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Objective: To evaluate the effect of hydrogen peroxide, potassium sorbate, sodium bicarbonate, and chitosan on mycelial growth and in vitro germination of Colletotrichum sp., to be used for future management of anthracnose disease in postharvest cv. Ataulfo mango fruit. Design/Methodology/Approach: The effectiveness of the treatments was evaluated using the poisoned culture method. The evaluated concentrations of hydrogen peroxide and potassium sorbate were 1.0, 0.8, 0.6, 0.4, 0.2, 0.16, 0.12, 0.08, and 0.04 %; sodium bicarbonate, 1.0, 0.8, 0.6, 0.4 and 0.2 %; and chitosan, 2.5, 2.0, 1.5, 1.0 and 0.5 %. A 6-day disk of Colletotrichum sp. mycelial growth was placed in each poisoned culture medium. The inhibition of mycelial growth and the germination of Colletotrichum sp. conidia were evaluated. The experimental design was completely randomized with five repetitions for mycelial growth and four for conidium germination. The results were analyzed using the Kruskal-Wallis test and the comparison of average ranges. The CE50 and CE95 of each product was estimated using Probit analysis with the results of mycelial growth inhibition. Results: The mycelial growth inhibition (100%) of the Colletotrichum sp. strain was reached starting at concentrations of 0.16, 0.2, 1.0, and 2.5% for hydrogen peroxide, potassium sorbate, sodium bicarbonate, and chitosan, respectively. The inhibition of conidium germination was only observed in treatments with hydrogen peroxide and potassium sorbate. The CE50 and CE95 for hydrogen peroxide was 0.1 and 0.12%; for potassium sorbate, 0.10 and 0.19%; for sodium bicarbonate, 0.16 and 0.88%; and for chitosan, 1.20 and 2.18%. Findings/Conclusions: The evaluated treatments represent an effective and viable ecological alternative for the control of Colletotrichum sp., causal agent of anthracnosis in mango fruit.

This paper presents a hybrid genetic algorithm with collective communication (HGACC) using distributed processing for the job shop scheduling problem. The genetic algorithm starts with a set of elite micro-populations created randomly, where the fitness of these individuals does not exceed a tuned upper bound in the makespan value. The computational processes distribute the micro-populations collectively. In the micro-populations, each individual’s search for good solutions is directed toward the solution space of the fittest individual, identified by an approximation of genetic traits. In each generation of the genetic algorithm, the best individual from each micro-population migrates to another micro-population to maintain diversity in populations. Changes in the genetic sequence are applied to each individual by the simulated annealing algorithm (iterative mutation). In this paper, the results obtained show that the genetic algorithm achieves excellent results, as compared to other genetic algorithms. It is also better than other non-genetic meta heuristics or competes with them.

This paper presents a methodological scheme to obtain the maximum benefit in occupational health by attending to psychosocial risk factors in a company. This scheme is based on selecting an optimal subset of psychosocial risk factors, considering the departments’ budget in a company as problem constraints. This methodology can be summarized in three steps: First, psychosocial risk factors in the company are identified and weighted, applying several instruments recommended by business regulations. Next, a mathematical model is built using the identified psychosocial risk factors information and the company budget for risk factors attention. This model represents the psychosocial risk optimization problem as a Multidimensional Knapsack Problem (MKP). Finally, since Multidimensional Knapsack Problem is NP-hard, one simulated annealing algorithm is applied to find a near-optimal subset of factors maximizing the psychosocial risk care level. This subset is according to the budgets assigned for each of the company’s departments. The proposed methodology is detailed using a case of study, and thirty instances of the Multidimensional Knapsack Problem are tested, and the results are interpreted under psychosocial risk problems to evaluate the simulated annealing algorithm’s performance (efficiency and efficacy) in solving these optimization problems. This evaluation shows that the proposed methodology can be used for the attention of psychosocial risk factors in real companies’ cases.

Paper waste in the mockups design with regular, irregular, and amorphous patterns is a critical problem in digital printing presses. Paper waste reduction directly impacts production costs, generating business and environmental benefits. This problem can be mapped to the two-dimensional irregular bin-packing problem. In this paper, an iterated local search algorithm using a novel neighborhood structure to detect overlaps between amorphous shapes is introduced. This algorithm is used to solve the paper waste problem, modeled as one 2D irregular bin-packing problem. The experimental results show that this approach works efficiently and effectively to detect and correct the overlaps between regular, irregular, and amorphous figures.

[EN] Traditionally, it has been assumed that the compression of the sign of wavelet coefficients is not worth the effort because they form a zero-mean process. However, several image encoders such as JPEG 2000 include sign-coding capabilities. In this paper, we analyze the convenience of including sign-coding techniques into wavelet-based image encoders and propose a methodology that allows the design of sign-prediction tools for whatever kind of wavelet-based encoder. The proposed methodology is based on the use of metaheuristic algorithms to find the best sign prediction with the most appropriate context distribution that maximizes the resulting sign-compression rate of a particular wavelet encoder. Following our proposal, we have designed and implemented a sign-coding module for the LTW wavelet encoder, to evaluate the benefits of the sign-coding tool provided by our proposed methodology. The experimental results show that sign compression can save up to 18.91% of bit-rate when enabling sign-coding capabilities. Also, we have observed two general behaviors when coding the sign of wavelet coefficients: (a) the best results are provided from moderate to high compression rates; and (b) the sign redundancy may be better exploited when working with high-textured images., This research was supported by the Spanish Ministry of Economy and Competitiveness under Grant RTI2018-098156-B-C54, co-financed by FEDER funds (MINECO/FEDER/UE).

El objetivo de esta investigación fue determinar los principales géneros de nematodos asociados a plantas adultas de litchi y su asociación con el síntoma de clorosis en Oaxaca. Para ello, de marzo a agosto de 2013, en cuatro parcelas comerciales de litchi se colectaron muestras de suelo en la zona de goteo de los árboles; se seleccionaron cinco plantas asintomáticas y cinco con clorosis de las variedades Brewster y Mauritius, en tres etapas fenológicas: floración, cosecha y desarrollo vegetativo. Aphelenchus, Ditylenchus, Helicotylenchus, Hemicriconemoides, Longidorus, Mesocriconema, Pratylenchus, Psilenchus, Rotylenchus, Trichodorus, Tylenchorhynchus, Tylenchus y Xiphinema fueron los principales géneros identificados. El mayor número de nematodos totales se obtuvo en etapa de cosecha en la variedad Mauritius (300.6, p=0.05), mientras que en Brewster fue en floración (576, p=0.05). No se observó una relación directa de la población total de nematodos y el síntoma de clorosis para las diferentes etapas fenológicas evaluadas.

The induction of decision trees is a widely-used approach to build classification models that guarantee high performance and expressiveness. Since a recursive-partitioning strategy guided for some splitting criterion is commonly used to induce these classifiers, overfitting, attribute selection bias, and instability to small training set changes are well-known problems in them. Other approaches, such as incremental induction, classifier ensembles, and the global search in the decision-tree-space, have been implemented to overcome these problems. In particular, metaheuristics such as simulated annealing, genetic algorithms, genetic programming, and ant colony optimization have been used to induce compact and accurate decision trees. This paper presents a state-of-the-art review of the use of single-solution-based metaheuristics and swarm and evolutionary computation algorithms to build decision trees as classification models. We outline the decision-tree-induction process components and detail the existing literature studies on metaheuristic-based approaches to building these classifiers. Several timelines showing the chronological order in which these approaches were introduced in the literature are included. A summary analysis of these studies is also conducted, focusing on their internal components and experimental studies. This work provides a useful reference point for future research in this field.

This paper presents two algorithms, Simulated Annealing and Iterated Local Search. Both metaheuristics use a neighborhood hybrid structure to evaluate their effectiveness and maximize the mechanical strength of microalloyed steel. Tests show that the best metaheuristic for this type of problem, which makes use of a neighborhood structure and a chemical composition, is Iterated Local Search because it gives a better mechanical strength than Simulated Annealing. Acicular Ferrite was developed in the laboratory using the best mechanical properties obtained by the heuristics in computational tests. Then the mechanical strength of the created steel was evaluated. The experimental results show that the yield strength obtained in the laboratory is comparable to that obtained in computational tests.

This paper presents a process that is based on sets of parts, where elements are fixed and removed to form different binary branch-and-bound (BB) trees, which in turn are used to build a parallel algorithm called &ldquo, multi-BB&rdquo, These sequential and parallel algorithms calculate the exact solution for the 0&ndash, 1 knapsack problem. The sequential algorithm solves the instances published by other researchers (and the proposals by Pisinger) to solve the not-so-complex (uncorrelated) class and some problems of the medium-complex (weakly correlated) class. The parallel algorithm solves the problems that cannot be solved with the sequential algorithm of the weakly correlated class in a cluster of multicore processors. The multi-branch-and-bound algorithms obtained parallel efficiencies of approximately 75%, but in some cases, it was possible to obtain a superlinear speedup.

This paper describes one grid-based genetic algorithm approach to solve the vehicle routing problem with time windows in one experimental cluster MiniGrid. Clusters used in this approach are located in two Mexican cities (Cuernavaca and Jiutepec, Morelos) securely communicating with each other since they are configured as one virtual private network, and its use as a single set of processors instead of isolated groups allows one to increase the computing power to solve complex tasks. The genetic algorithm splits the population of candidate solutions in several segments, which are simultaneously mutated in each process generated by the MiniGrid. These mutated segments are used to build a new population combining the results produced by each process. In this paper, the MiniGrid configuration scheme is described, and both the communication latency and the speedup behavior are discussed. Experimental results show one information exchange reduction through the MiniGrid clusters as well as an improved behavior of the evolutionary algorithm. A statistical analysis of these results suggests that our approach is better as a combinatorial optimization procedure as compared with other methods.

In this paper, an evolutionary algorithm, called EA-WDND, is developed to optimize water distribution network design for real instances. The evolutionary algorithm uses the Epanet Solver which, while not an optimizer, helps to evaluate the operational constraints of mass conservation, energy conservation, pressure in nodes (nodal heads) of the network, and velocities of water in network pipes. Epanet is used by the EA-WDND to evaluate whether the looped network is operating properly. Consequently, the EA-WDND obtains feasible configurations of network design. The best configuration, which has the lowest cost and best performance according to defined constraints, is obtained by the EA-WDND. This configuration can be practically implemented in real life. In this paper, a methodology for using Epanet Solver with a parallel evolutionary algorithm is presented.

A stochastic algorithm for obtaining feasible initial populations to the Vehicle Routing Problem with Time Windows is presented. The theoretical formulation for the Vehicle Routing Problem with Time Windows is explained. The proposed method is primarily divided into a clustering algorithm and a two-phase algorithm. The first step is the application of a modifiedk-means clustering algorithm which is proposed in this paper. The two-phase algorithm evaluates a partial solution to transform it into a feasible individual. The two-phase algorithm consists of a hybridization of four kinds of insertions which interact randomly to obtain feasible individuals. It has been proven that different kinds of insertions impact the diversity among individuals in initial populations, which is crucial for population-based algorithm behavior. A modification to the Hamming distance method is applied to the populations generated for the Vehicle Routing Problem with Time Windows to evaluate their diversity. Experimental tests were performed based on the Solomon benchmarking. Experimental results show that the proposed method facilitates generation of highly diverse populations, which vary according to the type and distribution of the instances.

Planning corridors for new facilities such as pipeline or transmission lines through geographical spaces is a topographical constraint optimization problem. The corridor planning problem requires finding an optimal route or a set of alternative paths between two locations. This article presents a simulated-annealing-based (SA) approach applying a variable neighborhood strategy in a continuous space to generate competitive and different alternative paths to solve the corridor planning problem. The variable neighborhood method randomly selects two points from a variable interval of the current solution generated by SA creating pseudo-random paths inside a corridor and finding spatially different alternatives. The proposed approach is evaluated with three practical problems using real topographic data from the Veracruz Basin in Mexico. The experimental results show that this approach obtains efficient and competitive solutions with improvements above 18% over those gotten by the compared method.

This research proposes a genetic algorithm that provides a solution to the problem of deficient distribution of drinking water via the current hydraulic network in the neighborhood “Fraccionamiento Real Montecasino” (FRM), in Huitzilac, Morelos, Mexico. The proposed solution is the addition of new elements to the FRM network. The new elements include storage tanks, pipes, and pressure-reducing valves. To evaluate the constraint satisfaction model of mass and energy conservation, the hydraulic EPANET solver (HES) is used with an optimization model to minimize the total cost of changes in the network (new pipes, tanks, and valves). A genetic algorithm was used to evaluate the optimization model. The analysis of the results obtained by the genetic algorithm for the FRM network shows that adequate and balanced pressures were obtained by means of small modifications to the existing network, which entailed minimal costs. Simulations were performed for an extended period, which means that the pressure was obtained by simulation with HSE at one-hour intervals, during the algorithm execution, to verify adequate pressure at a specific point in the system, or to make corrections to ensure proper distribution, this with the aim of having a final optimized network design.

This paper presents a simulated annealing algorithm accelerated by a partial scheduling mechanism and a cooling schedule mechanism that is a function of the standard deviation. This facilitates a rapid approach to good solutions in the flexible job shop scheduling problem (FJSSP). The results demonstrate that for benchmark instances of several sizes, simulated annealing that implements the proposed mechanism converges more quickly to good solutions than simulated annealing that does not implement the proposed mechanism.

This paper presents a neighbourhood generation mechanism for the job shop scheduling problems JSSPs. In order to obtain a feasible neighbour with the generation mechanism, it is only necessary to generate a permutation of an adjacent pair of operations in a scheduling of the JSSP. If there is no slack time between the adjacent pair of operations that is permuted, then it is proven, through theory and experimentation, that the new neighbour schedule generated is feasible. It is demonstrated that the neighbourhood generation mechanism is very efficient and effective in a simulated annealing.

This paper proposes a real mathematical constraint satisfaction model which defines the timetabling problem in the Faculty of Chemical Sciences and Engineering (FCSE) at the Autonomous University of Morelos State, Mexico. A Constructive Approach Algorithm (CAA) is used to obtain solutions in the proposed model. A comparison is made between the CAA’s results and the schedule generated by the FCSE administration. Using the constraint satisfaction model, it is possible to improve the allocation of class hours in the FCSE so that classroom use is more efficient.

A mathematical multi objective model for the selection of a portfolio of investment is presented and its application in the Mexican Stock Exchange (BMV). The multi objective model proposed is based on our mathematical model of linear programming recently published. Our multi object model is developed whereas the e-constrains method, with which the model remains linear and each iteration the SIMPLEX can be used to determine its solution. Our model is tested with the selection of a portfolio of investment with ten assets of the BMV, where our results are better than those obtained with maximum return and minimal risk models resolved independently.

In the developing of scaffolds for cell culture, a large number of architectures with different combinations of properties should be tested to determine the best. This can be costly in time, money and materials. In this paper we have proposed an optimization model that aims to maximize the growth of osteoblasts on polymeric scaffolds by regulating their properties and architecture. Based on the optimization model it was implemented a genetic algorithm to calculate the architecture and properties of the scaffolds. The fiber diameter, pore diameter, porosity, Young's modulus and contact angle of the scaffolds were calculated through four electrospinning parameters: voltage (kV), concentration (% w/v), flow rate (ml/h) and distance (cm). A fitness value was assigned to each scaffold and the highest one was chosen as the best condition for osteoblast growth. The preliminary results obtained by the Genetic Algorithm were consistent with the tendencies reported experimentally in other studies. Also, the methodology established here can be easily adapted to different types of polymers and cells. Finally, the optimization model can be applied not only by means of heuristic method, like a Genetic Algorithm, but also by exact methods.

The analysis of an existing water distribution system is presented. The constraint satisfaction model for the study of the network is evaluated using Epanet software. According to the analysis, possible solutions are proposed to improve the distribution of water in the community. One of the recommendations is to add new storage tanks and valves to increase the flow and regulate the pressure without changing the current design of the network. It is not possible to change pipes and diameters because the system is in operation.

This paper presents an experimental study of the solutions space generated by the mathematical model of the Water Distribution Network Design Problem by using Two-Looped network benchmarks to find the feasible solutions space. It shows how the performance of a typical Evolutionary Algorithm (EA) can be improved by considering the importance of working with a feasible population and carrying out repetitive mutations and crossovers to generate new feasible offspring with better fitness. The replacement of parents represents the mortality index of a population at each generation of EA. Aiming to compensate the mortality index, EA is forced to maintain a constant population size by increasing the number of descendants with the crossover operator. The experimental results show both the feasible solutions space and the results of the algorithm when using feasible solutions and varying population size.

A comparative analysis of several neighborhood structures is presented, including a variable neighborhood structure, which corresponds to a combination of the neighborhood structures evaluated in this paper. The performance of each neighborhood structure was tested using large random instances generated in this research and well-known benchmarks such as the Classical Symmetric Traveling Salesman Problem and the Unrelated Parallel Machines Problem. Experimental results show differences in the performance of the variable neighborhood search when it is applied to problems with differing complexity. Contrary to reports in literature about variable neighborhood searches, its performance varies according to the complexity of the problem.

Wavelet transforms have proved to be very powerful tools for image compression. Previous studies have verified that there is a strong correlation between the sign of a wavelet coefficient and the signs of their neighbors. This correlation opens the possibility of using a sign predictor in order to improve the image compression process. In this paper a new sign coding approximation method for the wavelet coefficients in a 2D image codec based on an iterated local search algorithm (ILS) is presented. The efficiency of the proposed algorithm versus simulated annealing algorithm (SA) and genetic algorithm (GA) using standard images and benchmarks of Kodak is compared. The proposed sign prediction algorithm is as efficient as other methods and it provides a significant reduction of wavelet coefficients sign information in the final bit-stream.

In this paper an unsupervised clustering method for the Capacited Vehicle Routing Problem is proposed. Advantages and disadvantages of the proposed algorithm are weighed, and comparisons are made to some clustering algorithms commonly used in the literature to tackle routing problems. Experimental tests were performed using Solomon and Hering/Homberger benchmarks applied to different distributions. The proposed algorithm is demonstrated as effective for the attempted problem, with the ability to improve upon weaknesses in some of the clustering algorithms in the literature.

In this paper a new sign coding approximation method for the wavelet coefficients in a 2D image codec based on a simulated annealing metaheuristic is presented. The efficiency of the proposed algorithm versus a genetic algorithm using benchmarks of Kodak is compared and showing that the proposed sign prediction algorithm is efficient and provides a significant reduction of wavelet coefficients sign information in the final bit-stream. The results show that, by including sign coding capabilities to a nonembedded encoder, the sign compression gain is up to 17.35%, being the rate-distortion (R/D) performance improvement up to 0.25 dB.

An experimental analysis of five neighborhoods is presented. The analysis includes a hybrid structure comprised of one random pair, two random pairs, three random pairs or four random pairs. The efficiency and effectiveness of each structure was tested using the minimum spanning tree problem. As proposed in this research paper, the hybrid structure approach applied to the minimum spanning tree problem demonstrates superior effectiveness and competitive efficiency as compared to other structures.

This article presents the Iterated Local Search Algorithm (ILS) heuristic with a neighborhood hybrid structure, composed of four different types of adjacent structures. Their effectiveness was evaluated to maximize the micro alloyed steels mechanical strength. Tests show that ILS using a hybrid structure has better mechanical strength for the micralloyed steel due to its chemical composition.

Around the world there have recently been new and more powerful computing platforms created that can be used to work with computer science problems. Some of these problems that are dealt with are real problems of the industry; most are classified by complexity theory as hard problems. One such problem is the vehicle routing problem with time windows (VRPTW). The computational Grid is a platform which has recently ventured into the treatment of hard problems to find the best solution for these. This chapter presents a genetic algorithm for the vehicle routing problem with time windows. The algorithm iteratively applies a mutation operator, first of the intelligent type and second of the restricting type. The algorithm takes advantage of Grid computing to increase the exploration and exploitation of the solution space of the problem. The Grid performance is analyzed for a genetic algorithm and a measurement of the latencies that affect the algorithm is studied. The convenience of applying this new computing platform to the execution of algorithms specially designed for Grid computing is presented.

In this paper a comparative analysis of a neighborhood structures group are presented, including a hybrid structure, which arises of a combination of this set of structures. The efficiency and effectiveness of each structure was tested using the Classical Symmetric Travelling Salesman Problem. This study identifies the neighborhood structure that allows performing a better exploration and exploitation of the space solutions to discrete optimization problems. A neighborhood hybrid structure proposed has better performance comparing with other techniques, this is experimentally proved, in addition a competitive efficiency is shown.

In this work is defined a scheme of two stages for sending population segments of one Parallel Genetic Algorithm (PGA) to the nodes of an experimental grid called “Tarantula miniGrid”. The technique used to link the clusters and to configure the Tarantula mini Grid is described and the effects of latency in the communications between these clusters and the behavior of the speedup are discussed. The PGA is used for solving instances of the Vehicle Routing Problem with Time Windows (VRPTW). Results of this approach show that this communication scheme increases the efficiency of PGA in a grid environment and produces greater exploration and exploitation of the solution space of VRPTW.