A Diagnostic Assessment Of Evolutionary Multiobjective Optimization For Water Resources Systems

This study contributes a rigorous diagnostic assessment of state-of-the-art multiobjective evolutionary algorithms (MOEAs) and highlights key advances that the water resources field can exploit to better discover the critical tradeoffs constraining our systems. This study provides the most comprehensive diagnostic assessment of MOEAs for water resources to date, exploiting more than 100,000 MOEA runs and trillions of design evaluations. The diagnostic assessment measures the effectiveness, efficiency, reliability, and controllability of ten benchmark MOEAs for a representative suite of water resources applications addressing rainfall-runoff calibration, long-term groundwater monitoring (LTM), and risk-based water supply portfolio planning. The suite of problems encompasses a range of challenging problem properties including (1) many-objective formulations with 4 or more objectives, (2) multi-modality (or false optima), (3) nonlinearity, (4) discreteness, (5) severe constraints, (6) stochastic objectives, and (7) non-separability (also called epistasis). The applications are representative of the dominant problem classes that have shaped the history of MOEAs in water resources and that will be dominant foci in the future. Recommendations are provided for which modern MOEAs should serve as tools and benchmarks in the future water resources literature.

Multi-Objective Pipe Smoothing Genetic Algorithm For Water Distribution Network Design

This paper describes the formulation of a Multi-objective Pipe Smoothing Genetic Algorithm (MOPSGA) and its application to the least cost water distribution network design problem. Evolutionary Algorithms have been widely utilised for the optimisation of both theoretical and real-world non-linear optimisation problems, including water system design and maintenance problems. In this work we present a pipe smoothing based approach to the creation and mutation of chromosomes which utilises engineering expertise with the view to increasing the performance of the algorithm whilst promoting engineering feasibility within the population of solutions. MOPSGA is based upon the standard Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and incorporates a modified population initialiser and mutation operator which directly targets elements of a network with the aim to increase network smoothness (in terms of progression from one diameter to the next) using network element awareness and an elementary heuristic. The pipe smoothing heuristic used in this algorithm is based upon a fundamental principle employed by water system engineers when designing water distribution pipe networks where the diameter of any pipe is never greater than the sum of the diameters of the pipes directly upstream resulting in the transition from large to small diameters from source to the extremities of the network. MOPSGA is assessed on a number of water distribution network benchmarks from the literature including some real-world based, large scale systems. The performance of MOPSGA is directly compared to that of NSGA-II with regard to solution quality, engineering feasibility (network smoothness) and computational efficiency. MOPSGA is shown to promote both engineering and hydraulic feasibility whilst attaining good infrastructure costs compared to NSGA-II.

Multi-population genetic algorithm to solve the synchronized and integrated two-level lot sizing and scheduling problem

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Hybrid evolutionary algorithm for the Capacitated Centered Clustering Problem

The Capacitated Centered Clustering Problem (CCCP) consists of defining a set of p groups with minimum dissimilarity on a network with n points. Demand values are associated with each point and each group has a demand capacity. The problem is well known to be NP-hard and has many practical applications. In this paper, the hybrid method Clustering Search (CS) is implemented to solve the CCCP. This method identifies promising regions of the search space by generating solutions with a metaheuristic, such as Genetic Algorithm, and clustering them into clusters that are then explored further with local search heuristics. Computational results considering instances available in the literature are presented to demonstrate the efficacy of CS. (C) 2010 Elsevier Ltd. All rights reserved.

Multi-objective evolutionary particle swarm optimization in the assessment of the impact of distributed generation

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

O problema do caixeiro viajante alugador : um estudo algorítmico

The Car Rental Salesman Problem (CaRS) is a variant of the classical Traveling Salesman Problem which was not described in the literature where a tour of visits can be decomposed into contiguous paths that may be performed in different rental cars. The aim is to determine the Hamiltonian cycle that results in a final minimum cost, considering the cost of the route added to the cost of an expected penalty paid for each exchange of vehicles on the route. This penalty is due to the return of the car dropped to the base. This paper introduces the general problem and illustrates some examples, also featuring some of its associated variants. An overview of the complexity of this combinatorial problem is also outlined, to justify their classification in the NPhard class. A database of instances for the problem is presented, describing the methodology of its constitution. The presented problem is also the subject of a study based on experimental algorithmic implementation of six metaheuristic solutions, representing adaptations of the best of state-of-the-art heuristic programming. New neighborhoods, construction procedures, search operators, evolutionary agents, cooperation by multi-pheromone are created for this problem. Furtermore, computational experiments and comparative performance tests are conducted on a sample of 60 instances of the created database, aiming to offer a algorithm with an efficient solution for this problem. These results will illustrate the best performance reached by the transgenetic algorithm in all instances of the dataset

Algoritomos transgenéticos aplicados ao problema da árvore geradora biobjetivo

The Multiobjective Spanning Tree is a NP-hard Combinatorial Optimization problem whose application arises in several areas, especially networks design. In this work, we propose a solution to the biobjective version of the problem through a Transgenetic Algorithm named ATIS-NP. The Computational Transgenetic is a metaheuristic technique from Evolutionary Computation whose inspiration relies in the conception of cooperation (and not competition) as the factor of main influence to evolution. The algorithm outlined is the evolution of a work that has already yielded two other transgenetic algorithms. In this sense, the algorithms previously developed are also presented. This research also comprises an experimental analysis with the aim of obtaining information related to the performance of ATIS-NP when compared to other approaches. Thus, ATIS-NP is compared to the algorithms previously implemented and to other transgenetic already presented for the problem under consideration. The computational experiments also address the comparison to two recent approaches from literature that present good results, a GRASP and a genetic algorithms. The efficiency of the method described is evaluated with basis in metrics of solution quality and computational time spent. Considering the problem is within the context of Multiobjective Optimization, quality indicators are adopted to infer the criteria of solution quality. Statistical tests evaluate the significance of results obtained from computational experiments

O problema do caixeiro alugador com coleta de bonus: um estudo algoritmico

This paper introduces a new variant of the Traveling Car Renter Problem, named Prizecollecting Traveling Car Renter Problem. In this problem, a set of vertices, each associated with a bonus, and a set of vehicles are given. The objective is to determine a cycle that visits some vertices collecting, at least, a pre-defined bonus, and minimizing the cost of the tour that can be traveled with different vehicles. A mathematical formulation is presented and implemented in a solver to produce results for sixty-two instances. The proposed problem is also subject of an experimental study based on the algorithmic application of four metaheuristics representing the best adaptations of the state of the art of the heuristic programming.We also provide new local search operators which exploit the neighborhoods of the problem, construction procedures and adjustments, created specifically for the addressed problem. Comparative computational experiments and performance tests are performed on a sample of 80 instances, aiming to offer a competitive algorithm to the problem. We conclude that memetic algorithms, computational transgenetic and a hybrid evolutive algorithm are competitive in tests performed

Practical considerations of embryo manipulation: Preimplantation genetic typing

In the past years, research in embryo technologies is moving to the establishment of preimplantation genetic typing or also denominated preimplantation genetic diagnosis (PGD). The objectives of these tests are the prevention of genetic diseases transmission and the prediction of phenotypic characteristics, as well as sex determination, genetic disorders and productive and reproductive profiles, prior to the embryo transfer or freezing, during early stages of development. This paper points out the state-of-the-art of PGD, mainly in cattle and discuss the perspectives of multiloci genetic analysis of embryos. (C) 2001 by Elsevier B.V.

Population genetic relationships between Casearia sylvestris (Salicaceae) varieties occurring sympatrically and allopatrically in different ecosystems in south-east Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Cryptic sexual populations account for genetic diversity and ecological success in a widely distributed, asexual fungus-growing ant

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Genetic structure in a tropical lek-breeding bird, the blue manakin (Chiroxiphia caudata) in the Brazilian Atlantic Forest

Determining the genetic structure of tropical bird populations is important for assessing potential genetic effects of future habitat fragmentation and for testing hypotheses about evolutionary mechanisms promoting diversification. Here we used 10 microsatellite DNA loci to describe levels of genetic differentiation for five populations of the lek-mating blue manakin (Chiroxiphia caudata), sampled along a 414-km transect within the largest remaining continuous tract of the highly endangered Atlantic Forest habitat in southeast Brazil. We found small but significant levels of differentiation between most populations. F-ST values varied from 0.0 to 0.023 (overall F-ST = 0.012) that conformed to a strong isolation by distance relationship, suggesting that observed levels of differentiation are a result of migration-drift equilibrium. N(e)m values estimated using a coalescent-based method were small (<= 2 migrants per generation) and close to the minimum level required to maintain genetic similarity between populations. An implication of these results is that if future habitat fragmentation reduces dispersal between populations to even a small extent, then individual populations may undergo a loss of genetic diversity due to an increase in the relative importance of drift, since inbreeding effective population sizes are relatively small (N-e similar to 1000). Our findings also demonstrate that population structuring can occur in a tropical bird in continuous habitat in the absence of geographical barriers possibly due to behavioural features of the species.

Evolutionary history of Scinax treefrogs on land-bridge islands in south-eastern Brazil

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Delimiting genetic units in Neotropical toads under incomplete lineage sorting and hybridization

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

GENETIC INSTABILITY IN NERVE SHEATH CELL TUMORS

After in vitro culture, we analyzed cytogenetically four acoustic nerve neurinomas, one intraspinal neurinoma and one neurofibroma obtained from unrelated patients. Monosomy of chromosomes 22 and 16 was an abnormality common to all cases, followed in frequency by loss of chromosomes 18 (three cases) and chromosomes 8, 17 and 19 (two cases). Trisomy of chromosome 20 was also detected in two cases. Structural rearrangements were detected at low frequencies, with del(10)(p12) being present in two cases. In addition, we observed cell subpopulations showing a certain degree of genetic instability, reflected by the presence of polyploid cells with inconsistent abnormalities, endoreduplications and telomeric associations resulting in dicentric chromosomes. It is probable that these cytogenetic abnormalities represent some kind of evolutionary advantage for the in vitro progression of nerve sheath tumors.