Catálogo de publicaciones - libros

In the steady-state model for genetic algorithms (SSGA), the choice of a replacement strategy plays an important role in performance. Being able to handle changes is important for an optimization algorithm since many real-world problems are dynamic in nature. The main aim of this study is to experimentally compare different variations for basic replacement strategies in a dynamic environment. To cope with changes, a very simple mechanism of duplicate elimination is used. As an example of a dynamic problem, a dynamic version of the multi-dimensional knapsack problem is chosen. The results obtained here are in keeping with previous studies while some further interesting results are also obtained due to the special landscape features of the chosen problem.

Researchers examining genetic algorithms (GAs) in applied settings rarely have access to anything other than fitness values of the best individuals to observe the behavior of the GA. In particular, researchers do not know what schemata are present in the population. Even when researchers look beyond best fitness values, they concentrate on either performance related measures like average fitness and robustness, or low-level descriptions like bit-level diversity measures. To understand the behavior of the GA on dynamic problems, it would be useful to track what is occurring on the “semantic” level of schemata. Thus in this paper we examine the evolving “content” in terms of schemata, as the GA solves dynamic problems. This allows us to better understand the behavior of the GA in dynamic environments. We finish by summarizing this knowledge and speculate about future work to address some of the new problems that we discovered during these experiments.

This paper presents an evolutionary computing approach for the estimation of dynamic Origin-Destination (O-D) trip matrices from automatic traffic counts in urban networks. A multi-objective, simultaneous optimization problem is formulated to obtain a mutually consistent solution between the resulting O-D matrix and the path/link flow loading pattern. A genetically augmented microscopic simulation procedure is used to determine the path flow pattern between each O-D pair by estimating the set of turning proportions at each intersection. The proposed approach circumvents the restrictions associated with employing a user-optimal Dynamic Traffic Assignment (DTA) procedure and provides a stochastic global search of the optimal O-D trip and turning flow distributions. The application of the model into a real arterial street sub-network demonstrates its ability to provide results of satisfactory accuracy within fast computing speeds and, hence, its potential usefulness to support the deployment of dynamic urban traffic management systems.

This paper examines the formulation and solution of the discrete version of the stochastic Network Design Problem (NDP) with incorporated network travel time reliability requirements. The NDP is considered as a two-stage Stackelberg game with complete information and is formulated as a combinatorial stochastic bi-level programming problem. The current approach introduces the element of risk in the metrics of the design process through representing the stochastic nature of various system components related to users’ attributes and network characteristics. The estimation procedure combines the use of mathematical simulation for the risk assessment with evolutionary optimization techniques (Genetic Algorithms), as they can suitably address complex non-convex problems, such as the present one. The implementation over a test network signifies the potential benefits of the proposed methodology, in terms of intrinsically incorporating stochasticity and reliability requirements to enhance the design process of urban road networks.

When non-recurrent road traffic congestion happens, the operator of the traffic control centre has to select the most appropriate traffic control measure or combination of measures in a short time to manage the traffic network. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control measures that need to be considered during the decision making process. The identification of suitable control measures for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic scenarios for a number of control measures in a complicated situation is very time-consuming. In this paper we propose an intelligent traffic control decision support system (ITC-DSS) to assist the human operator of the traffic control centre to manage online the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural network, and genetic algorithm. These approaches form a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a GA algorithm for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city. The results obtained for the case study are promising and show that the proposed approach can provide an effective support for online traffic control.

We consider the , denoted , in which a salesman using the railway network wishes to visit a certain number of cities to carry out his/her business, starting and ending at the same city, and having the goal to minimize the overall time of the journey. The is NP-hard and it is related to the . In this paper we present an effective meta-heuristic based on ant colony optimization () for solving the . Computational results are reported for real-world and synthetic data. The results obtained demonstrate the superiority of the proposed algorithm in comparison with the existing method.

CHAC, a Multi-Objective Ant Colony Optimization (MOACO), has been designed to solve the problem of finding the path that minimizes resources while maximizing safety for a military unit. The new version presented in this paper takes into acount new, more realistic, conditions and constraints. CHAC’s previously proposed transition rules have been tested in more realistic maps. In addition some improvements in the implementation have been made, so better solutions are yielded. These solutions are better than a baseline greedy algorithm, and still good from a military point of view.

A greedy randomized adaptive search procedure with path relinking is presented for the capacitated arc routing problem with time windows. Contrary to the vehicle routing problem with time windows, this problem received little attention. Numerical experiments indicate that the proposed metaheuristic is competitive with the best published algorithms: on a set of 24 instances, it reaches the optimum 17 times and improves the best-known solution 5 times, including 4 new optima.

Supply chain optimization usually involves multiple objectives. In this paper, supply chains are optimized with a multi-objective optimization approach based on genetic algorithm and simulation model. The supply chains are first modeled as batch deterministic and stochastic Petri nets, and a simulation-based optimization method is developed for inventory policies of the supply chains with a multi-objective optimization approach as its search engine. In this method, the performance of a supply chain is evaluated by simulating its Petri net model, and a Non dominated Sorting Genetic Algorithm (NSGA2) is used to guide the optimization search process towards global optima. An application to a real-life supply chain demonstrates that our approach can obtain inventory policies better than ones currently used in practice in terms of two objectives: inventory cost and service level.

Most of the work about flowshop scheduling problems assume that the problem data are known exactly at the advance or the common approach to the treatment of the uncertainties in the problem is use of probabilistic models. However, the evaluation and optimization of the probabilistic model is computationally expensive and rational only when the descriptions of the uncertain parameters are available from the historical data. In addition, a certain amount of delay on due dates may be tolerated in most real-world situations although they are handled as crisp dates in most of the previous papers. In this paper we deal with a flowshop scheduling problem with fuzzy processing times and flexible due dates. Schedules are generated by a proposed algorithm in the context of ant colony optimization metaheuristic approach.