An evolutionary dynamic multi-objective optimization algorithm based on center-point prediction and sub-population autonomous guidance

Dynamic multi-objective optimization problems (DMOPs) provide a challenge in that objectives conflict each other and change over time. In this paper, a hybrid approach based on prediction and autonomous guidance is proposed, which responds the environmental changes by generating a new population. According to the position of historical population, a part of the population is generated by predicting roughly and quickly. In addition, another part of the population is generated by autonomous guidance. A sub-population from current population evolves several generations independently, which guides the current population into the promising area. Compared with other three algorithms on a series of benchmark problems, the proposed algorithm is competitive in convergence and diversity. Empirical results indicate its superiority in dealing with dynamic environments

Multivariable teaching-learning-based optimization (MTLBO) algorithm for estimating the structural parameters of the buried mass by magnetic data

U ovom radu je predstavljen prirodno utemeljen multivarijabilni algoritam optimizacije poučavanjem-učenjem (MTLBO). MTLBO algoritam tijekom iterativnog postupka može procijeniti najbolje vrijednosti parametara podzemnih struktura (model) u višepredmetnom problemu. Algoritam djeluje u dvije računske faze: fazi učitelja i fazi učenika. Glavna svrha algoritma MTLBO je mijenjati naučene vrijednosti te poboljšavajući tako vrijednosti parametara modela dovesti do optimalnog rješenja. Varijable svakog učenika (model) su: dubina (z), koeficijent amplitude (k), faktor oblika (q), kut učinkovite magnetizacije (θ) i parametri osi (x0). U radu je korištena MTLBO metoda na podacima magnetskih anomalija uzrokovanih podzemnim strukturama jednostavnog geometrijskog oblika, poput sfere i vodoravno postavljenog cilindra. Učinkovitost MTLBO metode također je proučavana na šumom kontaminiranim sintetičkim podacima, budući da su dobiveni prihvatljivi rezultati. MTLBO metoda je primijenjena za interpretaciju četiri profila magnetske anomalije u Iranu, Brazilu i Indiji.This paper presents a nature-based algorithm, titled multivariable teaching-learning-based optimization (MTLBO) algorithm. MTLBO algorithm during an iterative process can estimates the best values of the buried structure (model) parameters in a multi-objective problem. The algorithm works in two computational phases: the teacher phase and the learner phase. The major purpose of the MTLBO algorithm is to modify the value of the learners and thus, improving the value of the model parameters which leads to the optimal solution. The variables of each learner (model) are the depth (z), amplitude coefficient (k), shape factor (q), angle of effective magnetization (θ) and axis location (x0) parameters. We employ MTLBO method for the magnetic anomalies caused by the buried structures with a simple geometric shape such as sphere and horizontal cylinder. The efficiency of the MTLBO is also studied by noise corruption synthetic data, as the acceptable results were obtained. We have applied the MTLBO for the interpretation of the four magnetic anomaly profiles from Iran, Brazil and India