Carlos A. Coello Coello

Carlos A. Coello Coello
Center for Research and Advanced Studies of the National Polytechnic Institute | Cinvestav · Departamento de Computación

About

776
Publications
157,610
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54,185
Citations
Citations since 2017
189 Research Items
23437 Citations
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201720182019202020212022202301,0002,0003,0004,000
201720182019202020212022202301,0002,0003,0004,000

Publications

Publications (776)
Article
Full-text available
Multicriteria sorting involves assigning the objects of decisions (actions) into a priori known ordered classes considering the preferences of a decision maker (DM). Two new multicriteria sorting methods were recently proposed by the authors. These methods are based on a novel approach called interval-based outranking which provides the methods w...
Article
Full-text available
Recently, a number of resource allocation strategies have been proposed for evolutionary algorithms to efficiently tackle multiobjective optimization problems (MOPs). However, these methods mainly allocate computational resources based on the convergence improvement under the decomposition-based framework, which may become ineffective with the incr...
Article
Molecular docking plays a vital role in modern drug discovery, by supporting predictions of the binding modes and affinities of ligands at the binding site of target proteins. Several docking programs have been developed for both commercial and academic applications. Typically, a docking program’s performance depends on the sampling algorithm used...
Article
Full-text available
NSGA-II is, by far, the most popular metaheuristic that has been adopted for solving multi-objective optimization problems. However, its most common usage, particularly when dealing with continuous problems, is circumscribed to a standard algorithmic configuration similar to the one described in its seminal paper. In this work, our aim is to show t...
Article
Presents summaries of new books published on the topic of competitive intelligence.
Article
Many-objective optimization is an area of interest common to researchers, professionals, and practitioners because of its real-world implications. Preference incorporation into Multi-Objective Evolutionary Algorithms (MOEAs) is one of the current approaches to treat Many-Objective Optimization Problems (MaOPs). Some recent studies have focused on t...
Chapter
Multi-objective particle swarm optimizers (MOPSOs) have been widely used to deal with optimization problems having two or more conflicting objectives. As happens with other metaheuristics, finding the most adequate parameters settings for MOPSOs is not a trivial task, and it is even harder to choose structural components that determine the algorith...
Chapter
iMOACO\(\mathbb {_R}\) is an ant colony optimization algorithm designed to tackle multi-objective optimization problems in continuous search spaces. It is built on top of ACO\(\mathbb {_R}\) and uses the R2 indicator (to improve its performance on high-dimensional objective function spaces) to rank the pheromone archive of the best previously-explo...
Chapter
Full-text available
The hypervolume indicator (HV) has been subject of a lot of research in the last few years, mainly because its maximization yields near-optimal approximations of the Pareto optimal front of a multi-objective optimization problem. This feature has been exploited by several evolutionary optimizers, in spite of the considerable growth in computational...
Article
Recently, particle swarm optimizer (PSO) is extended to solve many-objective optimization problems (MaOPs) and becomes a hot research topic in the field of evolutionary computation. Particularly, the leader particle selection (LPS) and the search direction used in a velocity update strategy are two crucial factors in PSOs. However, the LPS strategi...
Article
Context Code smells (a.k.a. anti-patterns) are manifestations of poor design solutions that can deteriorate software maintainability and evolution. Research gap Existing works did not take into account the issue of uncertain class labels, which is an important inherent characteristic of the smells detection problem. More precisely, two human exper...
Article
italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A Review of Single-Source Deep Unsupervised Visual Domain Adaptation , by S. Zhao, X. Yue, S. Zhang, B. Li, H. Zhao, B. Wu, R. Krishna, J. E. Gonzalez, A. L. Sangiovanni-Vincentelli, S. A. Seshia, and K. Keutzer, IEEE Transactions on Neural Networks a...
Technical Report
Full-text available
This report presents the results of a dynamic multimodal optimization method formed by the integration of the adaptive multilevel prediction (AMLP) method with the recent variant of co-variance matrix self-adaptation evolution strategy with repelling subpopulation (RS-CMSAESII) on the test problems of the CEC'2022 competition on seeking multiple op...
Technical Report
Full-text available
This study integrates adaptive multilevel prediction (AMLP) with a static multimodal solver based on evolution strategies with repelling subpopulations (RS-ES) to develop a method for dynamic multimodal optimization. In the resultant method, denoted by AMLP-RS-ES, static multimodal optimization is performed using RS-ES while AMLP predicts the locat...
Code
This is the MATLAB code of adaptive multilevel prediction method integrated with an improved covariance matrix self-adaptation evolution strategy with repelling subpopulations (AMLP-RS-CMSA-ESII). Please check the readme.pdf file for the instructions and license before use.
Article
Recently, dynamic multiobjective evolutionary algorithms (DMOEAs) with transfer learning have become popular for solving dynamic multiobjective optimization problems (DMOPs), as the used transfer learning methods in DMOEAs can effectively generate a good initial population for the new environment. However, most of them only transfer non-dominated s...
Article
“For pure-feedback nonlinear systems under asymmetric output constraint, we present a low-cost neuroadaptive tracking control solution with salient features benefited from two design steps. In the first step, a novel output-dependent universal barrier function (ODUBF) is constructed such that not only the restrictive condition on constraining bound...
Article
The solving of large-scale multi-objective optimization problem (LSMOP) has become a hot research topic in evolutionary computation. To better solve this problem, this paper proposes a self-organizing weighted optimization based framework, denoted S-WOF, for addressing LSMOPs. Compared to the original framework, there are two main improvements in o...
Article
In this paper, a dynamic multi-objective evolutionary algorithm is proposed based on polynomial regression and adaptive clustering, called DMOEA-PRAC. As the Pareto-optimal solutions and fronts of dynamic multi-objective optimization problems (DMOPs) may dynamically change in the optimization process, two corresponding change response strategies ar...
Article
Our world is moving fast towards the era of the Internet of Things (IoT), which connects all kinds of devices to digital services and brings significant convenience to our lives. With the rapid increase in the number of devices connected to the IoT, there may exist more network vulnerabilities, resulting in more network attacks. Under this dynamic...
Chapter
Ant colony optimization (ACO) is one of the most representative metaheuristics derived from the broad concept known as swarm intelligence (SI) where the behavior of social insects is the main source of inspiration. Being a particular SI approach, the ACO metaheuristic is mainly characterized by its distributiveness, flexibility, capacity of interac...
Preprint
Full-text available
Evolutionary algorithms have been successfully applied to attacking Physically Unclonable Functions (PUFs). CMA-ES is recognized as the most powerful option for a type of attack called the reliability attack. While there is no reason to doubt the performance of CMA-ES, the lack of comparison with different metaheuristics and results for the challen...
Article
italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A Survey of Stochastic Computing Neural Networks for Machine Learning Applications , by Y. Liu, S. Liu, Y. Wang, F. Lombardi, and J. Han, IEEE Transactions on Neural Networks and Learning Systems , Vol. 32, No. 7, July 2021, pp. 2809–2824.
Chapter
This chapter describes the main features of project portfolio selection and formalizes a problem statement that considers these features. We provide a simple but comprehensive illustrative example that shows the usefulness of the problem statement and argue that there are no published approaches so far that deal with its whole complexity. We also p...
Article
PyDDRBG is a Python framework for generating tunable test problems for static and dynamic multimodal optimization. It allows for quick and simple generation of a set of predefined problems for non-experienced users, as well as highly customized problems for more experienced users. It easily integrates with an arbitrary optimization method. It can c...
Article
Discretization-based feature selection approaches have shown interesting results when using several metaheuristic algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), etc. However, these methods share the same shortcoming which consists in encoding the problem solution as a sequence of cut-poi...
Article
Explicit and implicit averaging are two well-known strategies for noisy optimization. Both strategies can counteract the disruptive effect of noise; however, a critical question remains: which one is more efficient? This question has been raised in many studies, with conflicting preferences and, in some cases, findings. Nevertheless, theoretical fi...
Article
Due to the many objectives and constraints involved in urban land use planning (ULUP), this is considered as a many‐objective and complex optimization problem that needs a variety of geographical analyses. In this article, the main target is improving NSGA‐III as an advanced many‐objective optimization algorithm for solving the ULUP problem. In thi...
Article
In this paper, we enriched Ant Colony Optimization (ACO) with interval outranking to develop a novel multi-objective ACO optimizer to approach problems with many objective functions. This proposal is suitable if the preferences of the Decision Maker (DM) can be modeled through outranking relations. The introduced algorithm (Interval Outranking-base...
Article
“With the rapid development from traditional machine learning (ML) to deep learning (DL) and reinforcement learning (RL), dialog system equipped with learning mechanism has become the most effective solution to address human–machine interaction problems. The purpose of this article is to provide a comprehensive survey on learning-based human–machin...
Article
Full-text available
The covariance matrix self-adaptation evolution strategy with repelling subpopulations (RS-CMSA-ES) is one of the most successful multimodal optimization methods currently available. However, some of its components may become inefficient in certain situations. This study introduces the second variant of this method, called RS-CMSA-ESII. It improves...
Article
Design of experiments is a branch of statistics that has been employed in different areas of knowledge. A particular case of experimental designs is uniform mixture design. A uniform mixture design method aims to spread points (mixtures) uniformly distributed in the experimental region. Each mixture should meet the constraint that the sum of its co...
Article
Many real-world applications involve dealing with several conflicting objectives which need to be optimized simultaneously. Moreover, these problems may require the consideration of limitations that restrict their decision variable space. Evolutionary Algorithms (EAs) are capable of tackling Multi-objective Optimization Problems (MOPs). However, th...
Article
Existing solution approaches for handling disruptions in project scheduling use either proactive or reactive methods. However, both techniques suffer from some drawbacks that affect the performance of the optimization process in obtaining good quality schedules. Therefore, in this article, we develop an auto-configured multioperator evolutionary ap...
Article
Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expen-sive multiobjective optimization problems (EMOPs), as the sur-rogate models in SAEAs can approximate EMOPs well, thereby reducing the time cost of the optimization process. However, with the increased number of decision variables in EMOPs,...
Preprint
Full-text available
Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of computational resources. Parallel implementations of MOEAs (pMOEAs) provide considerable gains regarding performance and scalabil...
Article
Republishes 7 book reviews first appearing in various IEEE journals.
Article
Full-text available
Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of computational resources. Parallel implementations of MOEAs (pMOEAs) provide considerable gains regarding performance and scalabil...
Preprint
Full-text available
In this paper, we enriched Ant Colony Optimization (ACO) with interval outranking to develop a novel multiobjective ACO optimizer to approach problems with many objective functions. This proposal is suitable if the preferences of the Decision Maker (DM) can be modeled through outranking relations. The introduced algorithm (named Interval Outranking...
Article
This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks. In order to support our proposed AdaSwarm, a novel Exponentially weighted Momentum Particle Swarm Optimizer (EMPSO), is proposed. The ability of AdaSwarm to tackle optimization problems is...
Chapter
This paper presents a very short overview of diversity in the context of multi-objective evolutionary algorithms. Besides emphasizing the importance of diversity maintenance when dealing with multi-objective optimization problems, other concepts such as density estimators, mating restrictions, and secondary populations are also briefly discussed. I...
Preprint
Full-text available
Ensembles have been used in the evolutionary computation literature to evolve several populations in an independent manner, using different search approaches. Moreover, each population’s parents compete with their offspring and the other population’s offspring to improve diversity. It has been shown that ensemble algorithms improve the performance...
Article
Full-text available
In most existing studies on dynamic multimodal optimization (DMMO), numerical simulations have been performed using the Moving Peaks Benchmark (MPB), which is a two-decade-old test suite that cannot simulate some critical aspects of DMMO problems. This study proposes the Deterministic Distortion and Rotation Benchmark (DDRB), a method to generate d...
Preprint
Full-text available
This paper investigates the influence of genotype size on evolutionary algorithms' performance. We consider genotype compression (where genotype is smaller than phenotype) and expansion (genotype is larger than phenotype) and define different strategies to reconstruct the original variables of the phenotype from both the compressed and expanded gen...
Article
Presents summaries of recent books published in the field of computational intelligence.
Article
Many real systems are represented in form of multiplex networks composed of a set of nodes, multiple layers of links and coupling node relationships across all layers. These systems are very vulnerable to damages during both attacks and recoveries due to potential node cascading failures (NCFs). Although some progress has recently been made in stud...
Preprint
Full-text available
This paper tackles the age-old question of derivate-free optimization in neural networks. This paper introduces AdaSwarm, a novel derivative-free optimizer to have similar or better performance to Adam but without "gradients". To support the AdaSwarm, a novel Particle Swarm Optimization Exponentially weighted Momentum PSO (EM-PSO), a derivative-fre...
Article
In the last few years, the formulation of real-world optimization problems and their efficient solution via metaheuristic algorithms has been a catalyst for a myriad of research studies. In spite of decades of historical advancements on the design and use of metaheuristics, large difficulties still remain in regards to the understandability, algori...
Chapter
The original version of this chapter was revised. Two equations in section 2.1 have been corrected.
Chapter
Particle Swarm Optimization (PSO) is a bio-inspired metaheuristic that has been successfully adopted for single- and multi-objective optimization. Several studies show that the way in which particles are connected with each other (the swarm topology) influences PSO’s behavior. A few of these studies have focused on analyzing the influence of swarm...
Chapter
Recently, an increasing number of state-of-the-art Multi-objective Evolutionary Algorithms (MOEAs) have incorporated the so-called pair-potential functions (commonly used to discretize a manifold) to improve the diversity within their population. A remarkable example is the Riesz s-energy function that has been recently used to improve the diversit...
Article
Full-text available
For almost 20 years, quality indicators (QIs) have promoted the design of new selection mechanisms of multi-objective evolutionary algorithms (MOEAs). Each indicator-based MOEA (IB-MOEA) has specific search preferences related to its baseline QI, producing Pareto front approximations with different properties. In consequence, an IB-MOEA based on a...
Article
This paper proposes a novel bicriteria assisted adaptive operator selection (B-AOS) strategy for decomposition-based multiobjective evolutionary algorithms (MOEA/Ds). In this approach, two operator pools are employed to focus on exploitation and exploration, each of which includes two DE operators with distinct search patterns. Then, two criteria,...
Article
The following book is reviewed: "Survey on Multi-Output Learning" (Xu, D., et al; 2020). The aim of multi-output learning is to simultaneously predict multiple outputs given an input. It is an important learning problem for decision-making since making decisions in the real world often involves multiple complex factors and criteria. In recent times...
Article
Full-text available
This study develops an adaptive multilevel prediction (AMLP) method to detect and track multiple global optima over time. First, it formulates a multilevel prediction approach in which a higher-level prediction improves the accuracy of the lower-level prediction to reduce the prediction error, enabling it to capture more complex patterns in the cha...
Chapter
Evolutionary algorithms have become a popular choice for solving highly complex multi-objective optimization problems in recent years. Multi-objective evolutionary algorithms were originally proposed in the mid-1980s, but it was until the mid-1990s when they started to attract interest from researchers. Today, we have a wide variety of algorithms,...
Article
Full-text available
Pareto-based multi-objective evolutionary algorithms use non-dominated sorting as an intermediate step. These algorithms are easy to parallelize as various steps of these algorithms are independent of each other. Researchers have focused on the parallelization of non-dominated sorting in order to reduce the execution time of these algorithms. In th...
Article
In Resource Constrained Project Scheduling Problems (RCPSPs), it is usually assumed that the activity durations are known and integers. This assumption helps to conveniently develop a standard mathematical model, using discrete time steps. However, in reality, activity durations may not only be integer, and they may not be known with certainty at t...
Book
This book discusses a number of intelligent algorithms which are being developed and explored for the next-generation communication systems. These include algorithms enabled with artificial intelligence, machine learning, artificial neural networks, reinforcement learning, fuzzy logic, swarm intelligence and cognitive capabilities. The book provide...
Article
Spatial urban land-use planning is a complex process, through which we aim to allocate suitable land-uses while taking into consideration multiple and conflicting objectives and constraints under certain spatial contexts. Landowners should be modeled as players that are able to interact with each other so as to seek their best land-uses while consi...
Article
Full-text available
A reinitialization approach is an effective way of generalizing a static multi-objective optimization method to a dynamic one. It is usually comprised of a prediction operator for predicting the approximate location(s) of the optimal solution(s) and a variation operator for enhancing the diversity of the reinitialized solution(s) after a change. Wh...
Article
Network embedding (NE) aims to learn low-dimensional node representations of networks while preserving essential node structures and properties. Existing NE methods mainly preserve simple link structures in unsigned networks, neglecting conflicting relationships that widely exist in social media and Internet of things. In this paper, we propose a n...
Conference Paper
Full-text available
Recently, an increasing number of state-of-the-art Multi-objective Evolutionary Algorithms (MOEAs) have incorporated the so-called pair-potential functions (commonly used to discretize a manifold) to improve the diversity within their population. A remarkable example is the Riesz s-energy function that has been recently used to improve the diversit...
Article
Decomposition-based evolutionary algorithms using predefined reference points have shown good performance in many-objective optimization. Unfortunately, almost all experimental studies have focused on problems having regular Pareto fronts (PFs). Recently, it has been shown that the performance of such algorithms is deteriorated when facing irregula...
Preprint
Full-text available
Recently, it has been stressed that multi-objective evolutionary algorithms (MOEAs) need to have robust performance with respect to the Pareto front shape of the problem being solved. An alternative approach is for MOEAs to use selection mechanisms based on multiple quality indicators (QIs) such that their specific properties are exploited. In this...
Chapter
It has been shown that swarm topologies influence the behavior of Particle Swarm Optimization (PSO). A large number of connections stimulates exploitation, while a low number of connections stimulates exploration. Furthermore, a topology with four links per particle is known to improve PSO’s performance. In spite of this, there are few studies abou...
Chapter
Ensemble learning is one of the most employed methods in machine learning. Its main ground is the construction of stronger mechanisms based on the combination of elementary ones. In this paper, we employ AdaBoost, which is one of the most well-known ensemble methods, to generate an ensemble indicator-based density estimator for multi-objective opti...
Chapter
We propose a framework for Cooperative Co-Evolutionary Genetic Programming (CCGP) that considers co-evolution at three different abstraction levels: genotype, feature and output level. A thorough empirical evaluation is carried out on a real-world high dimensional ML problem (image denoising). Results indicate that GP’s performance is enhanced only...
Chapter
In recent years, there has been a growing interest in multiobjective evolutionary algorithms (MOEAs) with a selection mechanism different from Pareto dominance. This interest has been mainly motivated by the poor performance of Pareto-based selection mechanisms when dealing with problems having more than three objectives (the so-called many-objecti...
Article
Full-text available
Prediction methods are useful tools for dynamic multiobjective optimization (DMO), especially if the changes roughly follow some patterns. Multi-model prediction methods, in particular, may capture different types of change patterns; however, they should address two issues. First, they should define a similarity measure that can correctly find the...
Chapter
During the last decade, large-scale global optimization has been a very active research area not only because of its many challenges but also because of its high applicability. It is indeed crucial to develop more effective search strategies to explore large search spaces considering limited computational resources. In this paper, we propose a new...
Preprint
Full-text available
Performance of multi/many-objective evolutionary algorithms (MOEAs) based on decomposition is highly impacted by the Pareto front (PF) shapes of multi/many-objective optimization problems (MOPs), as their adopted weight vectors may not properly fit the PF shapes. To avoid this mismatch, some MOEAs treat solutions as weight vectors to guide the evol...
Article
Full-text available
This paper suggests a multimodal multi-objective evolutionary algorithm with dual clustering in decision and objective spaces. One clustering is run in decision space to gather nearby solutions, which will classify solutions into multiple local clusters. Non-dominated solutions within each local cluster are first selected to maintain local Pareto s...