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Meta-heuristic and Evolutionary Algorithms for Engineering Optimization
book

Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

by Omid Bozorg-Haddad, Mohammad Solgi, Hugo A. Loáiciga
October 2017
Intermediate to advanced content levelIntermediate to advanced
304 pages
8h 3m
English
Wiley
Content preview from Meta-heuristic and Evolutionary Algorithms for Engineering Optimization

14 Central Force Optimization

Summary

This chapter describes the central force optimization (CFO) algorithm. The basic concepts of the CFO are issued from kinesiology in physics. The CFO resembles the motion of masses under the influence of the gravity field. One of the most important features of the CFO is that it is a deterministic method, which means that each position of a particle (called probe in this method) follows a certain path toward a solution. The following sections relate Newton’s gravitational low and the CFO. The CFO algorithm is explained, and a pseudocode of the algorithm is presented.

14.1 Introduction

The central force optimization (CFO) is a search meta‐heuristic method developed by Formato (2007) based on gravitational kinematics. This algorithm models the motion of airborne probes under effect of gravity and maps the equations’ motion to an optimization scheme. The CFO algorithmic equations are developed for the probes’ positions and the accelerations using the analogy of particle motion in a gravitational field. The CFO is deterministic, which is a variance from most other meta‐heuristic algorithms. Formato (2007) assessed the performance of the CFO algorithm with recognized complex mathematical functions and electronic problems and compared the results with that of other algorithms. Formato (2010) demonstrated the good performance of the CFO algorithm in solving several different functions. Mahmoud (2011) applied the CFO to a microstrip antenna ...

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Publisher Resources

ISBN: 9781119386995Purchase book