A QSAR classification model of skin sensitization potential based on improving binary crow search algorithm

Classifying of skin sensitization using the quantitative structure-activityrelationship (QSAR) model is important. Applying descriptor selection isessential to improve the performance of the classification task. Recently, abinary crow search algorithm (BCSA) was proposed, which has been successfully applied to solve variable selection. In this work, a new time-varyingtransfer function is proposed to improve the exploration and exploitation capability of the BCSA in selecting the most relevant descriptors in QSAR classification model with high classification accuracy and short computing time.The results demonstrated that the proposed method is reliable and can reasonably separate the compounds according to sensitizers or non-sensitizerswith high classification accuracy

Application of modern metaheuristic algorithms in optimization ofprocess planning

Optimizacija tehnoloških procesa pripada grupi kompleksnih problema kod kojih je akcenat stavljen na određivanje redosleda zahvata obrade i optimalnu selekciju varijanti tehnoloških resursa među kojima se izdvajaju mašine alatke, rezni alati i smerovi prilaza reznih alata. Optimizacija se vrši minimiziranjem funkcije cilja koja je formulisana na bazi troškova, odnosno vremena realizacije zahvata obrade delova prizmatičnog i rotacionog oblika. Pored toga, pravila i odnosi prethođenja među tipskim tehnološkim oblicima i zahvatima obrade formiraju tzv. ograničenja prethođenja koja omogućavaju pronalaženje izvodljivih rešenja usklađenih sa tehnološkim zahtevima razmatranih mašinskih delova. Predloženi metaheuristički algoritmi za rešavanje ovog problema su algoritmi vrane, sivog vuka i grbavog kita. Pored teorijske analize ovih metoda izvršena je verifikacija njihovih performansi na šest različitih eksperimentalnih studija.Optimization of process planning belongs to the group of complex problems in which the emphasis is placed on determining the sequence of machining operations and the optimal selection of variants of technological resources such as machines, cutting tools and tool approach directions. Optimization is achieved by minimizing the objective function which is formulated on the basis of cost and time required for performing all the operations for prismatic or rotational parts. In addition, precedence rules and relationships among features and machining operations define so called precedence constraints which aid in finding feasible solutions that are complied with technological requirements of considered mechanical parts. The proposed metaheuristic algorithms for solving this problem are crow search optimization, grey wolf optimizer and whale optimization algorithm. Beside the theoretical analysis of these methods, verification of their performances was done on six different experimental studies