Speed Genetic Algorithm Research Articles

An Improved Bacterial Foraging Algorithm for Multi-modal Problems

In order to improve the performance of BFO (bacterial foraging optimization) algorithm in multi-modal problems, the algorithm is optimized and improved in this paper. In the chemotaxis operation, the gradient accumulation variable swimming step size is realized, and the convergence speed of the algorithm is accelerated. In the reproduction operation, a new population competition mechanism is added, and a double-index sorting is realized, which improves the ability of the algorithm to jump out of the local optimum and the diversity of the population. In the migration operation, the vitality index of bacteria is added to dynamically affect the migration probability, ensure the survival probability of elite individuals, and further improve the global optimization ability of the algorithm. Finally, a multi-modal function is selected to test. Compared with the genetic algorithm, the improved BFO algorithm is better than the genetic algorithm in speed and quality.

Structural System Identification in the Time Domain using Evolutionary and Behaviorally Inspired Algorithms and their Hybrids

In this study, parametric identification of structural properties such as stiffness and damping is carried out using acceleration responses in the time domain. The process consists of minimizing the difference between the experimentally measured and theoretically predicted acceleration responses. The unknown parameters of certain numerical models, viz., a ten degree of freedom lumped mass system, a nine member truss and a non-uniform simply supported beam are thus identified. Evolutionary and behaviorally inspired optimization algorithms are used for minimization operations. The performance of their hybrid combinations is also investigated. Genetic Algorithm (GA) is a well known evolutionary algorithm used in system identification. Recently Particle Swarm Optimization (PSO), a behaviorally inspired algorithm, has emerged as a strong contender to GA in speed and accuracy. The discrete Ant Colony Optimization (ACO) method is yet another behaviorally inspired method studied here. The performance (speed and accuracy) of each algorithm alone and in their hybrid combinations such as GA with PSO, ACO with PSO and ACO with GA are extensively investigated using the numerical examples with effects of noise added for realism. The GA+PSO hybrid algorithm was found to give the best performance in speed and accuracy compared to all others. The next best in performance was pure PSO followed by pure GA. ACO performed poorly in all the cases.

高速GAを用いたファジー形負荷周波数制御のための調整パラメータの決定手法

高速GAを用いたファジー形負荷周波数制御のための調整パラメータの決定手法