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Hybrid slime mould algorithm with adaptive guided differential evolution algorithm for combinatorial and global optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 174卷 114689-114689页

作者： Houssein, Essam H. Mahdy, Mohamed A. Blondin, Maude J. Shebl, Doaa Mohamed, Waleed M. Minia Univ Fac Comp & Informat Al Minya Egypt Beni Suef Univ Fac Comp & Artificial Intelligence Bani Suwayf Egypt Univ Sherbrooke Dept Elect Engn Sherbrooke PQ Canada

The Slime Mould Algorithm (SMA) is a recent metaheuristic inspired by the oscillation of slime mould. Similar to other original metaheuristic algorithms (MAs), SMA may suffer from drawbacks, such as being trapped in minimum local regions and improper balance between exploitation and exploration phases. To overcome these weaknesses, this paper proposes a hybrid algorithm: SMA combined to Adaptive Guided Differential Evolution Algorithm (AGDE) (SMA-AGDE). The AGDE mutation method is employed to enhance the swarm agents' local search, increase the population's diversity, and help avoid premature convergence. The SMA-AGDE's performance is evaluated on the CEC'17 test suite, three engineering design problems - tension/compression spring, pressure vessel, and rolling element bearing - and two combinatorial optimization problems - bin packing and quadratic assignment. The SMA-AGDE is compared with three categories of optimization methods: (1) The wellstudied MAs, i.e., Biogeography-Based Optimizer (BBO), Gravitational Search Algorithm (GSA), and Teaching Learning-Based optimization (TLBO), (2) Recently developed MAs, i.e., Harris Hawks optimization (HHO), Manta Ray Foraging optimization (MRFO), and the original SMA, and (3) High-performance MAs, i.e., Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), and AGDE. The overall simulation results reveal that the SMA-AGDE ranked first among the compared algorithms, and so, over different function landscapes. Thus, the proposed SMA-AGDE is a promising optimization tool for global and combinatorial optimization problems and engineering design problems.

关键词： Adaptive guided differential evolution algorithm (AGDE) combinatorial optimization problems Engineering design problems Exploration and exploitation Global optimization problems Metaheuristics Slime mould algorithm (SMA)

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Local Search Algorithms for Solving the combinatorial optimization and Constraint Satisfaction problems 6th

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6th International Workshop on Soft Computing Applications (SOFA)

作者： Kilani, Y. Alsarhan, A. Bsoul, M. Otoom, A. F. Hashemite Univ Fac Prince Al Hussein Bin Abdullah II Informat Te Dept Comp Informat Syst Zarqa Jordan Hashemite Univ Fac Prince Al Hussein Bin Abdullah II Informat Te Dept Comp Sci & Applicat Zarqa Jordan Hashemite Univ Fac Prince Al Hussein Bin Abdullah II Informat Te Dept Software Engn Zarqa Jordan

ISBN: (纸本)9783319182964

Local search is a metaheuristic for solving computationally hard optimization problems. In the past three decades, local search has grown from a simple heuristic idea into a mature field of research in combinatorial optimization that is attracting ever-increasing attention. It is still the method of choice for NP-hard problems as it provides a robust approach for obtaining high-quality solutions to problems of a realistic size in reasonable time. optimization problems such as the shortest path, the traveling salesman, pin packing, and the Knapsack problems. Local search techniques have been successful in solving large and tight constraint satisfaction problems. Local search algorithms turn out to be effective in solving many constraint satisfaction problems. This chapter gives an introduction to the local search algorithms, the optimization and the constraint satisfaction problems, and the local search methods used to solve them.

关键词： Local search Introduction combinatorial optimization problems Constraint satisfaction problems

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Generic agent-based optimization framework to solve combinatorial problems

Generic agent-based optimization framework to solve combinat...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Ajmi, Faiza Zgaya, Hayfa Ben Othman, Sarah Hammadi, Slim CRIStAL CNRS UMR 9189 Villeneuve Dascq France Univ Lille CERIM Lille France

ISBN: (纸本)9781728185262

The aim of this paper is to describe our proposed ABOS framework (Agent-Based optimization Systems) by demonstrating the interest in using the multi-agent approach while operating hybrid metaheuristics to solve combinatorial optimization problems (COP). Two main contributions are highlighted in this work: 1) to show that the alliance of the multi-agent systems (MAS) and the metaheuristics, based on the interaction and the parallelisms concepts, facilitates the hybrid metaheuristics development and allows the simultaneous exploration of different regions of the search space and 2) to demonstrate that the use the multi-agent approach, in the context of optimization, is a crucial option in the process of hybridization allowing the development of generic structures. These later promote the interaction between metaheuristics independent of the problem to be addressed. Our challenge in this ABOS framework is to endow the participant agents, with a set of rational behaviours allowing them to change in real time their strategies, according to the optimization process evolution. The simulation results show that the collaborative optimization can be effective in some cases, hence the need to set effectively the parameters of the optimization algorithms behaviours and the collaborative protocols. We also demonstrate that the use of ABOS framework with MAS allows a more robust and generic structure, capable with minimal changes handling different COP.

关键词： collaborative optimization multi-agent system combinatorial optimization problems hybrid optimization meta-heuristics

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Hyper-Heuristics and Scheduling problems: Strategies, Application Areas, and Performance Metrics

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IEEE ACCESS 2025年 13卷 14983-14997页

作者： Vela, Alonso Valencia-Rivera, Gerardo Humberto Cruz-Duarte, Jorge M. Ortiz-Bayliss, Jose Carlos Amaya, Ivan Tecnol Monterrey Sch Engn & Sci Monterrey 64700 Mexico Univ Lille CRIStAL Inria CNRS UMR 9189Cent Lille F-59000 Lille France

Scheduling problems, which involve allocating resources to tasks over specified time periods to optimize objectives, are crucial in various fields. This work presents hyper-heuristic applications for scheduling problems, analyzing 215 peer-reviewed publications over the last decade. We categorize and examine the prevailing strategies and configurations of hyper-heuristics, mainly focusing on their application across diverse scheduling scenarios such as job shop, flow shop, timetabling, and project scheduling. Our findings reveal a strong inclination towards selection and perturbative hyper-heuristics, with evolutionary computation emerging as the most commonly employed technique in this context, particularly in job shop and timetabling problems. Despite the robust development in hyper-heuristic methodologies, our analysis indicates an under-representation of multi-objective optimization and a limited use of performance metrics beyond makespan and tardiness. We also identify potential areas for future research, such as expanding hyper-heuristic applications to underexplored industries and exploring less conventional performance metrics. By providing a comprehensive overview of the current landscape and outlining future research directions, we aim to guide and inspire ongoing innovations in scheduling problem research.

关键词： Job shop scheduling Heuristic algorithms Measurement Approximation algorithms Systematic literature review Schedules Processor scheduling Metaheuristics Resource management Production combinatorial optimization problems hyper-heuristics job shop scheduling scheduling problems

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Dna coding theory and algorithms

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ARTIFICIAL INTELLIGENCE REVIEW 2025年第6期58卷 1-37页

作者： Xu, Jin Liu, Wenbin Zhang, Kai Zhu, Enqiang Peking Univ Sch Comp Sci Key Lab High Confidence Software Technol Minist Ed Beijing 100871 Peoples R China Beijing Technol & Business Univ Sch Comp & Artificial Intelligence Beijing 100048 Peoples R China Guangzhou Univ Inst Comp Technol Guangzhou 510006 Peoples R China Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430081 Peoples R China

DNA computing is an emerging computational model that has garnered significant attention due to its distinctive advantages at the molecular biological level. Since it was introduced by Adelman in 1994, this field has made remarkable progress in solving NP-complete problems, enhancing information security, encrypting images, controlling diseases, and advancing nanotechnology. A key challenge in DNA computing is the design of DNA coding, which aims to minimize nonspecific hybridization and enhance computational reliability. The DNA coding design is a classical combinatorial optimization problem focused on generating high-quality DNA sequences that meet specific constraints, including distance, thermodynamics, secondary structure, and sequence requirements. This paper comprehensively examines the advances in DNA coding design, highlighting mathematical models, counting theory, and commonly used DNA coding methods. These methods include the template method, multi-objective evolutionary methods, and implicit enumeration techniques.

关键词： DNA computing DNA coding design combinatorial optimization problems Multi-objective optimization algorithms

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Utility of NISQ devices: optimizing experimental parameters for the fabrication of Au atomic junction using gate-based quantum computers

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APPLIED PHYSICS EXPRESS 2025年第4期18卷

作者： Kanezashi, Takumi Tsukayama, Daisuke Shirakashi, Jun-ichi Shibuya, Tetsuo Imai, Hiroshi Tokyo Univ Agr & Technol Dept Elect Engn & Comp Sci Koganei Tokyo 1848588 Japan Univ Tokyo Inst Med Sci Human Genome Ctr Div Med Data Informat Minato Tokyo 1088639 Japan Univ Tokyo Grad Sch Informat Sci & Technol Bunkyo Tokyo 1138656 Japan

Feedback-controlled electromigration (FCE) enables precise regulation of atomic migration by carefully optimizing multiple experimental parameters. However, manually fine-tuning these parameters poses significant challenges. This study investigated the feasibility of autonomously fabricating Au atomic junctions through gate-based quantum computing using a noisy intermediate-scale quantum (NISQ) device, which effectively approximates solutions to combinatorial optimization problems. We compared the computational accuracy of the NISQ device against a previously reported D-Wave quantum annealer. The results indicate that the NISQ device achieved lower residual energies and produced higher-quality approximate solutions for large-scale problems than the quantum annealing system.

关键词： gate-based quantum computer feedback-controlled electromigration variational quantum eigensolver combinatorial optimization problems

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A Methodology of the Mathematical Modeling for Perspective Development of Nodes and Transport Routes in the Multicommodity Hierarchical Network. II. Experimental Research

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CYBERNETICS AND SYSTEMS ANALYSIS 2025年第2期61卷 232-249页

作者： Vasyanin, V. A. Trofymchuk, O. M. Ushakova, L. P. Natl Acad Sci Ukraine Inst Telecommun & Global Informat Space Kyiv Ukraine

This article is the second part of the study, which proposes a methodology for mathematical modeling of the phased development of nodes and transport routes in a hierarchical network with multicommodity discrete correspondence flows. As a rule, such networks consist of a decentralized mainline network and networks in the internal service areas of mainline nodes. In a multicommodity network, each node can exchange correspondence (products, goods, cargo, messages) with others. In the mainline network, all correspondence is transmitted via communication channels or transported by vehicles in transport units of a specified size (capacity, volume). In this part of the study, using the example of transportation networks, it is experimentally shown that the step-by-step solution to the problems of optimizing the structure of the mainline network and the distribution and routing of flows allows obtaining initial data for constructing dynamic deterministic and stochastic models of their development. The authors also demonstrated how one can use these problems for the operational redistribution of flows in case of equipment failures in nodes and communication lines (exceeding the capacities of nodes and communication channels, the transportation capacity of vehicles, etc.).

关键词： multicommodity hierarchical networks discrete flows combinatorial optimization problems mathematical models computer modeling

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SKI-SAT: A CMOS-Compatible Hardware for Solving SAT problems

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2025年

作者： Salim, Ahmet Yusuf Selman, Bart Kautz, Henry Ignjatovic, Zeljko Kose, Selcuk Univ Rochester Dept Elect & Comp Engn Rochester NY 14627 USA Cornell Univ Dept Comp Sci Ithaca NY 14850 USA Univ Virginia Dept Comp Sci Charlottesville VA 22903 USA

Nature-inspired computation is receiving increasing attention. Various Ising machine (IM) implementations have recently been proven to be effective in solving numerous combinatorial optimization problems including maximum cut, low density parity check (LDPC) decoding, and Boolean satisfiability (SAT) problems. In this paper, a novel method is presented to solve SAT or MAX-SAT problems with a CMOS circuit implementation. The technique solves a SAT problem by mapping the SAT variables onto quantized capacitor voltages generated by an array of nodes that interact through a network of coupling units. The nodal interaction is achieved through coupling currents produced by the coupling units, which charge or discharge capacitor voltages, implementing a gradient descent along the SAT problem's cost function to minimize the number of unsatisfied clauses. The system also incorporates a unique low-complexity perturbation scheme to avoid settling in local minima, greatly enhancing the performance of the system. The simulation results demonstrate that the proposed SKI-SAT is a high-performance and low-energy alternative that surpasses existing software-based SAT solvers by significant margins, achieving more than 10 times faster solution and over 300 times less power.

关键词： Hardware Cost function Polynomials Couplings Circuits Vectors Analog computers Perturbation methods Parity check codes Digital computers combinatorial optimization problems Ising machine Boolean satisfiability SAT CMOS nature-based computing

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Hybrid GRU-CNN bilinear parameters initialization for quantum approximate optimization algorithm

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PHYSICA SCRIPTA 2024年第8期99卷 085105-085105页

作者： Xu, Zuyu Cai, Pengnian Shen, Kang Yang, Tao Hu, Yuanming Gong, Maogao Zhu, Yunlai Wu, Zuheng Dai, Yuehua Yang, Fei Anhui Univ Sch Integrated Circuits Hefei 230601 Anhui Peoples R China

The Quantum Approximate optimization Algorithm (QAOA), a pivotal paradigm in the realm of variational quantum algorithms (VQAs), offers promising computational advantages for tackling combinatorial optimization problems. Well-defined initial circuit parameters, responsible for preparing a parameterized quantum state encoding the solution, play a key role in optimizing QAOA. However, classical optimization techniques encounter challenges in discerning optimal parameters that align with the optimal solution. In this work, we propose a hybrid optimization approach that integrates Gated Recurrent Units (GRU), Convolutional Neural Networks (CNN), and a bilinear strategy as an innovative alternative to conventional optimizers for predicting optimal parameters of QAOA circuits. GRU serves to stochastically initialize favorable parameters for depth-1 circuits, while CNN predicts initial parameters for depth-2 circuits based on the optimized parameters of depth-1 circuits. To assess the efficacy of our approach, we conducted a comparative analysis with traditional initialization methods using QAOA on Erd & odblac;s-R & eacute;nyi graph instances, revealing superior optimal approximation ratios. We employ the bilinear strategy to initialize QAOA circuit parameters at greater depths, with reference parameters obtained from GRU-CNN optimization. This approach allows us to forecast parameters for a depth-12 QAOA circuit, yielding a remarkable approximation ratio of 0.998 across 10 qubits, which surpasses that of the random initialization strategy and the PPN2 method at a depth of 10. The proposed hybrid GRU-CNN bilinear optimization method significantly improves the effectiveness and accuracy of parameters initialization, offering a promising iterative framework for QAOA that elevates its performance.

关键词： QAOA combinatorial optimization problems hybrid optimization gated recurrent units (GRU) convolutional neural networks (CNN) bilinear strategy parameter initialization

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Integrating constraint logic programming and operations research techniques for the crew rostering problem

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SOFTWARE-PRACTICE & EXPERIENCE 1998年第1期28卷 49-76页

作者： Caprara, A Focacci, F Lamma, E Mello, P Milano, M Toth, P Vigo, D Univ Bologna DEIS I-40136 Bologna Italy

In this paper, we investigate the possibility of integrating Artificial Intelligence (AI) and Operations Research (OR) techniques for solving the Crew Rostering Problem (CRP). CRP calls for the optimal sequencing of a given set of duties into rosters satisfying a set of constraints. The optimality criterion requires the minimization of the number of crews needed to cover the duties. This kind of problem has been traditionally solved by OR techniques. In recent years, a new programming paradigm based on Logic Programming, named Constraint Logic Programming (CLP), has been successfully used for solving hard combinatorial optimization problems. CLP maintains all the advantages of logic programming such as declarativeness, non-determinism and an incremental style of programming, while overcoming its limitations, mainly due to the inefficiency in exploring the search space, CLP achieves good results on hard combinatorial optimization problems which, however, are not comparable with those achieved by OR approaches, Therefore, we integrate both techniques in order to design an effective heuristic algorithm for CRP which fully exploits the advantages of the two methodologies: on the one hand, we maintain the declarativeness of CLP, its ease of representing knowledge and its rapid prototyping;on the other hand, we inherit from OR some efficient procedures based on a mathematical approach to the problem, Finally, we compare the results we achieved by means of the integration with those obtained by a pure OR approach, showing that AP and OR techniques for hard combinatorial optimization problems can be effectively integrated, (C) 1998 by John Wiley & Sons, Ltd.

关键词： constraint logic programming operations research crew rostering problem combinatorial optimization problems artificial intelligence

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