Bayesian learning provides a firm theoretical basis of the design and exploitation of algorithms in data-streams processing (preprocessing, change detection, hypothesis testing, clustering, etc).. Primarily, it relies on a recursive parameter estimation of a firmly bounded complexity.
This paper discusses the exploitation of genetic algorithm optimisation for improving condenser maintenance scheduling in a thermal power plant.
Moreover, possible exploitation of our algorithm in the fault detection issue is discussed through a simulation of an abrupt rotor short-circuit.
In ALPSO, we employ a self-learning based candidate generation strategy to ensure the exploration ability, and a competitive learning based prediction strategy to guarantee exploitation of the algorithm.
Xu et al. [59] developed a bee-swarm genetic algorithm for designing DNA sequences that satisfied some combinatorial and thermodynamic constraints, in which the optimum individual of population selected as a queen bee and a random population was introduced to reinforce the exploitation of genetic algorithm and increase the diversity of population.
In this method, a reinforced memory strategy is designed to update the local leaders of particles for avoiding the degradation of outstanding genes in the particles, and a uniform combination is proposed to balance the local exploitation and the global exploration of algorithm.
In this algorithm, a reinforced memory strategy is designed to update the local leaders of particles for avoiding the degradation of outstanding genes in the particles, and a uniform combination is proposed to balance the local exploitation and the global exploration of algorithm.
Consequently, effective exploitation of parallel algorithms is of high relevance to Bayesian computation.
In addition, in order to make full use of and balance the exploration and exploitation of the GWO algorithm, we introduced a nonlinear control parameter strategy, i.e., the control parameter of →a is nonlinearly increased over the course of iterations.
There are two restrictions to the exploitation of the AWA coexistence algorithm.
