One Class of Stochastic Local Search Algorithms

Accelerated probabilistic modeling algorithms, presenting stochastic local search (SLS) technique, are considered. General algorithm scheme and specific combinatorial optimization method, using “golden section” rule (GS-method), are given. Convergence rates using Markov chains are received. An overview of current combinatorial optimization techniques is presented

Fractal algorithms for finding global optimal solutions

For solving constrained nonlinear optimization problems, a new algorithm, which is called Fractal Algorithm, is presented. Feasible region is partitioned by fractal combining with golden section. Bad region is deleted, gradually and finally optimal solution remains. The advantages of the local fine structure of fractal and the quick convergence of golden section method are taken. Hence, the fractal algorithm is highly efficient and. highly speedy. The algorithm has the character of strong adaptability to a class of complex function. It requests only that the object function has one order derivative. The minimum can be found at any precision at which a computer can work. Furthermore, this method requests so little memory that it almost can. be implemented on any PC of which the efficiency is almost not influenced. The proof showing convergence of the algorithm, is given. The numerical results show that the algorithm is effective

The ADS general-purpose optimization program

The mathematical statement of the general nonlinear optimization problem is given as follows: find the vector of design variables, X, that will minimize f(X) subject to G sub J (x) + or - 0 j=1,m H sub K hk(X) = 0 k=1,l X Lower I approx less than X sub I approx. less than X U over I i = 1,N. The vector of design variables, X, includes all those variables which may be changed by the ADS program in order to arrive at the optimum design. The objective function F(X) to be minimized may be weight, cost or some other performance measure. If the objective is to be maximized, this is accomplished by minimizing -F(X). The inequality constraints include limits on stress, deformation, aeroelastic response or controllability, as examples, and may be nonlinear implicit functions of the design variables, X. The equality constraints h sub k(X) represent conditions that must be satisfied precisely for the design to be acceptable. Equality constraints are not fully operational in version 1.0 of the ADS program, although they are available in the Augmented Lagrange Multiplier method. The side constraints given by the last equation are used to directly limit the region of search for the optimum. The ADS program will never consider a design which is not within these limits

Optimization of a neutrino factory oscillation experiment

We discuss the optimization of a neutrino factory experiment for neutrino oscillation physics in terms of muon energy, baselines, and oscillation channels (gold, silver, platinum). In addition, we study the impact and requirements for detector technology improvements, and we compare the results to beta beams. We find that the optimized neutrino factory has two baselines, one at about 3000 to 5000km, the other at about 7500km (``magic'' baseline). The threshold and energy resolution of the golden channel detector have the most promising optimization potential. This, in turn, could be used to lower the muon energy from about 50GeV to about 20GeV. Furthermore, the inclusion of electron neutrino appearance with charge identification (platinum channel) could help for large values of \sin^2 2 \theta_{13}. Though tau neutrino appearance with charge identification (silver channel) helps, in principle, to resolve degeneracies for intermediate \sin^2 2 \theta_{13}, we find that alternative strategies may be more feasible in this parameter range. As far as matter density uncertainties are concerned, we demonstrate that their impact can be reduced by the combination of different baselines and channels. Finally, in comparison to beta beams and other alternative technologies, we clearly can establish a superior performance for a neutrino factory in the case \sin^2 2 \theta_{13} < 0.01.Comment: 51 pages, 25 figures, 6 tables, references corrected, final version to appear in Phys. Rev.

Workload Equity in Vehicle Routing Problems: A Survey and Analysis

Over the past two decades, equity aspects have been considered in a growing number of models and methods for vehicle routing problems (VRPs). Equity concerns most often relate to fairly allocating workloads and to balancing the utilization of resources, and many practical applications have been reported in the literature. However, there has been only limited discussion about how workload equity should be modeled in VRPs, and various measures for optimizing such objectives have been proposed and implemented without a critical evaluation of their respective merits and consequences. This article addresses this gap with an analysis of classical and alternative equity functions for biobjective VRP models. In our survey, we review and categorize the existing literature on equitable VRPs. In the analysis, we identify a set of axiomatic properties that an ideal equity measure should satisfy, collect six common measures, and point out important connections between their properties and those of the resulting Pareto-optimal solutions. To gauge the extent of these implications, we also conduct a numerical study on small biobjective VRP instances solvable to optimality. Our study reveals two undesirable consequences when optimizing equity with nonmonotonic functions: Pareto-optimal solutions can consist of non-TSP-optimal tours, and even if all tours are TSP optimal, Pareto-optimal solutions can be workload inconsistent, i.e. composed of tours whose workloads are all equal to or longer than those of other Pareto-optimal solutions. We show that the extent of these phenomena should not be underestimated. The results of our biobjective analysis are valid also for weighted sum, constraint-based, or single-objective models. Based on this analysis, we conclude that monotonic equity functions are more appropriate for certain types of VRP models, and suggest promising avenues for further research.Comment: Accepted Manuscrip