 001; Jae-Mo Kang (IEEE Internet Things J., 2024)

Metaheuristics for pruning convolutional neural networks: A comparative study. - Vikas Palakonda; Jamshid Tursunboev; Jae-Mo Kang; Sunghwan Moon (Expert Syst. Appl., 2025)

DeCo-MeSC: Deep Compression-Based Memory-Constrained Split Computing Framework for Cooperative Inference of Neural Network. - Mingyu Sung; Vikas Palakonda; Il-Min Kim 001; Sangseok Yun; Jae-Mo Kang (IEEE Trans. Veh. Technol., 2025)