in financial matters, portfolio can be interpreted as a combination or a series of investments hold by an institution or a person. portfolio optimization is one of the most important concerns of investors for maximizing the portfolio in financial markets. the formation of portfolio is a vital and critical decision for the companies.  in fact, the selection of portfolio is to specify the capital...

Multi-objective optimization using an evolutionary computation technique is used extensively for solving conflicting multi-objective optimization problems. In this work, an improved strategy of multi-objective differential evolution (MODE) where the mutation strategy is changed to a trigonometric mutation approach is proposed. The proposed strategy along with other well known strategies of MODE...

arguably, the natural gas transmission pipeline infrastructure in iran represents one of the largest andmost complex mechanical systems in the world. the optimization of large gas trunk lines known asigat results in reduced fuel consumption or higher capability and improves pipeline operation. in thecurrent study, a single-objective optimization was conducted for khormoj compressor station on t...

This paper presents a performance evaluation of Vector Evaluated Gravitational Search Algorithm (VEGSA), namely VEGSA-I and VEGSA-II algorithms, for multi-objective optimization problems. The VEGSA algorithms use a number of populations of particles. In particular, a population of particles corresponds to one objective function to be minimized or maximized. Simultaneous minimization or maximiza...

In recent years, the application of metaheuristic techniques to solve multi-objective optimization problems (MOPs) has become an active research area. Solving these kinds of problems involves obtaining a set of Pareto-optimal solutions in such a way that the corresponding Pareto front fulfills the requirements of convergence to the true Pareto front and uniform diversity. Most studies on metahe...

This paper deals with a bi-objective hybrid no-wait flowshop scheduling problem minimizing the makespan and total weighted tardiness, in which we consider transportation times between stages. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time by using traditional approaches and optimization tools is extremely difficult. This paper presen...

Dimension reduction or feature selection is thought to be the backbone of big data applications in order improve performance. Many scholars have shifted their attention recent years science and analysis for real-time using integration. It takes a long time humans interact with data. As result, while handling high workload distributed system, it necessary make elastic scalable. In this study, su...

We introduce a novel approximation method for multiobjective optimization problems called PAINT–SiCon. The method can construct consistent parametric representations of Pareto sets, especially for nonconvex problems, by interpolating between nondominated solutions of a given sampling both in the decision and objective space. The proposed method is especially advantageous in computationally expe...

We demonstrate the applicability of a new PAINT method to speed up iterations of interactive methods in multiobjective optimization. As our test case, we solve a computationally expensive non-linear, five-objective problem of designing and operating a wastewater treatment plant. The PAINT method interpolates between a given set of Pareto optimal outcomes and constructs a computationally inexpen...