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18th International Conference on Learning and Intelligent Optimization, LION 2024

作者： Basallo-Triana, Mario José Vidal-Holguín, Carlos Julio Bravo-Bastidas, Juan José Basallo-Triana, Yesid Fernando School of Industrial Engineering Universidad del Valle sede Buga Carrera 13 No. 5–21 Guadalajara de Buga Colombia School of Industrial Engineering Universidad del Valle Calle 13 No 100–00 Santiago de Cali Colombia Engineering Department Universidad Autónoma de Occidente Calle 25 No 115–85 Santiago de Cali Colombia

ISBN: (纸本)9783031756221

The design of intermodal hub networks is of paramount importance in logistic operations involving multiple transportation modes like trains and trucks. In this work, we consider two non-linear optimization models for the intermodal p-hub network design problem assuming that hubs can be located anywhere in a two-dimensional space. The first formulation focuses on minimizing congestion in transport activities, while the second formulation aims to maximize hub usage using the concept of entropy. To solve the problem, we propose two heuristic algorithms which are generalizations of well-known possibilistic clustering algorithms. These heuristics are relatively easy to implement and provide a practical way to efficiently solve problems. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： heuristic algorithms

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Bi-objective Enhanced Index Tracking: Performance Analysis of Meta-heuristic algorithms with Real-World Constraints 3rd

Bi-objective Enhanced Index Tracking: Performance Analysis o...

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3rd International Conference on Computing and Communication Networks, ICCCN 2023

作者： Rais, Ibtesaam Alam, Shahzad Kumar, Chanchal Meghwani, Suraj S. Jamia Millia Islamia New Delhi India Bennett University Greater Noida India Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9789819726707

Enhanced index tracking problem (EITP) aims to add sustainable value to portfolio management by emulating the behavior of the benchmark index while limiting the number of assets it holds. There have been numerous approaches to the EIT problem. Despite this, producing a high-quality portfolio continues to be a challenge. We examine bi-objective enhanced index tracking, which takes both the anticipated excess return of a portfolio associated to the benchmark and the degree of deviation from the benchmark, known as tracking error. In this approach, tracking error and excess return are used to measure the optimization of the problem. The objective of this study is to evaluate the relative efficacy of prominent meta-heuristic evolutionary algorithms NSGA-II, SPEA2, MOEA/D, and MOPSO in addressing enhanced index tracking problems with real-world constraints like a budget constraint, a bound constraint, and a cardinality constraint. Due to these constraints, this problem becomes nondeterministic polynomial-time hard. The paper examines the computational results of real-world benchmark instances. Our proposed methodology has been applied to five data sets derived from significant global markets. Preliminary empirical results show that NSGA-II outperforms other multiobjective evolutionary algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： heuristic algorithms

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Improvement strategies for heuristic algorithms based on machine learning and information concepts: a review of the seahorse optimization algorithm

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PeerJ Computer Science 2025年 11卷

作者： Zheng, Shixing School of Economics Nanjing University of Posts and Telecommunications Jiangsu Nanjing China

To overcome the mechanical limitations of traditional inertia weight optimization methods, this study draws inspiration from machine learning models and proposes an inertia weight optimization strategy based on the K-nearest neighbors (KNN) principle with dynamic adjustment properties. Unlike conventional approaches that determine inertia weight solely based on the number of iterations, the proposed strategy allows inertia weight to more accurately reflect the relative distance between individuals and the target value. Consequently, it transforms the discrete "iteration-weight" mapping (t ! w) into a continuous "distance-weight" mapping (d ! w), thereby enhancing the adaptability and optimization capability of the algorithm. Furthermore, inspired by the entropy weight method, this study introduces an entropy-based weight allocation mechanism in the crossover and mutation process to improve the efficiency of high-quality information inheritance. To validate its effectiveness, the proposed strategy is incorporated into the Seahorse Optimization Algorithm (SHO) and systematically evaluated using 31 benchmark functions from CEC2005 and CEC2021 test suites. Experimental results demonstrate that the improved SHO algorithm, integrating the logistic-KNN inertia weight optimization strategy and the entropy-based crossover-mutation mechanism, exhibits significant advantages in terms of convergence speed, solution accuracy, and algorithm stability. To further investigate the performance of the proposed improvements, this study conducts ablation experiments to analyze each modification separately. The results confirm that each individual strategy significantly enhances the overall performance of the SHO algorithm. Copyright 2025 Zheng

关键词： heuristic algorithms

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A Comprehensive Review of Swarm Intelligence-Based Meta-heuristic algorithms

A Comprehensive Review of Swarm Intelligence-Based Meta-Heur...

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Disruptive Technologies (ICDT), International Conference on

作者： Abhineet Suman Gunjan Anupam Biswas Department of Computer Science and Engineering National Institute of Technology Delhi India Department of Computer Science and Engineering National Institute of Technology Silchar Assam India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Swarm Intelligence (SI) algorithms, motivated by the group actions of natural systems, offer robust and efficient solutions to complex optimization problems. This review explores several key SI algorithms, including Cuckoo Search (CS), Firefly Algorithm (FA), Bat Algorithm (BA), and others. Each algorithm uses ideas from nature, like how ants find food and how bees interact with each other, to effectively solve nonlinear and multimodal problems. Comparative analysis focuses on convergence speed, solution quality, robustness, and computational complexity, highlighting the unique strengths and applicability of each algorithm. SI algorithms can be changed and scaled up or down easily, which makes them useful for dynamic and large-scale optimization tasks. This makes them future-proof solutions that can be used in many areas. Future research directions include new algorithms, hybrid approaches, real-world applications, and integration with machine learning techniques, promising continued advances and broader applicability. The development of SI algorithms is driven by their natural inspiration, simplicity, and potential for innovative applications, underscoring their importance in tackling diverse and complex challenges.

关键词： Machine learning algorithms Reviews heuristic algorithms Machine learning Robustness Computational complexity Particle swarm optimization Distributed computing Optimization Convergence

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Applying heuristic algorithms for Solving the 0–1 Knapsack Problem: An Experimental Analysis

Applying Heuristic Algorithms for Solving the 0–1 Knapsack ...

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International Conference on Robotics,Electrical and Signal Processing Techniques (ICREST)

作者： Fahim Faysal Sakib Jihad Shahriar Joy Sajjadul Islam Juel Md. Manzurul Hasan Department of Computer Science American International University-Bangladesh Dhaka Bangladesh

ISBN: (数字)9798331530952

ISBN: (纸本)9798331530969

This study analyses and compares the performance of six heuristic algorithms: Genetic Algorithm (GA), Simulated Annealing (SA), Hybrid (SA+GA), Tabu Search (TS), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO) in achieving the optimal solutions to combinatorial optimization problems represented by 0–1 Knapsack Problem (KP01). The algorithms were analyzed based on evaluation metrics like runtime, iteration time, performance, and complexity. The results indicate large differences in runtime efficiency and iteration speed. For runtime and iteration performance Tabu Search was the fastest with the lowest median runtime, followed by PSO and then GA. On the other hand, the average runtime for ACO is the longest and the highest standard deviation. The average time per iteration ranges from 0.027 ms for SA, to 70.8 ms per iteration for ACO which is very efficient. However, the hybrid method is competitive but provides a longer average time per iteration. Despite the variability, SA, GA, and PSO showed competitive results that can derive their robustness for combinatorial problems. These findings underline several key trade-offs and help to choose an optimal heuristic method that is robust and efficient for real-world decision-making applications.

关键词： Runtime heuristic algorithms Signal processing algorithms Simulated annealing Signal processing Search problems Robustness Time complexity Standards Genetic algorithms

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Optimization of Collaborative Delivery Between Rural Buses and Heterogeneous Drones Using heuristic algorithms

Optimization of Collaborative Delivery Between Rural Buses a...

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Digital Analysis and Processing, Intelligent Computation (DAPIC), International Conference on

作者： Huihui Yang Liangyu Pan Liqun Niu Chang'an University Xian China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

This paper explores the optimization of a rural bus and heterogeneous drone collaborative delivery system, aiming to reduce operational costs while enhancing delivery efficiency. A two-phase optimization approach is proposed, starting with the K-means clustering algorithm to partition demand points around bus stations, ensuring that each cluster has a designated drone launch point. In the first phase, a linear programming model is used to determine the delivery routes and schedules for Type A drones, minimizing delivery costs. The second phase expands the model to include three types of heterogeneous drones, with each drone type selected based on its payload capacity and flight characteristics. A simulated annealing algorithm is employed to optimize the drone deployment and delivery paths, further minimizing costs. The system also incorporates revenue optimization by considering pickup operations, where income is generated from collecting goods along the delivery route. The final model significantly improves the economic and operational efficiency of the rural delivery system, providing a practical solution for rural logistics and offering insights applicable to other collaborative delivery scenarios.

关键词： Schedules Costs heuristic algorithms Collaboration Clustering algorithms Simulated annealing Linear programming Optimization Drones Payloads

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Effective Dimension Extraction Mechanism: A novel mechanism for meta-heuristic algorithms in solving complex high-dimensional problems

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Expert Systems with Applications 2025年 285卷

作者： Su, Fang Song, Jiahao He, Rui Beijing University of Posts and Telecommunications Beijing100876 China

In recent years, meta-heuristic optimization algorithms have been increasingly studied due to their wide applicability in practical applications. However, the interference of redundant dimensions in complex high-dimensional problems often leads to performance degradation. To solve this problem, a new Effective Dimension Extraction Mechanism (EDEM) is proposed, which extracts effective dimensional information during the optimization using a Manifold Learning-based Mapping from high-dimensional to low-dimensional space and a Feature Mapping from low-dimensional to the original high-dimensional space, thereby providing direct guidance information for meta-heuristic optimization algorithms to correct the evolution direction of particle populations. As an independent mechanism, EDEM can be easily applied to multiple meta-heuristic optimization algorithms, providing a new way for these algorithms to solve the problems. A series of experiments are conducted to validate the effectiveness and advancement of EDEM. First, experiments on fifteen complex high-dimensional problems demonstrate its effectiveness in improving DE, PSO and ASO performance. Next, the comparative evaluation experiments with the state-of-art PSO and DE variant algorithms are performed, and the results confirm that EDEM is an effective mechanism, which provides a promising solution for the optimization on complex high-dimensional problems. Finally, EDEM is applied to the Capacitated Vehicle Routing Problem, and the experimental results show that the improved meta-heuristic optimization algorithm of EDEM can solve this practical problem well, which shows the practical ability of this new mechanism. © 2025 Elsevier Ltd

关键词： heuristic algorithms

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Review and Comparative Analysis of Meta-heuristic-MPPT algorithms of Photovoltaic Panels Under PSC

Review and Comparative Analysis of Meta-Heuristic-MPPT Algor...

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International Conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES 2024

作者： Hamidia, Fethia Abbadi, Amel Medjber, Ahmed Morsli, Abdelkader Skender Mohamed, Redha Safaa, Madani REA Laboratory Yahia Feres Medea University Medea Algeria REM Laboratory Yahia Feres Medea University Medea Algeria Yahia feres University Medea Algeria

ISBN: (纸本)9783031803000

Shading presents a significant challenge to the efficiency of photovoltaic (PV) panels, often resulting in suboptimal operation and reduced power generation, thereby leading to economic losses. Traditional Maximum Power Point Tracking (MPPT) algorithms, such as Perturb and Observe (P&O) or Incremental Conductance (Inc-Cond), may struggle to effectively handle Partial Shading Conditions (PSC), where the power-voltage (P-V) curves exhibit multiple local peaks alongside a singular global peak. In contrast, metaheuristic algorithms demonstrate adaptability and efficacy in find the global maximum. This study investigates and assesses the MPPT capabilities of six optimization techniques: Particle Swarm Optimization (PSO), Moth-flame Algorithm (MFA), Cuckoo Search Algorithm (CSA), Whale Optimization Algorithm (WOA), Flower Pollination Algorithm (FPA), and Grey Wolf Optimization Algorithm (GWO). Metaheuristic algorithms are recognized for their potential to converge more efficiently towards the optimal solution compared to traditional methods. This efficiency is particularly advantageous in real-time applications where rapid adaptation to changing environmental conditions is crucial for maximizing energy harvesting efficiency. The findings from this research contribute to advancing the understanding and application of metaheuristic approaches in enhancing the performance and resilience of PV systems under challenging operational conditions like Partial Shading. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： heuristic algorithms

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heuristic algorithms for Recognition of Some Cubic Hypersurfaces

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PROGRAMMING AND COMPUTER SOFTWARE 2021年第1期47卷 50-55页

作者： Seliverstov, A., V Russian Acad Sci Kharkevich Inst Inst Informat Transmiss Problems Bolshoi Karetnyi 19 Moscow 127051 Russia

In this paper, we propose some heuristic probabilistic polynomial time algorithms with one-sided error for recognition of cubic hypersurfaces the singular loci of which do not contain any linear subspace of sufficiently large dimension. These algorithms are easy to implement in computer algebra systems. The algorithms are based on checking the condition that the Hessian determinant of a cubic form does not vanish identically or does not determine any cone in the projective space. In turn, the properties of the Hessian can be verified with one-sided-error probabilistic algorithms based on the Schwartz-Zippel lemma.

关键词： heuristic algorithms

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heuristic algorithms for RIS-Assisted Wireless Networks: Exploring heuristic-Aided Machine Learning

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IEEE WIRELESS COMMUNICATIONS 2024年第4期31卷 106-114页

作者： Zhou, Hao Erol-Kantarci, Melike Liu, Yuanwei Poor, H. Vincent Univ Ottawa Ottawa ON Canada Queen Mary Univ London London England Princeton Univ Princeton NJ USA

Reconfigurable intelligent surfaces (RISs) are a promising technology to enable smart radio environments. However, integrating RISs into wireless networks also leads to substantial complexity for network management. This work investigates heuristic algorithms and applications to optimize RIS-aided wireless networks, including greedy algorithms, meta-heuristic algorithms, and matching theory. Moreover, we combine heuristic algorithms with machine learning (ML), and propose three heuristic-aided ML algorithms: heuristic deep reinforcement learning (DRL), heuristic-aided supervised learning, and heuristic hierarchical learning. Finally, a case study shows that heuristic DRL can achieve higher data rates and faster convergence than conventional deep Q-networks (DQNs). This work provides a new perspective for optimizing RIS-aided wireless networks by taking advantage of heuristic algorithms and ML.

关键词： heuristic algorithms Wireless networks Machine learning algorithms Metaheuristics Signal processing algorithms Greedy algorithms Training

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