Héctor Joaquín Fraire-Huacuja

• Professor (Full) at Instituto Tecnológico de Ciudad Madero

In this research, two new methods for solving the Internet shopping optimization problem with sensitive prices are proposed, incorporating adaptive adjustment of control parameters. This problem is classified as NP-hard and is relevant to current electronic commerce. The first proposed solution method corresponds to a Memetic Algorithm incorporatin...

In this paper, we approach the Internet Shopping Optimization Problem with Shipping Costs (IShOP), an NP-hard-relevant problem in the current e-commerce environment. To our knowledge, several solution metaheuristic algorithms have been reported in the literature. In this paper, we propose a novel Particle Swarm Optimization algorithm (PSO) to solve...

This paper conducts a comprehensive review of literature focusing on strategies applied in the realm of Machine Learning (ML) to address the Bin Packing Problem (BPP) and its various variants. The Bin Packing Problem, a renowned optimization challenge, involves efficiently allocating items of varying sizes into containers of fixed capacity to minim...

The main contribution of this paper is the implementation of a multi-objective evolutionary algorithm based on decomposition with adaptive adjustment of control parameters applied to the bi-objective problem of Internet shopping (MOEA/D-AACPBIShOP). For this variant of the IShOP, the minimization of the cost and shipping time of the shopping list i...

In the present landscape of cloud computing, the effective scheduling of tasks stands as a pivotal element in optimizing the operational efficiency of distributed systems. This paper conducts a thorough and comparative examination of recent trends and progress within this vital and ever-evolving domain. By meticulously reviewing crucial performance...

Cloud computing has become one of the most studied information technologies by researchers since in recent years it has emerged as a dominant paradigm for the delivery of scalable and on-demand computing resources over the Internet. Task scheduling is a crucial aspect of cloud computing, it plays a vital role in optimizing resource utilization, min...

The main contribution of this paper is to develop a new solution method applied to the bi-objective Internet shopping problem. The first objective of this problem considers minimizing the total cost of the shopping list, and the second objective is minimizing the shopping list delivery time. This solution consists of a Multi-objective Evolutionary...

The Bin Packing problem (BPP) is a classic optimization problem that is known for its applicability and complexity, which belongs to a special class of problems called NP-hard, in which, given a set of items of variable size, we search to accommodate them inside fixed size containers, seeking to optimize the number of containers to be used, that is...

Multi-objective optimization has evolved significantly to this day, but there are still challenges that remain open problems such as algorithmic design, scalability and handling of costly objective functions. To solve some of the aforementioned challenges, in the state of the art it is being proposed to apply coevolutionary algorithms and thus solv...

This paper presents a particle swarm optimization algorithm with improved opposition-based learning (IOBL-PSO) to solve continuous optimization problems. IOBL-PSO adds this mechanism at the end of each iteration to introduce randomness in the search process, improving the quality and the efficiency of the algorithm and to avoiding premature converg...

This paper compares three methods of adaptive selection of operators (AOS) incorporated into the Dynamic Multiobjective Evolutionary Algorithm Based on Decomposition (DMOEA/D). The first method is structured in several continuous sections and is designed to execute a test and application procedure to find the way that allows a better adaptive selec...

The Bin Packing Problem (BPP) is a classic optimization problem that is known for its applicability and complexity, which belongs to a particular class of problems called NP-hard, in which, given a set of items of variable size, we search to accommodate them inside fixed size containers, seeking to optimize the number of containers to be used, that...

When using natural language interfaces to databases (NLIDBs) for database queries that involve many tables, the resulting SQL query may include semantically implicit entities. These entities are related to the semantic meaning of a query, when upon referring to an entity (table), another entity (or entities) is (are) semantically implied with which...

Many real-world optimization problems involve changes related to the passage of time; this characteristic is known as dynamism. In this paper, we approach a dynamic multi-objective project portfolio selection problem with preferences. The objective of the general problem consists of determining the set of projects that optimize a vector of benefits...

In this paper, we address the exact solution of the vertex bisection problem (VBP). We propose two novel B&B algorithms to solve VBP, which include new upper and lower bound constructive heuristics, and an efficient strategy to explore the combinatorial search space. The computational results show that the proposed algorithms clearly outperforms th...

At present the analysis of the algorithms is a necessary process, especially algorithms that solve difficult problems in the daily life; Since the analysis of algorithms helps to give explanations of the performance and to understand the behavior of the algorithms when the input data change it means, the input instances present inherent characteris...

The development of ubiquity in computing demands more intelligence from connected devices to perform tasks better. Users usually lookout for devices that proactively aid in an environment, making decisions as themselves. Such cognitive models for hardware agents have increased in recent years. However, although numerous strategies emulate intellige...

A common issue in the Multi-Objective Portfolio Optimization Problem (MOPOP) is the presence of uncertainty that affects individual decisions, e.g., variations on resources or benefits of projects. Fuzzy numbers are successful in dealing with imprecise numerical quantities, and they found numerous applications in optimization. However, so far, they...

The decision-making process can be complex and underestimated, where mismanagement could lead to poor results and excessive spending. This situation appears in highly complex multi-criteria problems such as the project portfolio selection (PPS) problem. Therefore, a recommender system becomes crucial to guide the solution search process. To our kno...

Many real-world optimization problems involving several conflicting objective functions frequently appear in current scenarios and it is expected they will remain present in the future. However, approaches combining multi-objective optimization with the incorporation of the decision maker’s (DM’s) preferences through multi-criteria ordinal classifi...

Most real-world problems require the optimization of multiple objective functions simultaneously, which can conflict with each other. The environment of these problems usually involves imprecise information derived from inaccurate measurements or the variability in decision-makers’ (DMs’) judgments and beliefs, which can lead to unsatisfactory solu...

This chapter addresses the Internet Shopping Optimization Problem with Shipping Cost (IShOP) issue. In the related literature, there is only one metaheuristic solution reported. This solution is a cellular processing algorithm that simulates the parallel processing of two or more Search processes through the solution space and is currently consider...

Earning profits when investing in a stock exchange and avoiding losses has always been a priority for every investor. This is the main reason why the portfolio selection problem has been of great importance to obtain reasonable compensation between the rate of return and the risk. However, the portfolio problem has been extended by introducing real...

In this paper, we approach the Multi-Objective Portfolio Optimization Problem with trapezoidal fuzzy parameters. As to the best of our knowledge, there are not reports of this version of the problem. In this work, a formulation of the problem and a solution algorithm is presented for the first time. Traditionally, this kind of algorithm uses the cr...

In this chapter is presented a comparative study of the performance of ten state-of-the-art multiobjective algorithms. The performance metrics used are hypervolume (HV), epsilon indicator (\( {\text{I}}_{{{{\varepsilon }} + }} \)), inverted generational distance (IGD), and generalized spread (\( {{\Delta }}^{ *} \)). The used instances belong to th...

We propose a new accurate Micro Genetic Algorithm ( GA) for multi-objective optimization problems that we call Micro-FAME (or FAME). The distinctive feature of FAME with respect to the other existing multi-objective algorithms in the literature is its high elitism and fast convergence, produced by the application of the evolution directly on the...

In this paper, the portfolio optimization problem is approached. It is a NP-hard problem that consists in periodically creating an instance of the problem, using the time series of the shares value of a stock exchange. The instance is solved to determine the shares set that maximize the return, minimize the risk and minimize the number of selected...

In decision-making, the multiobjective portfolio selection problem (MPSP) consists of the selection of alternatives based on preferences of a particular decision-maker (DM). In real-world applications, MPSP has several conflicting criteria that DMs must consider to determine an appropriate solution. So far, only fuzzy outranking relations have been...

In this chapter, an analytical parameter tuning for the Archive Multi-Objective Simulated Annealing (AMOSA) with a fuzzy logic controller is proposed. The analytical tuning is used to compute the initial and final temperature, as well as the maximum metropolis length. The fuzzy logic controller is used to adjust the metropolis length for each tempe...

In this chapter, the optimization of Dynamic Multi-Objective Problems (DMOP) is approached. To solve this kind of problems several evolutionary algorithms with a static selection of operators are reported in the literature. In this work, a new evolutionary algorithm with that an online operator selector is proposed. The operator choice is guided by...

En este artículo se aborda el problema de la solución exacta del problema de la bisección de los vértices de un grafo. Este es un problema NP-duro que surge en el contexto de las redes de comunicación. Actualmente, el mejor algoritmo del estado del arte es el denominado BBVBMP, el cual utiliza la metodología de Ramificación y Acotamiento. En este a...

This work presents a novel parallel branch and bound algorithm to efficiently solve to optimality a set of instances of the multi-objective flexible job-shop scheduling problem for the first time, to the very best of our knowledge. It makes use of the well-known NSGA-II algorithm to initialize its upper bound. The algorithm is implemented for share...

Dynamic optimization multi-objective problems (DMOPs) are characterized by the environmental changes they experiment . For these problems, optimization algorithms have limited time to find accurate results before every change. A common scenario in optimization is the presence of a decision maker (DM), which establishes preferences on the problem be...

It is important to know the properties of an optimization problem and the difficulty an algorithm faces to solve it. Population evolvability obtains information related to both elements by analysing the probability of an algorithm to improve current solutions and the degree of those improvements. DPEM_HH is a dynamic multi-objective hyper-heuristic...

We propose a new method for multi-objective optimization, called Fuzzy Adaptive Multi-objective Evolutionary algorithm (FAME). It makes use of a smart operator controller that dynamically chooses the most promising variation operator to apply in the different stages of the search. This choice is guided by a fuzzy logic engine, according to the cont...

The vertex bisection problem splits a graph G=(V,E) into two sets, L⊆V,|L|=⌊|V|/2⌋ and R=V∖L, such that it minimizes the number of vertices in L connected to R. This problem is a combinatorial NP-hard problem with relevant applications in network communications and, currently, there is only one metaheuristic method that solves it. In this paper, we...

Pareto fronts found by a parallel Branch and bound. The Pareto fronts are from Fattahi instances set. The objectives are the makespan, max workload, and total workload. The optimal Pareto fronts found are from SFJS1 to SFJS10 and MSFJ1 to MFJS2.

FAME implementation based on the jMetal 5 project.

50 DAYS FREE ACCESS VIA THIS LINK: https://authors.elsevier.com/a/1XQ9S77nKSbX8 Orthogonal polynomial kernels have been recently introduced to enhance support vector machine classifiers by reducing their number of support vectors. Previous works have studied these kernels as isolated cases and discussed only particular aspects. In this paper, a no...

Predicting volatility in stock market price indices is a major economic problem. The idea of forecasting time series is that the patterns associated with past values in a data series can be used to project future values. The study of volatility can be applied to solving these economic problems, because volatility allows measuring the risk of asset...

Proper tuning of hyper-parameters is essential to the successful application of SVMclassifiers. Several methods have been used for this problem: grid search, random search, Estimation of Distribution Algorithms (EDAs), bio-inspired metaheuristics, among others. The objective of this paper is to determine the optimal method among those that recently...

Hyper-parameter tuning for support vector machines has been widely studied in the past decade. A variety of metaheuristics, such as Genetic Algorithms and Particle Swarm Optimization have been considered to accomplish this task. Notably, exhaustive strategies such as Grid Search or Random Search continue to be implemented for hyper-parameter tuning...

Computing the Pathwidth of a graph is the problem of finding a linear ordering of the vertices such that the width of its corresponding path decomposition is minimized. This problem has been proven to be NP-hard. Currently, some of the best exact methods for generic graphs can be found in the mathematical software project called SageMath. This proj...

In this paper, the analytical parameter tuning for the Archive Multi-objective Simulated Annealing (AMOSA) is described. The analytical tuning method yields the initial and final temperature, and the maximum metropolis length. The analytically tuned AMOSA is used to solve the Heterogeneous Computing Scheduling Problem with independent tasks and it...

In the recent years, Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP). GE can use a diversity of search strategies including Swarm Intelligence (SI). Bee Swarm Optimization (BSO) is part of SI and it tries to solve the main problems of the Particle Swarm Optimization (PSO): the premature convergence and the p...

The Cut width Minimization Problem is a NP-Hard problem that is found in the VLSI design, graph drawing, design of compilers and linguistics. Developing solutions that could solve it efficiently is important due to its impact in areas that are critical for society. It consists in finding the linear array of an undirected graph that minimizes the ma...

Path-metaheuristics have been used successfully in combinatorial optimization. However, in continuous optimization problems, the lack of neighborhood definitions makes them difficult to design and implement. This paper proposes a neighborhood operator based on first order linear approximation of the gradient. In order to adapt the linear approximat...

The Internet shopping optimization problem arises when a customer aims to purchase a list of goods from a set of web-stores with a minimum total cost. This problem is NP-hard in the strong sense. We are interested in solving the Internet shopping optimization problem with additional delivery costs associated to the web-stores where the goods are bo...

Muchos de los algoritmos de optimización multi-objetivo más populares son poco eficaces al tratar con problemas de tres o más objetivos. Esto se debe en general al uso de estimadores de densidad, como la distancia de crowding de NSGA-II, que fueron diseñados cuando el principal reto era optimizar problemas de dos objetivos. En este artículo present...

Internet shopping has been one of the most common online activities, carried out by millions of users every day. As the number of available offers grows, the difficulty in getting the best one among all the shops increases as well. In this paper we propose an integer linear programming (ILP) model and two heuristic solutions, the MinMin algorithm a...

Simulated Annealing is an analogy with the annealing of solids, which foundations come from a physical area known as statistical mechanics. This chapter presents a review of the literature on multi-objective simulated annealing (MOSA). There are several multi-objective approaches to solve optimization problems with simulated annealing such as hybri...

In this paper the Pareto optimization of the Heterogeneous Computing Scheduling Multi-Objective Problem (HCSMOP) is approached. The goal is to minimize two objectives which are in conflict: the overall completion time (makespan) and the energy consumed. In the revised literature, there are no reported exact algorithms which solve the HCSMOP. In thi...

Este trabajo se enfoca en el problema de asignación de tareas independientes en sistemas de cómputo heterogéneo. La principal contribución es un estudio comparativo de diversas cruzas y mutaciones para el problema de consumo de energía para tareas sin precedencias en clústeres heterogéneos. Dentro de este comparativo se propone una mutación que apr...

Scheduling is a problem in computer science with a wide range of applicability in industry. The Heterogeneous Computing Scheduling Problem (HCSP) belongs to the parallel computing area and is applicable to scheduling in clusters and high performance data centers. HCSP has been solved traditionally as a mono-objective problem that aims at minimizing...

Grammatical Evolution has been used to evolve heuristics for the Bin Packing Problem. It has been shown that the use of Grammatical Evolution can generate an heuristic for either one instances or a full instance set for this problem. In many papers the selection of instances for heuristics generation has been done randomly. The present work propose...

Despite the large number of Natural Language Interfaces to Databases (NLIDB) that have been implemented, they do not guarantee to provide a correct response in 100% of the queries. In this paper, we present a way of semantic modelling the elements that integrate the knowledge of a NLIDB with the aim of increasing the number of correctly-answered qu...

This chapter is focused on the problem of scheduling independent tasks on heterogeneous machines. The main contributions of our work are the following: a linear programming model to compute energy consumption for the execution of independent tasks on heterogeneous clusters, a constructive heuristic based on local search, and a new benchmark set. To...

The vertex separation problem (VSP) consists of finding a linear ordering of the vertices of an input graph that minimizes the maximum number of vertex separators at each cut-point induced by the ordering. VSP is an NP-hard problem whose efficient solution is relevant in fields such as very large scale integration design, computer language compiler...

The master bay planning problem (MBPP) arises in the context of maritime transportation. In particular, MBPP consists of determining an efficient plan to stowage the containers into the containership such that the total loading time is minimized. This problem is classified as NP-hard due to the large number of possible solutions generated by the co...

In this chapter we propose two trajectory-based heuristics, particularly Simulated Annealing and Tabu Search, to solve instances of the Internet Shopping Optimisation Problem (ISOP). Since these metaheuristics are relatively less costly in terms of computational resources such as CPU time and memory, compared to population-based metaheuristics, but...

Kernel selection is a main factor in the designing of support vector machines. Evolutionary techniques have been applied to select the fittest kernel for specific classification problems. However, technical issues emerge when attempting to apply this methodology to deal with large datasets. On the other hand, a new method for improving the training...

The Vertex Separation Problem (VSP) is an NP-hard combinatorial optimization problem in the context of graph theory. Particularly, VSP belongs to a family of linear ordering problems in which the goal is to find the best separator of vertices in a generic graph. In the literature reviewed, we only found two exact methods based on integer linear pro...

In this paper, the NP-hard problem of minimizing power consumption in wireless communications systems is approached. In the literature, several metaheuristic approaches have been proposed to solve it. Currently a homogeneous cellular processing algorithm and a GRASP algorithm hybridized with path-relinking are considered the state of the art algori...

In this work we tackle the problem of the stowage of containers on a ship, the so-called Master Bay Planning Problem (MBPP). This is an important hard combinatorial optimization problem in the context of maritime port logistics which has proven to be really difficult to solve. We propose two new lower bounds and two new upper bounds which are usefu...

In this work a polynomial-time reduction to the NP-complete subset sum problem is followed in order to prove the complexity of Multiple Kernel Support Vector Machine decision problem. The Lagrangian function of the standard Support Vector Machine in its dual form was considered to derive the proof. Results of this derivation allow researchers to pr...

In this study, the one-dimensional Bin Packing Problem (BPP) is approached. The BPP is a classical optimization problem that is known for its applicability and complexity. We propose a method that is referred to as the Grouping Genetic Algorithm with Controlled Gene Transmission (GGA-CGT) for Bin Packing. The proposed algorithm promotes the transmi...

In recent years Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP) which has been applied to many optimization problems such as symbolic regression, classification, Boolean functions, constructed problems, and algorithmic problems.GE can use a diversity of searching strategies including SwarmIntelligence (SI)....

In this chapter we propose a new integer linear programming model based on precedences for the cutwidth minimization problem (CWP). A review of the literature indicates that this model is the only one reported for this problem. The results of the experiments with standard instances shows that the solution of the problem with the proposed model outp...

The multi-objective optimization methods are traditionally based on Pareto dominance or relaxed forms of dominance in order to achieve a representation of the Pareto front. However, the performance of traditional optimization methods decreases for those problems with more than three objectives to optimize. The decomposition of a multi-objective pro...

Recent works in experimental analysis of algorithms have identified the need to explain the observed performance. To understand the behavior of an algorithm it is necessary to characterize and study the factors that affect it. This work provides a summary of the main works related to the characterization of heuristic algorithms, by comparing the wo...

n this chapter the vertex separation problem (VSP) is approached. VSP is NP-hard with important applications in VLSI, computer language compiler design, and graph drawing, among others. In the literature there are several exact approaches to solve structured graphs and one work that proposes an integer linear programming (ILP) model for general gra...

In this chapter we approach the vertex bisection problem (VB), which is relevant in the context of communication networks. A literature review shows that the reported exact methods are restricted to solve particular graph cases. As a first step to solve the problem for general graphs using soft computing techniques, we propose two new integer-linea...

This chapter deals with the containership stowage problem. It is an NPhard combinatorial optimization whose goal is to find optimal plans for stowing containers into a containership with low operational costs, subject to a set of structural and operational constraints. In order to optimize a stowage planning, like in the literature, we have develop...

The aim of this paper is to show the solution of the Vehicle Routing Problem with Time Windows (VRPTW) as a key factor to solve a logistics system for the distribution of bottled products. We made a hybridization between an Ant Colony System algorithm (ACS) and a set of heuristics focused on instance characterization and performance learning. We ma...

This paper approaches the containership stowage problem. It is an NP-hard minimization problem whose goal is to find optimal plans for stowing containers into a containership with low operational costs, subject to a set of structural and operational constraints. In this work, we apply to this problem an ant-based hyperheuristic algorithm for the fi...

The paper deals with the problem of scheduling precedence-constrained applications on a distributed heterogeneous computing system with the aim of minimizing the response time or total execution time. We design and investigate the effectiveness of a micro genetic algorithm based scheduling algorithm. Due to a lack of generally accepted standard ben...

The paper deals with the problem of scheduling precedence-constrained applications on a distributed heterogeneous computing system with the aim of minimizing the response time or total execution time. The main contribution is a scheduling algorithm that promotes an iterative local search process. Due to a lack of generally accepted standard benchma...

The Bin Packing Problem is a classic optimization problem, over the years many heuristics have been developed to obtain better results. There are many approaches to generating heuristics automatically, those approaches are based Genetic Programming, but the heuristics generated sometimes can not be applied to the problem. Recently in the Artificial...

We propose the usage of formal languages for expressing instances of NP-complete problems for their application in polynomial transformations. The proposed approach, which consists of using formal language theory for polynomial transformations, is more robust, more practical, and faster to apply to real problems than the theory of polynomial transf...

This paper promotes the application of empirical techniques of analysis within computer science in order to construct models that explain the performance of heuristic algorithms for NP-hard problems. We show the application of an experimental approach that combines exploratory data analysis and causal inference with the goal of explaining the algor...