By A. Kaveh, V.R. Mahdavi
This booklet offers and applies a singular effective meta-heuristic optimization set of rules known as Colliding our bodies Optimization (CBO) for varied optimization difficulties. the 1st a part of the ebook introduces the recommendations and techniques concerned, whereas the second one is dedicated to the purposes. even though optimum layout of buildings is the most subject, chapters on optimum research and purposes in constructional administration also are included.
This set of rules is predicated on one-dimensional collisions among our bodies, with every one agent answer being regarded as an item or physique with mass. After a collision of 2 relocating our bodies with unique plenty and velocities, those our bodies back separate, with new velocities. This collision reasons the brokers to maneuver towards greater positions within the seek space.
The major set of rules (CBO) is internally parameter self sustaining, environment it except formerly constructed meta-heuristics. This set of rules is better (ECBO) for extra effective purposes within the optimum layout of structures.
The algorithms are carried out in regular desktop programming languages (MATLAB and C++) and major codes are supplied for ease of use.
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Additional info for Colliding Bodies Optimization: Extensions and Applications
4 The Features of CBO This section presents a novel efﬁcient meta-heuristic optimization algorithm called Colliding Bodies Optimization (CBO). From the results obtained of our method, we draw the following conclusions: (i) Using the governing laws from physics, we proposed a simple and efﬁcient algorithm, where these laws determine the movement process of the objects. In this algorithm, each agent solution is considered as the colliding body. After a collision of two moving bodies which have speciﬁed masses and velocities, these bodies are separated with new velocities.
36679. 369562 Fig. 11 Comparison of the convergence curves of the two algorithms for the constrained function II. 69515, respectively. Although, the average result of the 2D-CBO is more than that of Deb . The best results obtained using 2D-CBO with less population size is better than those of the CBO. 11 shows the convergence curves for the CBO and 2D-CBO algorithms. 4 Discussions In this part, a new optimization algorithm, named as 2D-CBO, is developed. The proposed algorithm is a method based on the reformation of the standard CBO algorithm.
It appears that CBO found the best design overall saving about 1000 kg with respect to the optimum currently reported in literature. 3 Size Optimization of Truss Structures 53 Fig. 10 Comparison of the allowable and existing constraints for the 582-bar truss using the DHPSACO. a Stress ratio. b Displacement in the z-direction. c Displacement in the y-direction d Displacement in the x-direction optimized weight observed for CBO in 20 independent optimization runs was lower than for the other meta-heuristic optimization algorithms taken as basis of comparison.
Colliding Bodies Optimization: Extensions and Applications by A. Kaveh, V.R. Mahdavi