Anupam Shukla

TL;DR: This paper solves the problem of robotic path planning using a combination of A* algorithm and Fuzzy Inference and the resulting FIS was easily able to plan the path of the robot.

TL;DR: This work utilizes a newly proposed methodology named ‘grey wolf optimization (GWO)’ to solve the path planning problem of three Dimensional UAV, whose task is to find the feasible trajectory while avoiding collision among obstacles and other UAVs.

TL;DR: A comparative analysis of various nature inspired algorithms to select optimal features/variables required for aiding in the classification of affected patients from the rest shows Binary Bat Algorithm outperformed traditional techniques like Particle Swarm Optimization (PSO), Genetic Algorithm and Modified Cuckoo Search Algorithm with a competitive recognition rate on the dataset of selected features.

TL;DR: A new algorithm for solving the problem of path planning in a static environment by making use of an algorithm developed earlier by the authors called Multi-Neuron Heuristic Search (MNHS), a modified A^@?

TL;DR: A new nature inspired meta-heuristics algorithm called Egyptian Vulture Optimization Algorithm which primarily favors combinatorial optimization problems which is derived from the nature, behavior and key skills of the Egyptian Vultures for acquiring food for leading their livelihood.