Constraint handling strategies in Genetic Algorithms application to optimal batch plant design

Optimal batch plant design is a recurrent issue in Process Engineering, which can be formulated as a Mixed Integer Non-Linear Programming(MINLP) optimisation problem involving specific constraints, which can be, typically, the respect of a time horizon for the synthesis of various products. Genetic Algorithms constitute a common option for the solution of these problems, but their basic operating mode is not always wellsuited to any kind of constraint treatment: if those cannot be integrated in variable encoding or accounted for through adapted genetic operators, their handling turns to be a thorny issue. The point of this study is thus to test a few constraint handling techniques on a mid-size example in order to determine which one is the best fitted, in the framework of one particular problem formulation. The investigated methods are the elimination of infeasible individuals, the use of a penalty term added in the minimized criterion, the relaxation of the discrete variables upper bounds, dominancebased tournaments and, finally, a multiobjective strategy. The numerical computations, analysed in terms of result quality and of computational time, show the superiority of elimination technique for the former criterion only when the latter one does not become a bottleneck. Besides, when the problem complexity makes the random location of feasible space too difficult, a single tournament technique proves to be the most efficient one

An artificial fish swarm filter-based Method for constrained global optimization

Ana Maria A.C. Rocha, M. Fernanda P. Costa and Edite M.G.P. Fernandes, An Artificial Fish Swarm Filter-Based Method for Constrained Global Optimization, B. Murgante, O. Gervasi, S. Mirsa, N. Nedjah, A.M. Rocha, D. Taniar, B. Apduhan (Eds.), Lecture Notes in Computer Science, Part III, LNCS 7335, pp. 57–71, Springer, Heidelberg, 2012.An artificial fish swarm algorithm based on a filter methodology for trial solutions acceptance is analyzed for general constrained global optimization problems. The new method uses the filter set concept to accept, at each iteration, a population of trial solutions whenever they improve constraint violation or objective function, relative to the current solutions. The preliminary numerical experiments with a wellknown benchmark set of engineering design problems show the effectiveness of the proposed method.Fundação para a Ciência e a Tecnologia (FCT

A reliability-based network design problem

This paper presents a reliability-based network design problem. A network reliability concept is embedded into the continuous network design problem in which travelers' route choice behavior follows the stochastic user equilibrium assumption. A new capacity-reliability index is introduced to measure the probability that all of the network links are operated below their capacities when serving different traffic patterns deviating from the average condition. The reliability-based network design problem is formulated as a bi-level program in which the lower level sub-program is the probit-based stochastic user equilibrium problem and the upper level sub-program is the maximization of the new capacity reliability index. The lower level sub-program is solved by a variant of the method of successive averages using the exponential average to represent the learning process of network users on a daily basis that results in the daily variation of traffic-flow pattern, and Monte Carlo stochastic loading. The upper level sub-program is tackled by means of genetic algorithms. A numerical example is used to demonstrate the concept of the proposed framework.link_to_subscribed_fulltextInternational Conference on the Application of Information and Communication Technology in Transport Systems in Developing Countries, Sri Lanka, 5-7 August 2004. In Journal Of Advanced Transportation, 2005, v. 39 n. 3, p. 247-27

A bi-objective traffic counting location problem for origin-destination trip table estimation

In this study, we consider the bi-objective traffic counting location problem for the purpose of origin-destination (O-D) trip table estimation. The problem is to determine the number and locations of counting stations that would best cover the network. The maximal coverage and minimal resource utilization criteria, which are generally conflicting, are simultaneously considered in a multi-objective manner to reveal the tradeoff between the quality and cost of coverage. A distance-based genetic algorithm (GA) is used to solve the proposed bi-objective traffic counting location problem by explicitly generating the non-dominated solutions. Numerical results are provided to demonstrate the feasibility of the proposed model. The primary results indicate that the distance-based GA can produce the set of non-dominated solutions from which the decision makers can examine the tradeoff between the quality and cost of coverage and make a proper selection without the need to repeatedly solve the maximal covering problem with different levels of resource

New reserve capacity model of signal-controlled road network

The aim of this study is to examine further the new capacity-reliability index recently proposed for the design of a new reserve capacity model for a signal-controlled road network. This new reliability index explicitly accounts for the probability that all network links would be operated below their capacities when serving different traffic patterns deviating from the average condition. Specifically, the reserve capacity model is reformulated by using the new reliability index in a bilevel optimization framework. In addition, the reliability-based design is compared with that of the reserve capacity model to gain insights between the deterministic and stochastic network design problems.link_to_subscribed_fulltex

Norm approximation method for handling traffic count inconsistencies in path flow estimator

Path flow estimator (PFE) is a one-stage network observer proposed to estimate path flows and hence origin-destination (O-D) flows from traffic counts in a transportation network. Although PFE does not require traffic counts to be collected on all network links when inferring unmeasured traffic conditions, it does require all available counts to be reasonably consistent. This requirement is difficult to fulfill in practice due to errors inherited in data collection and processing. The original PFE model handles this issue by relaxing the requirement of perfect replication of traffic counts through the specification of error bounds. This method enhances the flexibility of PFE by allowing the incorporation of local knowledge, regarding the traffic conditions and the nature of traffic data, into the estimation process. However, specifying appropriate error bounds for all observed links in real networks turns out to be a difficult and time-consuming task. In addition, improper specification of the error bounds could lead to a biased estimation of total travel demand in the network. This paper therefore proposes the norm approximation method capable of internally handling inconsistent traffic counts in PFE. Specifically, three norm approximation criteria are adopted to formulate three Lp-PFE models for estimating consistent path flows and O-D flows that simultaneously minimize the deviation between the estimated and observed link volumes. A partial linearization algorithm embedded with an iterative balancing scheme and a column generation procedure is developed to solve the three Lp-PFE models. In addition, the proposed Lp-PFE models are illustrated with numerical examples and the characteristics of solutions obtained by these models are discussed.Origin-destination estimation Path flow estimator Stochastic user equilibrium Norm approximation Partial linearization method

Quality measures of origin-destination trip table estimated from traffic counts: review and new generalized demand scale measure

The goals of this paper are to (1) provide a review of the recently developed measures for assessing the quality of origin-destination (O-D) trip tables estimated from traffic counts and (2) propose a new generalized demand scale (GDS) measure. This GDS quality measure generalizes the total demand scale (TDS) quality measure by allowing the assessment of the intrinsic underdeterminant problem of O-D estimation from traffic counts at various spatial levels. Numerical examples are provided to compare the proposed GDS measure with the TDS measure, the maximal possible relative error, and the expected relative error and to illustrate the features of the GDS measure