A New Implementation of Population Based Incremental Learning Method for Optimizations in Electromagnetics

Abstract

To enhance the global search ability of population based incremental learning (PBIL) methods, it is proposed that multiple probability vectors are to be included on available PBIL algorithms. The strategy for updating those probability vectors and the negative learning and mutation operators are thus redefined correspondingly. Moreover, to strike the best tradeoff between exploration and exploitation searches, an adaptive updating strategy for the learning rate is designed. Numerical examples are reported to demonstrate the pros and cons of the newly implemented algorithm. Index Terms-Genetic algorithm (GA), global optimization, inverse problem, population based incremental learning (PBIL) method.

Topology Optimisation Using MPBILs and Multi-Grid Ground Element

“…To balance the reliability and speed of convergence in all iterations, the learning rate needs to adapt. A model of adaptive learning rate has been proposed by Yang et al [15] that satisfies the previous conditions. That model is shown as follows…”

“…Furthermore, it has been found [14] that learning rate was the most affective with search performance of PBIL. Another way to improve the search performance of MPBIL is to use an adaptive learning rate method [15]. This method is categorized as self-learning adaptations, so the effectiveness of this technique needs to be addressed in this research.…”

Benchmarking a Wide Spectrum of Metaheuristic Techniques for the Radio Network Design Problem

“…Although PBIL has been used in very diverse optimization problems ( [62]- [65] are some recent examples), surprisingly it has not been used in many telecommunication studies (only a few cases exist [66]- [69]). …”

Using selfish gene theory to construct mutual information and entropy based clusters for bivariate optimizations

“…A wide variety of EDAs using different techniques to estimate and sample the probability distribution have been proposed for solving different kinds of optimization problems (Baluja 1994;Muhlenbein and Paass 1996;Bonet et al 1997;Baluja and Davies 1997;Muhlenbein 1998, 1999;Harik et al 1999Harik et al , 2006Yang et al 2007;Ahn and Ramakrishna 2008;Yang and Yao 2008;Hong et al 2008;Zhang et al 2008;Zhou et al 2008). In these existing algorithm, a class of EDAs which focus on the bivariate dependency have been applied in the optimization of discrete problems.…”