Article
Computer Science, Artificial Intelligence
Bahriye Akay, Dervis Karaboga, Beyza Gorkemli, Ebubekir Kaya
Summary: This paper reviews the use of Artificial Bee Colony algorithm for solving discrete numeric optimization problems, discussing various encoding types, search operators and selection operators integrated into ABC. It is the first comprehensive survey study on this topic and aims to benefit readers interested in utilizing ABC for binary, integer and mixed integer discrete optimization problems.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Nicolas Rojas-Morales, Maria-Cristina Riff, Elizabeth Montero
Summary: A collaborative framework called Multiple Opposite Synergic Strategy for Ants (MOSSA) is proposed in this paper to improve the search process of ant-based algorithms using multiple Opposition-Inspired Learning strategies. By collaborating different strategies, the ants algorithm shows better performance in solving Constraint Satisfaction Problems.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Anand Kumar, Manoj Thakur, Garima Mittal
Summary: This study extends ant colony optimization algorithm for solving Economic Load Dispatch (ELD) problems, proposing a new Constrained Ant Colony Optimization (ACO) algorithm with Adaptive Penalty (AP) method named CACO-LD-AP. Experimental results demonstrate the efficiency and superiority of CACO-LD-AP over other algorithms in terms of solution quality.
APPLIED SOFT COMPUTING
(2022)
Article
Management
Hassan T. Anis, Roy H. Kwon
Summary: This paper investigates the cardinality constrained risk parity optimization problem and proposes two formulations that can be solved to global optimality using existing solvers. The experiments show that the convex formulation is efficient in terms of both speed and accuracy, and the resulting portfolios exhibit great out-of-sample performance.
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Mohamed Kurdi
Summary: This work proposes a new metaheuristic algorithm called ACONEH for open shop scheduling problem with the goal of improving the exploration capability of ant colony optimization and solving OSSP more effectively. The algorithm utilizes a new heuristic information approach that incorporates randomness, diversity, and improvability. Experimental results show that ACONEH achieves significant improvements in reducing the makespan of OSSP compared to traditional methods.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Dong Zhao, Lei Liu, Fanhua Yu, Ali Asghar Heidari, Mingjing Wang, Guoxi Liang, Khan Muhammad, Huiling Chen
Summary: By enhancing the selection mechanism of the ACOR method and introducing random spare strategy and chaotic intensification strategy, the convergence speed and accuracy can be significantly improved, effectively avoiding local optima. Through a series of experiments, these improved methods demonstrate superior performance in problem-solving, and compared to other techniques, RCACO has a more reliable ability to step out of local optima.
KNOWLEDGE-BASED SYSTEMS
(2021)
Article
Mathematics
Ibrahim Al-Shourbaji, Na Helian, Yi Sun, Samah Alshathri, Mohamed Abd Elaziz
Summary: This paper discusses the importance of feature selection in the telecommunications industry for machine learning models. It introduces a new approach that combines ant colony optimization and reptile search algorithm, and evaluates its performance in customer churn prediction.
Article
Operations Research & Management Science
Tommaso Giovannelli, Giampaolo Liuzzi, Stefano Lucidi, Francesco Rinaldi
Summary: This paper focuses on mixed-integer nonsmooth constrained optimization problems where objective/constraint functions are only available as the output of a black-box zeroth-order oracle that lacks derivative information. The authors propose a novel derivative-free linesearch-based algorithmic framework to handle these problems effectively. They first describe a scheme for bound constrained problems that combines dense sequence directions with primitive directions to handle the nonsmoothness of the objective function and discrete variables. Then, they embed an exact penalty approach in the scheme to manage nonlinear (possibly nonsmooth) constraints suitably. The proposed algorithms are analyzed for their global convergence properties towards stationary points, and extensive numerical experiments on a set of mixed-integer test problems are presented.
COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
(2022)
Article
Mathematics, Applied
Dominik Garmatter, Margherita Porcelli, Francesco Rinaldi, Martin Stoll
Summary: This article investigates penalization techniques as an alternative solution approach for optimal control problems with PDE and integer constraints. A novel improved penalty algorithm is proposed, incorporating a basin hopping strategy and an interior point method specialized for the problem class. Thorough numerical investigations demonstrate the versatility of the approach.
COMPUTERS & MATHEMATICS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Fadl Dahan
Summary: In this study, an efficient agent-based ant colony optimization (ACO) algorithm is introduced to solve the cloud service composition problem, which aims to meet the complex and challenging requirements of enterprises/users in a cloud environment. The computational results demonstrate the effectiveness of the multi-agent ACO approach across 25 real datasets, showing competitiveness with state-of-the-art algorithms in literature comparisons.
Article
Operations Research & Management Science
Charis Ntakolia, Dimitrios Lyridis
Summary: Recent studies suggest that the expected increase in flight volume by 2030 will lead to air traffic capacity and congestion issues. This study proposes a mixed integer nonlinear model of Air Traffic Flow Management (ATFM) based on 4D trajectories and free flight aspects, and introduces a novel n-D ant colony optimization algorithm integrated with fuzzy logic (n-DACOF) to solve the problem.
OPERATIONAL RESEARCH
(2022)
Article
Automation & Control Systems
Yangfei Yuan, Weifeng Gao, Lingling Huang, Hong Li, Jin Xie
Summary: This article proposes a two-phase constraint-handling technique, called TPDE, for solving constrained optimization problems. In the exploration phase, an exterior penalty function method is used to push the population into the feasible region, while in the exploitation phase, an interior penalty function method is used to enhance the search ability. Experimental results show that TPDE is competitive with other popular algorithms on benchmark test suites.
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Walid Behiri, Sana Belmokhtar-Berraf, Chengbin Chu
Summary: This paper considers a promising alternative for freight transport using urban passenger rail networks, and addresses the scheduling problem of freight rail transport. By developing robust ant colony optimization algorithm, the authors have successfully provided near-optimal solutions in short computation times.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Computer Science, Software Engineering
Yining Gu, Yanjun Wang
Summary: In this paper, an exact reformulation and a deterministic approximation method are proposed for distributionally robust joint chance-constrained programmings with convex uncertain constraints and data-driven Wasserstein ambiguity sets. It is shown that the proposed worst-case CVaR approximation model can be exactly reformulated as an optimization problem involving biconvex constraints for joint DRCCP. A convex relaxation of this approximation model is derived by introducing new decision variables to eliminate biconvex terms. The numerical results demonstrate the computational effectiveness and superiority of the proposed formulations.
OPTIMIZATION METHODS & SOFTWARE
(2023)
Article
Operations Research & Management Science
Ran Ji, Miguel A. Lejeune
Summary: This study focuses on distributionally robust chance-constrained programming optimization problems with data-driven Wasserstein ambiguity sets, proposing algorithmic solutions for various uncertainties. The proposed linear programming and mixed-integer second-order cone programming formulations are evaluated for scalability and tightness on a distributionally robust chance-constrained knapsack problem, showcasing their effectiveness in different uncertainty scenarios.
JOURNAL OF GLOBAL OPTIMIZATION
(2021)
Article
Mathematics, Applied
Bjoern Martens, Matthias Gerdts
SET-VALUED AND VARIATIONAL ANALYSIS
(2019)
Article
Automation & Control Systems
Alberto De Marchi, Matthias Gerdts
Article
Operations Research & Management Science
Marcella Sama, Andrea D'Ariano, Konstantin Palagachev, Matthias Gerdts
Article
Automation & Control Systems
Ilaria Xausa, Robert Baier, Olivier Bokanowski, Matthias Gerdts
OPTIMAL CONTROL APPLICATIONS & METHODS
(2020)
Article
Automation & Control Systems
Bjoern Martens, Matthias Gerdts
SIAM JOURNAL ON CONTROL AND OPTIMIZATION
(2020)
Article
Automation & Control Systems
Damir Rusiti, Tiago Roux Oliveira, Miroslav Krstic, Matthias Gerdts
Summary: This paper presents a Newton-based extremum seeking algorithm for maximizing higher derivatives of unknown maps in the presence of time-varying delays. The algorithm incorporates a filtered predictor feedback with a perturbation-based estimate for the Hessian’s inverse. Exponential stability and convergence to a small neighborhood of the unknown extremum point are achieved for locally quadratic derivatives using backstepping transformation and averaging theory in infinite dimensions.
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING
(2021)
Article
Operations Research & Management Science
Alberto De Marchi, Axel Dreves, Matthias Gerdts, Simon Gottschalk, Sergejs Rogovs
Summary: This paper presents a novel approach for approximating the primal and dual parameter-dependent solution functions of parametric optimization problems. The authors propose an equation reformulation to derive the necessary optimality conditions and then use approximating functions to find optimal coefficients for test parameters. The stationary points are proven to be global minima, and the function approximations interpolate the solution functions at all test parameters. Additionally, the authors propose a cheap function evaluation criterion to estimate the approximation error. Preliminary numerical results demonstrate the feasibility of the approach.
JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS
(2023)
Article
Chemistry, Analytical
Markus Zwick, Matthias Gerdts, Peter Stuetz
Summary: This study presents an innovative concept that maps the influence of environmental conditions on detection performance using sensor performance models, aiming to increase the accuracy of object detection in aerial reconnaissance. The optimized reference flight trajectories are calculated using nonlinear model predictive control and dynamic programming, combined with a newly developed sensor performance model. By adjusting the sensor position, the sensor data acquisition is optimized to enhance the detection performance.
Article
Automation & Control Systems
Jeremy Bertoncini, Viktoriya Nikitina, Matthias Gerdts
Summary: This letter investigates a coordinated multi-agent path planning and tracking method. The solution of a pre-processed dynamic scheduling problem performs target assignment and provides optimal starting times and paths for each agent. Afterwards, a linear model predictive controller ensures robust and fast path tracking while preventing agents from collisions. This task is formulated as a discretized quadratic programming (QP) problem and is solved using an in-house developed semi-smooth Newton method. Numerical experiments have demonstrated the efficiency of the approach.
IEEE CONTROL SYSTEMS LETTERS
(2023)
Article
Engineering, Aerospace
Zhidong Lu, Haichao Hong, Matthias Gerdts, Florian Holzapfel
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS
(2022)
Article
Automation & Control Systems
Bjoern Martens, Matthias Gerdts
Summary: The paper establishes error estimates for implicit Euler discretizations of optimal control problems involving index two differential-algebraic equations and first-order pure state constraints. By transforming discrete Lagrange multipliers, discrete necessary conditions consistent with the continuous ones are derived. It is proved that a perturbed version of the discretized problem has a solution for sufficiently small perturbations, with an error estimate dependent on the perturbation and mesh size in relation to the continuous solution.
SIAM JOURNAL ON CONTROL AND OPTIMIZATION
(2021)
Article
Engineering, Aerospace
Haichao Hong, Patrick Piprek, Matthias Gerdts, Florian Holzapfel
JOURNAL OF GUIDANCE CONTROL AND DYNAMICS
(2021)
Proceedings Paper
Computer Science, Theory & Methods
Alberto De Marchi, Matthias Gerdts
COMPUTATIONAL SCIENCE - ICCS 2019, PT III
(2019)
Article
Mathematics
Andreas Huber, Matthias Gerdts, Enrico Bertolazzi
VIETNAM JOURNAL OF MATHEMATICS
(2018)
Article
Automation & Control Systems
Damir Rusiti, Giulio Evangelisti, Tiago Roux Oliveira, Matthias Gerdts, Miroslav Krstic
IEEE CONTROL SYSTEMS LETTERS
(2019)
