Optimizing efforts in interoperability implementation is considered a key requirement. This way one can effectively sets up, develops, and evolves intra and inter organizational collaboration. Therefore, the objective of the present paper is to initiate a novel method for linear modeling of the interoperability optimization between involved information systems. Interoperability degree is assessed using a novel Particle Swarm Optimization (PSO) model with dynamic neighborhood topology associated to parallel computation. The idea behind using dynamic neighborhood topology is to overcome premature convergence of PSO algorithm, by well exploring and exploiting the search space for a better solution quality. Parallel computation is used to accelerate calculations especially for complex optimization problems. The obtained results demonstrate good performance of the proposed algorithm in solving interoperability optimization.

This paper describes an evolutionary hybrid model linking two algorithms: Particle Swarm Optimization (PSO) and Simulated Annealing (SA). The basic idea behind using a hybrid model is improving the reliability of the obtained results from our first model, namely MPSO (Modified PSO) based on PSO algorithm, by adding SA algorithm which is quite popular for its powerful feature of effective escaping from the trap of local minima. MPSO model uses the concept of evolutionary neighborhoods associated to parallel computation, to overcome to the two essential disadvantages of PSO: high running time and premature convergence. The presented algorithm has two essential operations: first running PSO algorithm in parallel using the new concept of evolutionary neighborhood to obtain a global best solution, then improving the results with SA algorithm to get the global optimal solution. By testing this hybrid algorithm (H-MPSO-SA) on a set of standard benchmark functions and according to the obtained results, the program have given satisfactory results of the hybrid model compared to the basic PSO and MPSO algorithms.

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Fused deposition modeling (FDM) is one of the most used additive manufacturing processes in the current time. Predicting the impact of different 3D printing parameters on the quality of printed parts is one of the critical challenges facing researchers. The present paper aims to examine the effect of three FDM process parameters, namely deposition velocity, extrusion temperature, and raster orientation on the bending strength, stiffness, and deflection at break of polylactic acid (PLA) parts using Taguchi design of experiment technique. The results indicate that the temperature has the highest impact on the mechanical properties of PLA specimens followed by the velocity and the orientation. The optimum composition offering the best mechanical behavior was determined. The optimal predicted response was 159.78 N, 39.92 N/mm, and 12.55 mm for the bending strength, bending stiffness, and deflection at break, respectively. The R2 obtained from analysis of variance (ANOVA) showed good agreement between the experimental results and those predicted using a regression model.

Meta-heuristic PSO has limits, such as premature convergence and high running time, especially for complex optimization problems. In this paper, a description of three parallel models based on the PSO algorithm is developed, on the basis of combining two concepts: parallelism and neighborhood, which are designed according to three different approaches in order to avoid the two disadvantages of the PSO algorithm. The third model, SPM (Spherical-neighborhood Parallel Model), is designed to improve the obtained results from the two parallel NPM (Neighborhood Parallel Model) and MPM (Multi-PSO Parallel Model) models. The experimental results presented in this paper show that SPM model performed much better than both NPM and MPM models in terms of computing time and solution quality.