Traditional optimization algorithms search for a single global optimum that maximizes (or minimizes) the objective function. Multimodal highest peaks in space can be more than one. Quality-Diversity are recent addition to evolutionary computation toolbox do not only set of local optima, but instead try illuminate space. In effect, they provide holistic view how high-performing solutions distrib...

The paper discusses the application of multi-stage stochastic optimization for managing and optimizing expected returns versus risk, and contrasts static (single-stage) versus dynamic (multi-stage) portfolio optimization. We present how to best fund a pool of similar fixed rate mortgages through issuing bonds, callable and non-callable, of various maturities using stochastic optimization. We di...

stochastic approach to vehicle routing problem: development and theories abstract in this article, a chance constrained (ccp) formulation of the vehicle routing problem (vrp) is proposed. the reality is that once we convert some special form of probabilistic constraint into their equivalent deterministic form then a nonlinear constraint generates. knowing that reliable computer software for lar...

A stochastic approximation method for optimizing a class of discrete functions is considered. The procedure is a version of the Simultaneous Perturbation Stochastic Approximation (SPSA) method that has been modified to obtain a stochastic optimization method for cost functions defined on a grid of points in Euclidean p-space having integer components. We discuss the algorithm and examine its co...

This paper provides an overview of the one-stage R&D portfolio optimization problem. It provides a novel problem model that can be solved with stochastic combinatorial optimization methods. Current solution methods are reviewed an a new method, Stochastic Gradient Portfolio Optimization (SGPO), is proposed. We proved global convergence under certain conditions. SGPO is numerically compared to c...

the main objective of this paper is to introduce a new intelligent optimization technique that uses a predictioncorrectionstrategy supported by a recurrent neural network for finding a near optimal solution of a givenobjective function. recently there have been attempts for using artificial neural networks (anns) in optimizationproblems and some types of anns such as hopfield network and boltzm...

Stochastic optimization—those problems that involve random variables—is a fundamental challenge in many disciplines. Unfortunately, current solvers for stochastic optimization restrictively require finiteness by either replacing the original problem with a sample average surrogate, or by having complete knowledge of a finite population. To help alleviate this restriction, we state a general, no...