in most of the real-life applications we deal with the problem of transporting some special fruits, as banana, which has particular production and distribution processes. in this paper we restrict our attention to formulating and solving a new bi-criterion problem on a network in which in addition to minimizing the traversing costs, admissibility of the quality level of fruits is a main objecti...

In this paper, a procedure has been introduced to the multi-objective optimal design of semi-active tuned mass dampers (SATMDs) with variable stiffness for nonlinear structures considering soil-structure interaction under multiple earthquakes. Three bi-objective optimization problems have been defined by considering the mean of maximum inter-story drift as safety criterion of structural compone...

Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural n...

Deep learning is one of the subsets of machine learning that is widely used in Artificial Intelligence (AI) field such as natural language processing and machine vision. The learning algorithms require optimization in multiple aspects. Generally, model-based inferences need to solve an optimized problem. In deep learning, the most important problem that can be solved by optimization is neural n...

Structural  design  optimization  usually  deals  with  multiple  conflicting  objectives  to  obtain the minimum construction cost, minimum weight, and maximum safety of the final design. Therefore, finding the optimum design is hard and time-consuming for  such problems.  In this paper, we borrow the basic concept of multi-criterion decision-making and combine it with  Particle  Swarm  Optimi...

The ultimate goal of optimization is to find the minimizer of a target function. However, typical criteria for active optimization often ignore the uncertainty about the minimizer. We propose a novel criterion for global optimization and an associated sequential active learning strategy using Gaussian processes. Our criterion is the reduction of uncertainty in the posterior distribution of the ...

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The optimization criterion and a practically feasible new algorithm is stated for the optimization of the index assignments of a multiple description unconstrained vector quantizer with an arbitrary number of descriptions. In the simulations, the index-optimized multiple description vector quantizer achieves significant gains in source SNR over scalar multiple description schemes.

Abstract Surrogate-assisted evolutionary algorithms have been paid more and attention to solve computationally expensive problems. However, model management still plays a significant importance in searching for the optimal solution. In this paper, new method is proposed measure approximation uncertainty, which differences between solution its neighbour samples decision space, ruggedness of obje...

This article addresses the problem of derivative-free (singleor multi-objective) optimization subject to multiple inequality constraints. Both the objective and constraint functions are assumed to be smooth, non-linear and expensive to evaluate. As a consequence, the number of evaluations that can be used to carry out the optimization is very limited, as in complex industrial design optimizatio...