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ACM Transactions on Evolutionary Learning and optimization 2025年第1期5卷 1-53页

作者： Zhao, Shihan Tjanaka, Bryon Fontaine, Matthew C. Nikolaidis, Stefanos Thomas Lord Department of Computer Science University of Southern California Los AngelesCA United States

Single-objective optimization algorithms search for the single highest quality solution with respect to an objective. Quality diversity (QD) optimization algorithms, such as Covariance Matrix Adaptation MAP-Elites (CMA-ME), search for a collection of solutions that are both high quality with respect to an objective and diverse with respect to specified measure functions. However, CMA-ME suffers from three major limitations highlighted by the QD community: prematurely abandoning the objective in favor of exploration, struggling to explore flat objectives, and having poor performance for low-resolution archives. We propose a new QD algorithm, CMA MAP-Annealing (CMA-MAE), and its differentiable QD variant, CMA-MAE via a Gradient Arborescence (CMA-MAEGA), that address all three limitations. We provide theoretical justifications for the new algorithm with respect to each limitation. Our theory informs our experiments, which support the theory and show that CMA-MAE achieves state-of-the-art performance and robustness on standard QD benchmark and reinforcement learning domains. © 2025 Copyright held by the owner/author(s).

关键词： optimization algorithms

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Application of Improved Multi-Objective Evolutionary Algorithm in Intelligent Tourism Interest Point Recommendation and Itinerary Planning

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第7期49卷 1-16页

作者： Zhang, Jieli Li, Yan School of Humanities & Tourism Yiwu Industrial & Commercial College Yiwu322000 China

In the context of a boost in tourism and transportation, people's needs for the quality of tourism services are also increasing. Traditional scenic spot recommendations and itinerary planning methods cannot meet the personalized needs of tourists. Therefore, to achieve personalized services for tourist attractions and itineraries, this study introduces weakly correlated adaptive evolutionary algorithms and archival strategy algorithms to improve multi-objective optimization algorithms. It proposes an adaptive multi-objective evolutionary algorithm model for interest point recommendation and a multiobjective archival ant colony algorithm model for itinerary planning. The experimental results demonstrated that the proposed algorithm exhibited a 10% superiority over the enhanced algorithm in the recommended value of tourist attraction heat features and a 22.2% superiority in the recommended value of tourist attraction social network features. In trip planning, the convergence speed of the proposed algorithm model was faster, and the optimal solution could be found in 100 iterations, while the traditional algorithm needed 140 iterations. In addition, the DTLZ and WFG function test sets were used to evaluate the algorithm’s performance. When the target number was 8, the HV value of the proposed algorithm was 0.42 and 0.16 higher than that of the improved algorithm and MOEADD algorithm, respectively. In the trip planning experiment, the total trip length was shortened from 213.4 km to 170.2 km by optimizing the path, effectively reducing unnecessary travel distance. The introduction of a weak correlation adaptive evolutionary algorithm and archiving strategy has led to a notable enhancement in the precision and efficacy of travel interest point recommendation and trip planning. Furthermore, this study has delineated a novel technical avenue for the advancement of personalized tourism services. © 2025 Slovene Society Informatika. All rights reserved.

关键词： optimization algorithms

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On-board hydrogen-rich syngas production via waste heat recovery from compression-ignition engines: maximizing hydrogen content with novel multi-objective algorithms

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International Journal of Hydrogen Energy 2025年 130卷 411-422页

作者： Ağbulut, Ümit Vozka, Petr Bakır, Hüseyin Brieu, Nathan A. Polat, Fikret Sarıdemir, Suat Department of Chemistry and Biochemistry California State University Los Angeles State University Drive 5151 Los AngelesCA90032 United States Department of Mechanical Engineering Mechanical Engineering Faculty Yildiz Technical University Istanbul34349 Turkey Department of Technical Sciences Western Caspian University Baku Azerbaijan Department of Electronics and Automation Vocational School Dogus University Istanbul34775 Turkey Department of Mechanical Engineering Faculty of Engineering Düzce University Düzce81620 Turkey

A significant portion of fuel energy in internal combustion engines is lost as waste heat, yet limited efforts have been made to recover it effectively. This research explores the utilization of exhaust heat from a diesel engine to produce H2-rich syngas through the methanol-steam reforming (MSR) process. The engine operates at varying loads (15, 30, 45, and 60 Nm) while maintaining a constant speed of 2000 rpm. Exhaust heat is redirected to an MSR reactor, where the methanol-to-water (MtW) molar ratio is adjusted (0.5, 1, 1.5, and 2). Results reveal that the highest hydrogen content in syngas (70.3 %) is achieved at an engine load of 30 Nm and an MtW ratio of 1. To further optimize hydrogen production, three novel algorithms (DSC-MOPSO, MOSPO, and MOGWO) are applied to key operation parameters. optimization increases hydrogen content to 72.5 % with DSC-MOPSO, 72.4 % with MOSPO, and 72.1 % with MOGWO, with error margins below 0.7 %. © 2025 The Authors

关键词： Hydrogen-rich gas Methanol-steam reforming Waste heat recovery optimization algorithms

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Addressing Phishing Threats Using A Metaheuristic Perspective On Machine Learning Classification Models Code

JOURNAL OF APPLIED SCIENCE AND ENGINEERING

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JOURNAL OF APPLIED SCIENCE AND ENGINEERING 2025年第7期28卷 1503-1514页

作者： Hu, Bo Zhang, Sainan Nanjing Normal Univ Special Educ Ctr Informat Construct & Management Nanjing 210038 Peoples R China

Web phishing attacks have emerged as a significant threat to online security, enabling phishers to steal sensitive financial information and commit fraud. To combat this, many anti-phishing systems have been developed, focusing on detecting phishing content in online communications. This study introduces novel approaches to enhance phishing detection by employing machine learning techniques. Specifically, three different single models were analyzed: Random Forest Classifier (RFC), Adaptive Boosting Classification (ADAC), and Na & iuml;ve Bayes Classification Algorithm (NBC). These models were optimized using Artificial Rabbits optimization (ARO), resulting in hybrid models RFAR, NBAR, and ADAR. The results of the models' analysis indicate that the RFAR hybrid model performs better than the other single models and their optimized models. The RFAR model achieved precision scores of 0.950 for phishing websites, 0.954 for suspicious websites, and 0.872 for legitimate websites, with corresponding recall values of 0.929, 0.954, and 0.990, respectively. In comparison, the ADAR model was notably effective in classifying legitimate websites with a precision score of 0.896. The study's novelty lies in integrating ARO with traditional classifiers to create hybrid models that improve classification accuracy.

关键词： Phishing Cyber Attacks Classification Data Mining optimization algorithms Phishing Websites Prediction Artificial Intelligence

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Estimation of daily reference evapotranspiration based on an improved Slime Mould Algorithm (SMA) for the Hetao irrigation district in northwest China

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THEORETICAL AND APPLIED CLIMATOLOGY 2025年第2期156卷 1-15页

作者： He, Huaijie Wang, Zhengqian Wang, Zhenchen Liu, Desheng Ouyang, Li Zhao, Mingzhi He, Lei Ma, Pingnan Powerchina Huadong Engn Corp Ltd Hangzhou 311122 Peoples R China Hohai Univ Coll Harbour Coastal & Offshore Engn Nanjing 210098 Peoples R China

The Hetao Irrigation District (HID) is one of the three major irrigation districts in China, and the accurate estimation of the reference crop evapotranspiration (ETo) for effective water resource allocation and crop irrigation planning. In this study, the slime mould algorithm (SMA) was improved (named ISMA) by incorporating good point set initialization and reverse differential evolution methods. Daily meteorological data from five stations in the HID (2000-2014) were used to train and validate the ISMA model for ETo estimation. ISMA's optimization performance was benchmarked against SMA, particle swarm optimization (PSO), salp swarm algorithm (SSA), and honey badger algorithm (HBA) using 23 test functions, with results demonstrating ISMA's advantages in fast convergence, stability, and robustness. Six combinations of meteorological parameters (C1-C6) were evaluated, with the C6 combination (Tmax, Tmean, Tmin, RH, Rs, u2) achieving the best performance at all five stations, including lower MAE (0.085-0.098 mm d-1), MSE (0.015-0.019), RMSE (0.019-0.134 mm d-1), MAPE (4.14-5.11%), and the highest R2 (0.998). Additionally, the C4 combination (Tmax, Tmean, RH, Rs) also provided satisfactory estimation accuracy. The results highlighted the critical role of solar radiation as a key input for ETo modeling in HID. In conclusion, ISMA demonstrated high accuracy and adaptability in estimating daily ETo with limited meteorological data, offering valuable data support for water resource management and promoting the development of precision agriculture in the HID.

关键词： Reference evapotranspiration Metaheuristics algorithms (MAs) optimization algorithms Precision agriculture Solar radiation

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Feature Generation with Genetic algorithms for Imagined Speech Electroencephalogram Signal Classification

ENG

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ENG 2025年第4期6卷 75-75页

作者： Lara-Arellano, Edgar Takacs, Andras Tovar-Arriaga, Saul Rodriguez-Resendiz, Juvenal Univ Autonoma Queretaro Fac Ingn Queretaro 76010 Mexico

This work presents a method for classifying EEG (Electroencephalogram) signals generated when a person concentrates on specific words, defined as "Imagined Speech". Imagined speech is essential to enhance problem-solving, memory, and language development. In addition, imagined speech is beneficial because of its applications in therapy fields like managing anxiety or improving communication skills. EEG measures the electrical activity of the brain. EEG signal classification is difficult as the machine learning (ML) algorithm has to learn how to categorize the signal linked to the imagined word. This work proposes a novel method to generate a specific feature vector to achieve classification with superior accuracy results to those found in the state of the art. The method leverages a genetic algorithm to create an optimal feature combination for the classification task and machine learning model. This algorithm can efficiently explore ample feature space and identify the most relevant features for the task. The proposed method achieved an accuracy of 96% using eight electrodes for EEG signal recordings.

关键词： imagined speech EEG feature extraction genetic algorithm classification optimization algorithms signal processing biomedical

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Advanced swarm intelligence algorithms in quantum circuit design

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2025年第1期40卷 1-20页

作者： Horackova, Libuse Zelinka, Ivan VSB Tech Univ Ostrava IT4Innovat 17. listopadu 2172-15 Ostrava 708 00 Czech Republic VSB Tech Univ Ostrava Quantum Comp Lab IT4Innovat Ostrava Czech Republic VSB Tech Univ Ostrava Dept Comp Sci Ostrava Czech Republic

This study explores the potential of employing algorithms like iSOMA, Differential Evolution, Particle Swarm optimization, Grey Wolf optimization, and Ant Colony optimization for the design of quantum computing circuits. Utilizing the Qiskit environment, the research involved simulating a straightforward quantum circuit with variable parameters. To substantiate the effectiveness of these algorithms, three distinct experimental setups were conducted under varying conditions and degrees of freedom. The findings reveal that these algorithms are not only suitable for simulations but also excel in identifying solutions that conserve qubits. A comparative analysis of the methods was performed using the Friedman test, followed by the Nemenyi post-hoc test to evaluate their relative performance.

关键词： Quantum computation quantum circuit Qiskit optimization algorithms

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Pareto equilibrium solution algorithm based on the metrics planning method and its application to multi-objective aerospace optimization

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AIP ADVANCES 2025年第3期15卷

作者： Fang, Ziqi Tang, Zhili Nanjing Univ Aeronaut & Astronaut Coll Aerosp Engn Nanjing 210016 Peoples R China

In high-dimensional multi-objective optimization problems, there are many Pareto solutions obtained by using high-dimensional multi-objective optimization algorithms, but there is only one meaningful optimal solution for the engineering problem;to find the unique solution, this paper develops a high-dimensional Pareto equilibrium solution algorithm based on the metrics planning method. First, the optimization problem of high-dimensional objectives is integrated into a single-objective problem based on the reference point according to the metrics planning method. Then, the weighting values of each objective function in the integrated single-objective problem are calculated using the weight allocation method. Finally, optimization experiments on the test functions show that the approach can effectively find the ideal optimal solution located in the set of Pareto solutions of the high-dimensional multi-objective optimization problem. This paper further applies the algorithm to the multi-objective optimization design of a liquid rocket engine injector and obtains the ideal Pareto optimal solution, which indicates that the high-dimensional Pareto equilibrium solution algorithm based on metrics planning in this paper is an effective method to help engineers find the meaningful solution from the numerous solutions, and it is promising in multi-objective aerospace optimization problems.

关键词： optimization algorithms

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Optimizing Inventory Costs in Manufacturing Systems Using Trapezoidal and Pentagonal Fuzzy Numbers

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JOURNAL OF MATHEMATICS 2025年第1期2025卷

作者： Dhandapani, Prasantha Bharathi Kalaichelvan, Kalaiarasi Ramalingam, Soundaria Biomy, Mohamed Radwan, Taha Sri Eshwar Coll Engn Dept Math Coimbatore 641202 Tamil Nadu India Bharathidasan Univ Cauvery Coll Women Autonomous PG & Res Dept Math Tiruchirappalli 620018 Tamil Nadu India Srinivas Univ Dept Math Mangalore 574146 India Qassim Univ Coll Business & Econ Dept Management Informat Syst Buraydah 51452 Saudi Arabia

Every manufacturing industry strives to always provide impeccable goods. Due to machine failures, labor issues, etc., this is practically unachievable in real-world situations during the manufacturing run time. As a result, things of subpar quality are produced by the equipment systems. The inferior-quality products are improved at a cost to make them better, and then they are prepared for sale. The nonlinear programming Lagrangian method is used to determine the best solution, which affects the average monthly cost. In the suggested model, the graded mean integration representation method is used to describe defuzzification while trapezoidal and pentagonal fuzzy numbers are used to calculate the optimal cost though there are different types of fuzzy numbers available that are used to test the optimality. The main aim of the paper is to compare the trapezoidal and pentagonal fuzzy numbers to test the optimal total cost. As a result, the trapezoidal fuzzy number gives an accurate result in all cases, while in the pentagonal fuzzy number, there is a slight deviation in the fuzzy case. So when we go with a higher-order fuzzy number, the accuracy of the optimal total cost changes. Finally, a graphic comparison using MATLAB is carried out for the two fuzzy numbers and the best out of them is found.

关键词： decision making fuzzy logics inventory model optimization algorithms

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An Improved Branch-and-bound Algorithm for Sum-of-linear-ratios Problem

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IAENG International Journal of Applied Mathematics 2025年第2期55卷 369-377页

作者： Ding, Xianfeng Mei, Hanbing Wen, Pengfei Yi, Xiaolin Qin, Yiyu Luo, Qianmei School of Science Southwest Petroleum University Chengdu610500 China

This paper presents a fresh global optimization algorithm specifcally designed for the sum-of-linear-ratios problem. Initially, auxiliary variables are introduced to reformulate the original problem into an equivalent form, and the equivalence of the optimal solution in this new formulation is established. Additionally, the linear relaxation technique is applied to simplify the objective function and constraint conditions of the equivalent problem, thereby obtaining a linear relaxation programming problem. By resolving a progression of linear programming instances, the upper bound of the original problem undergoes continuous updates, while the linear relaxation problem offers a viable and effcient lower bound for the original problem. Subsequently, an improved branch-and-bound algorithm for tackling the original problem is introduced, incorporating hybrid division rules. The convergence of this algorithm is demonstrated. Ultimately, numerical experiments validate the feasibility and effcacy of the proposed algorithm. © (2024), (International Association of Engineers). All rights reserved.

关键词： optimization algorithms

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