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+34 946 567 842
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+34 946 567 842
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ccoello@bcamath.org
Information of interest
- Orcid: 0000-0002-8435-680X
Group Leader at BCAM.
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Multi-agent deep Q-network-based metaheuristic algorithm for Nurse Rostering Problem
Zhang, X.; Yang, Y.; Zhu, Q.; Lin, Q.; Chen, W.; Li, J.; Coello, C.A.
(2024-06-01)
The Nurse Rostering Problem (NRP) aims to create an efficient and fair work schedule that balances both the needs of employees and the requirements of hospital operations. Traditional local search-based metaheuristic ...
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A localized decomposition evolutionary algorithm for imbalanced multi-objective optimization
Ye, Y.; Lin, Q.; Wong, K. C.; Li, J.; Ming, Z.; Coello, C.A. (2024-03-01)
Multi-objective evolutionary algorithms based on decomposition (MOEA/Ds) convert a multi-objective optimization problem (MOP) into a set of scalar subproblems, which are then optimized in a collaborative manner. However, ...
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Revisiting Implicit and Explicit Averaging for Noisy Optimization
Ahrari, A.; Elsayed, S.; Sarker, R.; Essam, D.; Coello, C.A. (2023-10-01)
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 ...
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Challenging test problems for multi- and many-objective optimization
Zapotecas-Martínez, S.; Coello, C.A.; Aguirre, H.E.; Tanaka, K. (2023-08-01)
In spite of the extensive studies that have been conducted regarding the construction of multi-objective test problems, researchers have mainly focused their interests on designing complicated search spaces, disregarding, ...
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Discretization-Based Feature Selection as a Bilevel Optimization Problem
Rihab, S.; Maha, E.; Bechikh, S.; Coello, C.A.; Said, L. B. (2023-08-01)
Discretization-based feature selection (DBFS) approaches have shown interesting results when using several metaheuristic algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization ...
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On the utilization of pair-potential energy functions in multi-objective optimization
Falcón-Cardona, J.G.; Covantes Osuna, E.; Coello, C.A.; Ishibuchi, H. (2023-06-01)
In evolutionary multi-objective optimization (EMO), the pair-potential energy functions (PPFs) have been used to construct diversity-preserving mechanisms to improve Pareto front approximations. Despite PPFs have shown ...
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An ACO-based Hyper-heuristic for Sequencing Many-objective Evolutionary Algorithms that Consider Different Ways to Incorporate the DM's Preferences
Rivera, G.; Cruz-Reyes, L.; Fernández, E.; Gomez-Santillan, C.; Rangel-Valdez, N.; Coello, C.A. (2023-02-01)
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) ...
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Uncertainty-wise software anti-patterns detection: A possibilistic evolutionary machine learning approach
Boutaib, S.; Elarbi, M.; Bechikh, S.; Coello, C.A.; Said, L. B. (2022-11-01)
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 ...
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A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization
Li, L.; Li, Y.; Lin, Q.; Ming, Z.; Coello, C.A. (2022-10-01)
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 ...
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On the Construction of Pareto-Compliant Combined Indicators
Falcón-Cardona, J.G.; Emmerich, M.; Coello, C.A. (2022-08-12)
The most relevant property that a quality indicator (QI) is expected to have is Pareto compliance, which means that every time an approximation set strictly dominates another in a Pareto sense, the indicator must reflect ...
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Multiple source transfer learning for dynamic multiobjective optimization
Ye, Y.; Lin, Q.; Ma, L.; Wong, K. C.; Gong, M.; Coello, C.A. (2022-08-01)
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 ...
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An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization
Lin, Q.; Wu, X.; Ma, L.; Li, J.; Gong, M.; Coello, C.A. (2022-08-01)
Surrogate-assisted evolutionary algorithms (SAEAs) have become very popular for tackling computationally expensive multiobjective optimization problems (EMOPs), as the surrogate models in SAEAs can approximate EMOPs well, ...
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A self-organizing weighted optimization based framework for large-scale multi-objective optimization
Li, Y.; Li, L.; Lin, Q.; Wong, K. C.; Ming, Z.; Coello, C.A. (2022-07-01)
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 ...
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Preference incorporation in MOEA/D using an outranking approach with imprecise model parameters
Fernández, E.; Rangel-Valdez, N.; Cruz-Reyes, L.; Gomez-Santillan, C.; Coello, C.A. (2022-07-01)
Multi-objective Optimization Evolutionary Algorithms (MOEAs) face numerous challenges when they are used to solve Many-objective Optimization Problems (MaOPs). Decomposition-based strategies, such as MOEA/D, divide an MaOP ...
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A dynamic multi-objective evolutionary algorithm based on polynomial regression and adaptive clustering
Yu, Q.; Lin, Q.; Zhu, Z.; Wong, K. C.; Coello, C.A. (2022-06-01)
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 ...
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Static and Dynamic Multimodal Optimization by Improved Covariance Matrix Self-Adaptation Evolution Strategy with Repelling Subpopulations
Ahrari, A.; Elsayed, S.; Sarker, R.; Essam, D.; Coello, C.A. (2022-06-01)
The covariance matrix self-adaptation evolution strategy with repelling subpopulations (RS-CMSA-ES) is one of the most successful multimodal optimization (MMO) methods currently available. However, some of its components ...
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VSD-MOEA: A Dominance-Based Multiobjective Evolutionary Algorithm with Explicit Variable Space Diversity Management
Castillo, J.; Segura, C.; Coello, C.A. (2022-06-01)
Most state-of-the-art Multiobjective Evolutionary Algorithms (moeas) promote the preservation of diversity of objective function space but neglect the diversity of decision variable space. The aim of this article is to ...
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AdaSwarm: Augmenting Gradient-Based Optimizers in Deep Learning with Swarm Intelligence
Mohapatra, R.; Saha, S.; Coello, C.A.; Bhattacharya, A.; Dhavala, S. S.; Saha, S. (2022-04-01)
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 ...
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Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking
Rivera, G.; Coello, C.A.; Cruz-Reyes, L.; Fernandez, E.R.; Gomez-Santillan, C.; Rangel-Valdez, N. (2022-03-01)
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 ...
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Preference incorporation into many-objective optimization: An Ant colony algorithm based on interval outranking
Rivera, G.; Coello, C.A.; Cruz-Reyes, L.; Fernandez, E.R.; Gomez-Santillan, C.; Rangel-Valdez, N. (2022-03-01)
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 ...
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PyDDRBG: A Python framework for benchmarking and evaluating static and dynamic multimodal optimization methods
Ahrari, A.; Elsayed, S.; Sarker, R.; Essam, D.; Coello, C.A. (2022-01-01)
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 ...
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Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey
Falcón-Cardona, J.G.; Hernández Gómez, R.; Coello, C.A.; Castillo Tapia, M.G. (2021-12-01)
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 ...
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COARSE-EMOA: An indicator-based evolutionary algorithm for solving equality constrained multi-objective optimization problems
Llano García, J.L.; Monroy, R.; Sosa Hernández, V.A.; Coello, C.A. (2021-12-01)
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 ...
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On the Effect of the Cooperation of Indicator-Based Multiobjective Evolutionary Algorithms
Falcon-Cardona, J.G; Ishibuchi, H.; Coello, C.A.; Emmerich, M. (2021-08-01)
For almost 20 years, quality indicators (QIs) have promoted the design of new selection mechanisms of multiobjective evolutionary algorithms (MOEAs). Each indicator-based MOEA (IB-MOEA) has specific search preferences ...
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A Novel Parametric benchmark generator for dynamic multimodal optimization
Ahrari, A.; Elsayed, S.; Sarker, R.; Essam, D.; Coello, C.A. (2021-08-01)
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 ...