Abstract
As is typical of metaheuristic optimization algorithms, particle swarm optimization is guided solely by the objective function. However, experience with separable and roughly separable problems suggests that, for subsets of the decision variables, the use of alternative ‘guide objectives’ may result in improved performance. This paper describes how, through the use of such guide objectives, simple problem domain knowledge may be incorporated into particle swarm optimization and illustrates how such an approach can be applied to both academic optimization problems and a real-world optimization problem from the domain of petroleum engineering.
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Reynolds, A.P., Abdollahzadeh, A., Corne, D.W., Christie, M., Davies, B., Williams, G. (2012). Guide Objective Assisted Particle Swarm Optimization and Its Application to History Matching. In: Coello, C.A.C., Cutello, V., Deb, K., Forrest, S., Nicosia, G., Pavone, M. (eds) Parallel Problem Solving from Nature - PPSN XII. PPSN 2012. Lecture Notes in Computer Science, vol 7492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32964-7_20
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DOI: https://doi.org/10.1007/978-3-642-32964-7_20
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