Search results for " Programming"
showing 10 items of 1616 documents
On Constraint Handling in Surrogate-Assisted Evolutionary Many-Objective Optimization
2016
Surrogate-assisted evolutionary multiobjective optimization algorithms are often used to solve computationally expensive problems. But their efficacy on handling constrained optimization problems having more than three objectives has not been widely studied. Particularly the issue of how feasible and infeasible solutions are handled in generating a data set for training a surrogate has not received much attention. In this paper, we use a recently proposed Kriging-assisted evolutionary algorithm for many-objective optimization and investigate the effect of infeasible solutions on the performance of the surrogates. We assume that constraint functions are computationally inexpensive and consid…
A branch-and-cut algorithm for the Orienteering Arc Routing Problem
2016
[EN] In arc routing problems, customers are located on arcs, and routes of minimum cost have to be identified. In the Orienteering Arc Routing Problem (OARP),in addition to a set of regular customers that have to be serviced, a set of potential customers is available. From this latter set, customers have to be chosen on the basis of an associated profit. The objective is to find a route servicing the customers which maximize the total profit collected while satisfying a given time limit on the route.In this paper, we describe large families of facet-inducing inequalities for the OARP and present a branch-and-cut algorithm for its solution. The exact algorithm embeds a procedure which builds…
A new approach for critical resources allocation
2009
This paper presents a solution based on Artificial Intelligence using Multi-objective Genetic Algorithms to optimize the allocation of teachers and classrooms. The implementation was created in order to optimize the process in both cases, allowing them to compete so as to establish a balance and arrive at a feasible solution quickly and efficiently.
Portfolios with fuzzy returns: Selection strategies based on semi-infinite programming
2008
AbstractThis paper provides new models for portfolio selection in which the returns on securities are considered fuzzy numbers rather than random variables. The investor's problem is to find the portfolio that minimizes the risk of achieving a return that is not less than the return of a riskless asset. The corresponding optimal portfolio is derived using semi-infinite programming in a soft framework. The return on each asset and their membership functions are described using historical data. The investment risk is approximated by mean intervals which evaluate the downside risk for a given fuzzy portfolio. This approach is illustrated with a numerical example.
Complementary Judgment Matrix Method with Imprecise Information for Multicriteria Decision-Making
2018
The complementary judgment matrix (CJM) method is an MCDA (multicriteria decision aiding) method based on pairwise comparisons. As in AHP, the decision-maker (DM) can specify his/her preferences using pairwise comparisons, both between different criteria and between different alternatives with respect to each criterion. The DM specifies his/her preferences by allocating two nonnegative comparison values so that their sum is 1. We measure and pinpoint possible inconsistency by inconsistency errors. We also compare the consistency of CJM and AHP trough simulation. Because preference judgments are always more or less imprecise or uncertain, we introduce a way to represent the uncertainty throu…
State-Feedback Stabilization for a Class of Stochastic Feedforward Nonlinear Time-Delay Systems
2013
We investigate the state-feedback stabilization problem for a class of stochastic feedforward nonlinear time-delay systems. By using the homogeneous domination approach and choosing an appropriate Lyapunov-Krasovskii functional, the delay-independent state-feedback controller is explicitly constructed such that the closed-loop system is globally asymptotically stable in probability. A simulation example is provided to demonstrate the effectiveness of the proposed design method.
The Multiple Multidimensional Knapsack with Family-Split Penalties
2021
Abstract The Multiple Multidimensional Knapsack Problem with Family-Split Penalties (MMdKFSP) is introduced as a new variant of both the more classical Multi-Knapsack and Multidimensional Knapsack Problems. It reckons with items categorized into families and where if an individual item is selected to maximize the profit, all the items of the same family must be selected as well. Items belonging to the same family can be assigned to different knapsacks; however, in this case, split penalties are incurred. This problem arises in resource management of distributed computing contexts and Service Oriented Architecture environments. An exact algorithm based on the exploitation of a specific combi…
GRASP with path relinking heuristics for the antibandwidth problem
2010
This article proposes a linear integer programming formulation and several heuristics based on GRASP and path relinking for the antibandwidth problem. In the antibandwidth problem, one is given an undirected graph with n nodes and must label the nodes in a way that each node receives a unique label from the set {1, 2,…,n}, such that, among all adjacent node pairs, the minimum difference between the node labels is maximized. Computational results show that only small instances of this problem can be solved exactly (to optimality) with a commercial integer programming solver and that the heuristics find high-quality solutions in much less time than the commercial solver. © 2010 Wiley Periodic…
A New Crowded Comparison Operator in Constrained Multiobjective Optimization for Capacitors Sizing and Siting in Electrical Distribution Systems
2005
This paper presents a new Crowded Comparison Operator (CCO) for NSGA-II to solve the Multiobjective and constrained problem of optimal capacitors placement in electrical distribution systems.
Scatter Search and Local NLP Solvers: A Multistart Framework for Global Optimization
2007
The algorithm described here, called OptQuest/NLP or OQNLP, is a heuristic designed to find global optima for pure and mixed integer nonlinear problems with many constraints and variables, where all problem functions are differentiable with respect to the continuous variables. It uses OptQuest, a commercial implementation of scatter search developed by OptTek Systems, Inc., to provide starting points for any gradient-based local solver for nonlinear programming (NLP) problems. This solver seeks a local solution from a subset of these points, holding discrete variables fixed. The procedure is motivated by our desire to combine the superior accuracy and feasibility-seeking behavior of gradie…