Finding p-Hub Median Locations: An Empirical Study on Problems and Solution Techniques by Xiaoqian Sun, Weibin Dai, Yu Zhang, Sebastian Wandelt

“…Classical problems in small networks can be solved efficiently using CPLEX because of their low complexity. Genetic algorithms perform well for solving three types of single allocation problems, since the problem formulations can be neatly encoded with chromosomes of reasonable size. …”
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Sensor and Dynamic Pricing Aware Vertical Transportation in Smart Buildings by Hyunkyoung Choi, Kyungwoon Cho, Hyokyung Bahn

“…As this is a complex optimization problem, we use an evolutionary computation technique based on genetic algorithms (GA). We inject a learning module into the control unit of ECS, which monitors the change of the electricity price and the passengers’ traffic detected by sensors. …”
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Evolutionary Computation and Its Applications in Neural and Fuzzy Systems by Biaobiao Zhang, Yue Wu, Jiabin Lu, K.-L. Du

“…In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. …”
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Improved Clonal Selection Algorithm Based on Biological Forgetting Mechanism by Chao Yang, Bing-qiu Chen, Lin Jia, Hai-yang Wen

“…Compared with the existing clonal selection and genetic algorithms, the experiment and time complexity analysis show that the algorithm has good optimization efficiency and stability.…”
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Modeling the effect of extrusion parameters on density of biomass pellet using artificial neural network by Abedin Zafari, Mohammad Hossein Kianmehr, Rahman Abdolahzadeh

“…Shankar and Bandyopadhyay and Shankar et al. successfully used genetic algorithms and artificial neural networks to understand and optimize an extrusion process. …”
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Evolutionary Computation with Spatial Receding Horizon Control to Minimize Network Coding Resources by Xiao-Bing Hu, Mark S. Leeson

“…The minimization of network coding resources, such as coding nodes and links, is a challenging task, not only because it is a NP-hard problem, but also because the problem scale is huge; for example, networks in real world may have thousands or even millions of nodes and links. Genetic algorithms (GAs) have a good potential of resolving NP-hard problems like the network coding problem (NCP), but as a population-based algorithm, serious scalability and applicability problems are often confronted when GAs are applied to large- or huge-scale systems. …”
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Powertrain Matching and Optimization of Dual-Motor Hybrid Driving System for Electric Vehicle Based on Quantum Genetic Intelligent Algorithm by Yong Wang, Dongye Sun

“…Based on preliminary matches, quantum genetic algorithm was introduced to optimize the matching in the dual-motor hybrid power system. …”
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Application of Soft Computing Techniques for the Analysis of Tractive Properties of a Low-Power Agricultural Tractor under Various Soil Conditions by Katarzyna Pentoś, Krzysztof Pieczarka, Krzysztof Lejman

“…To this end, the performances of soft computing approaches, including neural networks, genetic algorithms, and adaptive network fuzzy inference system, were evaluated. …”
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PSO Based PI Controller Design for a Solar Charger System by Her-Terng Yau, Chih-Jer Lin, Qin-Cheng Liang

“…According to the simulation and experimental results, the control parameters resulted from PSO have better performance than genetic algorithms (GAs).…”
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Feature Extraction and Selection Scheme for Intelligent Engine Fault Diagnosis Based on 2DNMF, Mutual Information, and NSGA-II by Peng-yuan Liu, Bing Li, Cui-e Han, Feng Wang

“…A novel feature extraction and selection scheme is presented for intelligent engine fault diagnosis by utilizing two-dimensional nonnegative matrix factorization (2DNMF), mutual information, and nondominated sorting genetic algorithms II (NSGA-II). Experiments are conducted on an engine test rig, in which eight different engine operating conditions including one normal condition and seven fault conditions are simulated, to evaluate the presented feature extraction and selection scheme. …”
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Feature Selection and Parameter Optimization of Support Vector Machines Based on Modified Cat Swarm Optimization by Kuan-Cheng Lin, Yi-Hung Huang, Jason C. Hung, Yung-Tso Lin

“…Previous studies have indicated that CSO algorithms outperform other well-known metaheuristics, such as genetic algorithms and particle swarm optimization. This study presents a modified version of cat swarm optimization (MCSO), capable of improving search efficiency within the problem space. …”
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Toward Enhancing the Energy Efficiency and Minimizing the SLA Violations in Cloud Data Centers by E. I. Elsedimy, Fahad Algarni

“…VMPMOPSO was compared with a simple single-objective algorithm, called First-Fit-Decreasing (FFD), and two multiobjective ant colony and genetic algorithms. Two simulation experiments were conducted to verify the effectiveness and efficiency of the proposed VMPMOPSO. …”
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Optimizing cut order planning: A comparative study of heuristics, metaheuristics, and MILP algorithms by Sharif Al-Mahmud, Jose Alejandro Cano, Emiro Antonio Campo, Stephan Weyers

“…Modifications to existing heuristics, combined with tournament selection in genetic algorithms (GA), improve solution quality and efficiency. …”
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Non-linear multi-objective optimization model of production planning based on fuzzy logic and machine learning by Zahra Saeidi Mobarakeh, Hossein Amoozadkhalili

“…This model uses the combination of non-dominant fourth sorting genetic algorithms (NSGA-IV) and variable selection network (VSN) in a hybrid framework and provides an advanced and multi-faceted approach to solving complex multi-objective optimization problems. …”
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Evaluation of novel NaOH/activated carbon/zeolite biocomposite as an efficient adsorbent for oilfield produced water treatment by Eghe Amenze Oyedoh, Onyedikachi Praise Igbokwe

“…The adsorption process was optimized using Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) integrated with genetic algorithms. The results indicated a hydrocarbon removal efficiency of 99.86 % with RSM and 99.99 % with ANN under optimal conditions. …”
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Optimal capacity planning with economic emission considerations in isolated solar-wind-diesel microgrid using combined arithmetic-golden jackal optimization by Sujoy Barua, Adel Merabet, Ahmed Al-Durra, Tarek El Fouly, Ehab F. El-Saadany

“…The results demonstrate significant cost savings using the solar-wind-diesel microgrid under the proposed combined optimization compared to the arithmetic optimization algorithm and golden jackal algorithm and conventional metaheuristic optimization based on genetic algorithms.…”
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A Survey of Nature-Inspired Meta-Heuristic Algorithms in Network Alignment by Anagh Awal, Farsuram Pandit

“…It explores a wide range of nature-inspired algorithms, including genetic algorithms, particle swarm optimization, ant colony optimization, and simulated annealing. …”
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Control of Discrete Event Systems by Means of Discrete Optimization and Disjunctive Colored PNs: Application to Manufacturing Facilities by Juan-Ignacio Latorre-Biel, Emilio Jiménez-Macías, Mercedes Pérez de la Parte, Julio Blanco-Fernández, Eduardo Martínez-Cámara

“…Furthermore, the metaheuristic of genetic algorithms is applied to the search of the best solutions in the solution space. …”
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Optimization and loss estimation in energy-deficient polygeneration systems: A case study of Pakistan's utilities with integrated renewable energy by Muhammad Shoaib Saleem, Naeem Abas

“…Techniques used for electricity demand forecasting encompass artificial intelligence, artificial neural networks, trend line extrapolations, fuzzy logic, vector support machines, genetic algorithms and expert systems. Demand forecasting becomes even more difficult in polygeneration utilities with renewable energy sources integrated to meet the varying demands. …”
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Introducing a Markov Chain-Based Energy Awareness Approach for Cloud Data Center Virtual Machine Dynamic Management by Xinbin Huang, Yu Huang, Cecılıa. Mercado

“…The approach integrates genetic algorithms and refrigeration simulation into the migration replacement process, leveraging absorbing Markov chains for predictive analysis. …”
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