In this paper, we extend TOPSIS (Technique for Order Preference by Similarity Ideal Solution) for solving Large Scale Multiple Objective Programming problems involving fuzzy parameters. These fuzzy parameters are characterized as fuzzy numbers. For such problems, the α-Pareto optimality is introduced by extending the ordinary Pareto optimality on the basis of the α-Level sets of fuzzy numbers. An interactive fuzzy decision making algorithm for generating α-Pareto optimal solution through TOPSIS approach is provided, where a decision maker (DM) is asked to specify the degree α and the relative importance of objectives. Finally, a numerical example is given to clarify the main results developed in the paper.

In this paper, we extend TOPSIS (Technique for Order Preference by Similarity Ideal Solution) for solving Large Scale Multiple Objective Programming problems involving fuzzy parameters. These fuzzy parameters are characterized as fuzzy numbers. For such problems, the α-Pareto optimality is introduced by extending the ordinary Pareto optimality on the basis of the α-Level sets of fuzzy numbers. An interactive fuzzy decision making algorithm for generating α-Pareto optimal solution through TOPSIS approach is provided, where a decision maker (DM) is asked to specify the degree α and the relative importance of objectives. Finally, a numerical example is given to clarify the main results developed in the paper

The main purpose of this research is to propose a novel hybrid memetic searching optimization algorithm, the proposed algorithm integrates the modified whale optimization algorithms with simulated annealing algorithm in a novel approach in order to enhance the searching capabilities of the modified whale optimization algorithm with the help of simulated annealing (SA). The proposed algorithm is used to find the minimum feature subset based on hybrid modified whale optimization algorithms and simulated annealing (WOA2SA, WOA3SA), where SA is embedded into the modified WO algorithms to achieve that good balance between (exploitation) and (exploration) capabilities of the modified algorithms. The resulting memetic algorithm improve the performance of the general classification tasks and hence had been used in the prediction of terrorist group (s) which responsible of the terror attacks on Egypt based on GTD terrorism data. The findings of this research can serve as an alarm tool to minimize the terrorist attacks on a certain region (country).

The main purpose of this research is to develop a hybrid computational intelligent algorithm (framework) as a decision support (DS) tool for terrorism phenomenon that has been defeated for years by governments, countries, and different multiple institutions and hence it needs multiple and integrated research from different science disciplines with a hope of being eliminated in the future. The proposed hybrid prediction algorithm based on integrated different Operations Research (OR) and Decision support tools with Data Mining (DM) techniques especially prediction and classification algorithms as well as different directions of modification and improvements in a number of recent and popular metaheuristics inspired algorithms. The proposed system has been developed, implemented, and evaluated according to different set of assessment measures. Through this study, it was found that, the proposed system is capable of predicting the terrorist group (s) responsible of terror attacks on different regions (countries). The findings of this research may serve as an alarm tool to determine the terrorist groups' networks and so minimize the terrorist attacks.

Sustainability is an important consideration in product design. The sustainable design should fully consider the environmental, social, and economic factors of the product. However, the three factors are often conflicting with each other. This paper aims to strike a balance between these factors and achieve sustainable product design through multi-objective optimization. The three influencing factors of sustainability, namely, the environmental factor, social factor and economic factor, were respectively defined as environmental impact, labor time and labor cost. Then, the product to be designed was represented as a design structure matrix (DSM), a list of all product components and the dependency patterns among these components. On this basis, the non-dominated sorting and cuckoo search were combined into a multi-objective optimization technique to optimize the product functionality. This technique looks for a set of Pareto optimal solutions, each of which represents the structure of modules and the number of modules. The effectiveness of the proposed technique was verified through a case study on a coffee maker. The results show that our technique outperformed the previous optimization methods.