V
V.R. Mahdavi
Researcher at Iran University of Science and Technology
Publications - 24
Citations - 1186
V.R. Mahdavi is an academic researcher from Iran University of Science and Technology. The author has contributed to research in topics: Truss & Particle swarm optimization. The author has an hindex of 13, co-authored 23 publications receiving 961 citations.
Papers
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Journal ArticleDOI
Colliding bodies optimization: A novel meta-heuristic method
Ali Kaveh,V.R. Mahdavi +1 more
TL;DR: In this paper, the authors presented a novel efficient meta-heuristic optimization algorithm called Colliding Bodies Optimization (CBO), which is based on one-dimensional collisions between bodies, with each agent solution being considered as an object or body with mass.
Journal ArticleDOI
Colliding Bodies Optimization method for optimum design of truss structures with continuous variables
Ali Kaveh,V.R. Mahdavi +1 more
TL;DR: A new and simple optimization algorithm is presented to solve weight optimization of truss structures with continuous variables and its capability in solving the present optimization problems is demonstrated.
Journal ArticleDOI
Colliding Bodies Optimization method for optimum discrete design of truss structures
Ali Kaveh,V.R. Mahdavi +1 more
TL;DR: In this paper, the authors implemented the recently developed meta-heuristic algorithm Colliding Bodies Optimization (CBO) for the optimization of truss structures with discrete sizing variables.
Book
Colliding Bodies Optimization: Extensions and Applications
Ali Kaveh,V.R. Mahdavi +1 more
TL;DR: This book presents and applies a novel efficient meta-heuristic optimization algorithm called Colliding Bodies Optimization (CBO) for various optimization problems and introduces the concepts and methods involved.
Journal ArticleDOI
A hybrid CBO-PSO algorithm for optimal design of truss structures with dynamic constraints
Ali Kaveh,V.R. Mahdavi +1 more
TL;DR: An efficient hybrid algorithm that utilizes the recently developed colliding bodies optimization (CBO) algorithm as the main engine and uses the positive properties of the particle swarm optimization (PSO) algorithm to increase the efficiency of the CBO.