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Showing 4 results for Ilchi Ghazaan

A. Kaveh , M. Ilchi Ghazaan,
Volume 4, Issue 3 (9-2014)
Abstract

Colliding bodies optimization (CBO) is a new population-based stochastic optimization algorithm based on the governing laws of one dimensional collision between two bodies from the physics. Each agent is modeled as a body with a specified mass and velocity. A collision occurs between pairs of objects to find the global or near-global solutions. Enhanced colliding bodies optimization (ECBO) uses memory to save some best solutions and utilizes a mechanism to escape from local optima. The performances of the CBO and ECBO are shown through truss and frame design optimization problems. The codes of these methods are presented in MATLAB and C++.
A. Kaveh , M. Ilchi Ghazaan,
Volume 5, Issue 1 (1-2015)
Abstract

This paper presents the application of metaheuristic methods to the minimum crossing number problem for the first time. These algorithms including particle swarm optimization, improved ray optimization, colliding bodies optimization and enhanced colliding bodies optimization. For each method, a pseudo code is provided. The crossing number problem is NP-hard and has important applications in engineering. The proposed algorithms are tested on six complete graphs and eight complete bipartite graphs and their results are compared with some existing methods.
A. Kaveh , M. Ilchi Ghazaan,
Volume 5, Issue 2 (3-2015)
Abstract

The failure probability of the structures is one of the challenging problems in structural engineering. To obtain the reliability index introduced by Hasofer and Lind, one needs to solve a nonlinear equality constrained optimization problem. In this study, four of the most recent metaheuristic algorithms are utilized for finding the design point and the failure probability of problems with continuous random variables. These algorithms consist of Improved Ray Optimization, Democratic Particle Swarm Optimization, Colliding Bodies Optimization, and Enhanced Colliding Bodies Optimization. The performance of these algorithms is tested on nineteen engineering optimization problems
A. Kaveh, M. Ilchi Ghazaan,
Volume 7, Issue 3 (7-2017)
Abstract

In this paper, MATLAB code for a recently developed meta-heuristic methodology, the vibrating particles system (VPS) algorithm, is presented. The VPS is a population-based algorithm which simulates a free vibration of single degree of freedom systems with viscous damping. The particles gradually approach to their equilibrium positions that are achieved from current population and historically best position. Two truss towers with 942 and 2386 elements are examined for the validity of the present algorithm; however, the performance VPS has already been proven through truss and frame design optimization problems.



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