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Light-EvoOPT: A Lightweight Evolutionary Optimization Framework for Ultra-Large-Scale Mixed Integer Linear Programs
IEEE Transactions on Evolutionary Computation
Year: 2025 | Early Access Article |
Machine Learning (ML)-based optimization frameworks emerge as a promising technique for solving large-scale Mixed Integer Linear Programs (MILPs), as they can capture the mapping between problem structures and optimal solutions to expedite their solution process. However, existing solution frameworks often suffer from high model computation costs, incomplete problem reduction, and reliance on larg...Show More
A Comprehensive System to Support Decision Making in Highly Complex Project Portfolio Situations
Efrain Solares;Eduardo Fernández;Carlos A. Coello Coello;Xochitl Segura Lozano;Reimundo Moreno-Cepeda;Raymundo Diaz
This paper describes a new approach in project portfolio selection (PPS) problems, emphasizing the need to overcome traditional deficiencies with respect to multicriteria decision-making and multiobjective optimization. While existing methods typically allow the solving of partial aspects of the PPS problem, the proposed approach seeks to provide a holistic framework dealing with aspects like inte...Show More
Nearest-Better Network for Fitness Landscape Analysis of Continuous Optimization Problems
Yiya Diao;Changhe Li;Sanyou Zeng;Shengxiang Yang;Carlos A. Coello Coello
IEEE Transactions on Evolutionary Computation
Year: 2024 | Early Access Article |
Fitness landscape analysis (FLA) is quite important in evolutionary computation. In this paper, we propose a novel FLA method, the nearest-better network (NBN), which uses the nearest-better relationship to simplify the original fitness landscape of continuous optimization problems. We introduce an efficient algorithm to calculate NBN for continuous problems. We also propose four numerical measure...Show More
Exploring Generative AIs as Population Variation Operator in Multi-objective Optimization Problems
Gerardo Ibarra-Vazquez;Hugo Terashima-Marin;Carlos A. Coello
2024 IEEE Congress on Evolutionary Computation (CEC)
Year: 2024 | Conference Paper |
In recent years, evolutionary computation has signif-icantly advanced in processes related to machine learning. How-ever, the reciprocal integration of machine learning techniques into evolutionary computation remains relatively unexplored. Machine learning can substantially enhance the understanding of processes within Multi-Objective Evolutionary Algorithms (MOEAs) by harnessing its proficiency ...Show More
Recombination Operators for the Multi-Objective Team Formation Problem in Social Networks
Julio Juárez;Carlos A. Brizuela;Hugo Terashima-Marín;Carlos A. Coello Coello
2024 IEEE Congress on Evolutionary Computation (CEC)
Year: 2024 | Conference Paper |
The Team Formation Problem in Social Networks (TFP-SN) describes the process of finding an effective group of people, drawn from a network of experts, to perform a particular task. For a team to be considered as effective, it requires to comply with a task-specific skills set while also showing a high degree of cohesiveness. Although team effectiveness is subject to multiple criteria, the study of...Show More
Superpixel Segmentation Based Evolutionary Multitasking Algorithm for Feature Selection of Hyperspectral Images
Lingjie Li;Yuze Zhang;Qiuzhen Lin;Zhong Ming;Carlos A. Coello Coello;Victor C. M. Leung
IEEE Transactions on Evolutionary Computation
Year: 2024 | Early Access Article |
Cited by: Papers (1)
Feature selection (FS) is a very important technique for hyperspectral image (HSI) classification, as successfully selecting informative features can significantly increase the learning performance while reducing the computational cost. However, most of the existing FS methods tend to treat the HSI as a whole for FS, which does not fully consider the unique characteristics of HSIs and disregards t...Show More
Neural Net-Enhanced Competitive Swarm Optimizer for Large-Scale Multiobjective Optimization
Lingjie Li;Yongfeng Li;Qiuzhen Lin;Songbai Liu;Junwei Zhou;Zhong Ming;Carlos A. Coello Coello
IEEE Transactions on Cybernetics
Cited by: Papers (11)
The competitive swarm optimizer (CSO) classifies swarm particles into loser and winner particles and then uses the winner particles to efficiently guide the search of the loser particles. This approach has very promising performance in solving large-scale multiobjective optimization problems (LMOPs). However, most studies of CSOs ignore the evolution of the winner particles, although their quality...Show More
Multiobjective Multitasking Optimization With Decomposition-Based Transfer Selection
Qiuzhen Lin;Zhongjian Wu;Lijia Ma;Maoguo Gong;Jianqiang Li;Carlos A. Coello Coello
IEEE Transactions on Cybernetics
Cited by: Papers (3)
Multiobjective multitasking optimization (MTO) needs to solve a set of multiobjective optimization problems simultaneously, and tries to speed up their solution by transferring useful search experiences across tasks. However, the quality of transfer solutions will significantly impact the transfer effect, which may even deteriorate the optimization performance with an improper selection of transfe...Show More
Routing and Scheduling in Multigraphs With Time Constraints—A Memetic Approach for Airport Ground Movement
Lilla Beke;Lourdes Uribe;Adriana Lara;Carlos Artemio Coello Coello;Michal Weiszer;Edmund K. Burke;Jun Chen
Routing and scheduling problems with increasingly realistic modeling approaches often entail the consideration of multiple objectives, time constraints, and modeling the system as a multigraph. This detailed modeling approach has increased computational complexity and may also lead to violation of the additivity property of the costs. In the worst scenario, increased complexity makes the problem i...Show More
An Improved Version of MMOEA/DC Based on Alternative Clustering Definitions
2023 IEEE Congress on Evolutionary Computation (CEC)
Year: 2023 | Conference Paper |
Multimodal multiobjective optimization problems (MMOPs) have recently received considerable attention since they emerge in many real-world applications (e.g., in multiobjective knapsack problems and flow shop scheduling). However, MMOPs constitute a very particular class of problem. Indeed, looking for an adequate Pareto front (PF) representation is insufficient. MMOPs contain multiple subsets wit...Show More
Solving the Discretization-based Feature Construction Problem using Bi-level Evolutionary Optimization
Rihab Said;Slim Bechikh;Carlos A. Coello Coello;Lamjed Ben Said
2023 IEEE Congress on Evolutionary Computation (CEC)
Year: 2023 | Conference Paper |
Feature construction represents a crucial data preprocessing technique in machine learning applications because it ensures the creation of new informative features from the original ones. This fact leads to the improvement of the classification performance and the reduction of the problem dimensionality. Since many feature construction methods require discrete data, it is important to perform disc...Show More
Inferring Preferences for Multi-Criteria Ordinal Classification Methods Using Evolutionary Algorithms
Eduardo Fernández;Jorge Navarro;Efrain Solares;Carlos A. Coello Coello;Raymundo Díaz;Abril Flores
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 with attractive practical and theoretical charact...Show More
An Immune-Inspired Resource Allocation Strategy for Many-Objective Optimization
Lingjie Li;Qiuzhen Lin;Zhong Ming;Ka-Chun Wong;Maoguo Gong;Carlos A. Coello Coello
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 increased number of optimization objectives. To addres...Show More
AutoDock Koto: A Gradient Boosting Differential Evolution for Molecular Docking
Junkai Ji;Jin Zhou;Zhangfan Yang;Qiuzhen Lin;Carlos A. Coello Coello
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 to generate the ligand’s potential conformations a...Show More
Revisiting Implicit and Explicit Averaging for Noisy Optimization
Ali Ahrari;Saber Elsayed;Ruhul Sarker;Daryl Essam;Carlos A. Coello Coello
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 findings on the noisy sphere problem with additive G...Show More
Discretization-Based Feature Selection as a Bilevel Optimization Problem
Rihab Said;Maha Elarbi;Slim Bechikh;Carlos Artemio Coello Coello;Lamjed Ben Said
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 (ACO), etc. However, these methods share the same shortcoming which consists in encoding the problem solution as a sequence of cut-points. From this cut-points vector, the deci...Show More
Interval-based Cost-sensitive Classification Tree Induction as a Bi-level Optimization Problem
Rihab Said;Maha Elarbi;Slim Bechikh;Carlos A. Coello Coello;Lamjed Ben Said
2022 IEEE Congress on Evolutionary Computation (CEC)
Year: 2022 | Conference Paper |
Cost-sensitive learning is one of the most adopted approaches to deal with data imbalance in classification. Unfortunately, the manual definition of misclassification costs is still a very complicated task, especially with the lack of domain knowledge. To deal with the issue of costs' uncertainty, some researchers proposed the use of intervals instead of scalar values. This way, each cost would be...Show More
Static and Dynamic Multimodal Optimization by Improved Covariance Matrix Self-Adaptation Evolution Strategy With Repelling Subpopulations
Ali Ahrari;Saber Elsayed;Ruhul Sarker;Daryl Essam;Carlos A. Coello Coello
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 may become inefficient in certain situations. This study introduces the second variant of this method, called RS-CMSA-ESII. It improves the adaptation schemes for the normalized t...Show More
Pro-Reactive Approach for Project Scheduling Under Unpredictable Disruptions
Forhad Zaman;Saber Elsayed;Ruhul Sarker;Daryl Essam;Carlos A. Coello Coello
IEEE Transactions on Cybernetics
Cited by: Papers (2)
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 approach, with a novel pro-reactive scheme for handl...Show More
An Ensemble Surrogate-Based Framework for Expensive Multiobjective Evolutionary Optimization
Qiuzhen Lin;Xunfeng Wu;Lijia Ma;Jianqiang Li;Maoguo Gong;Carlos A. Coello Coello
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, thereby reducing the time cost of the optimization process. However, with the increased number of decision variables in EMOPs, the prediction accuracy of surrogate models will de...Show More
AdaSwarm: Augmenting Gradient-Based Optimizers in Deep Learning With Swarm Intelligence
Rohan Mohapatra;Snehanshu Saha;Carlos A. Coello Coello;Anwesh Bhattacharya;Soma S. Dhavala;Sriparna Saha
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 attributed to its capability to perform good gradi...Show More
Enhancing Robustness and Resilience of Multiplex Networks Against Node-Community Cascading Failures
Lijia Ma;Xiao Zhang;Jianqiang Li;Qiuzhen Lin;Maoguo Gong;Carlos A. Coello Coello;Asoke K. Nandi
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 studying network robustness and resilience, the compr...Show More
SNEGAN: Signed Network Embedding by Using Generative Adversarial Nets
Lijia Ma;Yuchun Ma;Qiuzhen Lin;Junkai Ji;Carlos A. Coello Coello;Maoguo Gong
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 novel generative adversarial nets learning framewor...Show More
A Fuzzy Decomposition-Based Multi/Many-Objective Evolutionary Algorithm
Songbai Liu;Qiuzhen Lin;Kay Chen Tan;Maoguo Gong;Carlos A. Coello Coello
IEEE Transactions on Cybernetics
Cited by: Papers (36)
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 evolutionary search, which can adapt to the target MOP...Show More
A Self-Guided Reference Vector Strategy for Many-Objective Optimization
Songbai Liu;Qiuzhen Lin;Ka-Chun Wong;Carlos A. Coello Coello;Jianqiang Li;Zhong Ming;Jun Zhang
IEEE Transactions on Cybernetics
Cited by: Papers (43)
Generally, decomposition-based evolutionary algorithms in many-objective optimization (MaOEA/Ds) have widely used reference vectors (RVs) to provide search directions and maintain diversity. However, their performance is highly affected by the matching degree on the shapes of the RVs and the Pareto front (PF). To address this problem, this article proposes a self-guided RV (SRV) strategy for MaOEA...Show More
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