This paper considers an open shop scheduling problem that minimizes bi-objectives, namely makespan and total tardiness. This problem, due to its complexity, is ranked in the class of NP-hard problems. In this case, traditional approaches cannot reach to an optimal solution in a reasonable time. Thus, we propose an efficient method based on multi-objective simulated annealing and ant colony optimization, in order to solve the given problem. Finally, we compare our computational results with a well-known multi-objective genetic algorithm, namely NSGA II. Comparisons are made in single objective case too. The outputs show the encouraging results in the form of the solution quality.

This paper presents a hybrid efficient method namely hybrid immune algorithm (HIA) based on artificial immune algorithm (AIA) and bacterial optimization for clustering problems. Four local searches on the basis of heuristic rules for the given clustering problem are designed and applied. This proposed method is implemented and tested on two real datasets. Further, its performance is compared with other well-known meta-heuristics, such as ACO, GA, simulated annealing (SA), and tabu search (TS). At last, paired comparison t-test is also applied to proof the efficiency of our proposed method. The associated outputs give very encouraging results.

An analysis was performed to evaluate the strength of pin-loaded composite and aluminum joints. The analysis involved using three classifiers: decision tree, adaptive neuro fuzzy inference system and the combination of two. By using the well-known C4.5 algorithm, as a quick process, the structure of fuzzy inference system (number of membership functions and fuzzy rules) could be roughly estimated. Then, the parameter identification is carried out by adaptive neuro-fuzzy system. The comparison of performance of three methods indicates that mentioned hybridization speeds up learning processes and reduced errors.