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Journal articles on the topic 'Population-based algorithm'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Tang, Ke, Fei Peng, Guoliang Chen, and Xin Yao. "Population-based Algorithm Portfolios with automated constituent algorithms selection." Information Sciences 279 (September 2014): 94–104. http://dx.doi.org/10.1016/j.ins.2014.03.105.

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2

M., M. H. Elroby, F. Mekhamer S., E. A. Talaat H., and A. Moustafa. Hassan M. "Population based optimization algorithms improvement using the predictive particles." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 3261–74. https://doi.org/10.11591/ijece.v10i3.pp3261-3274.

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A new efficient improvement, called Predictive Particle Modification (PPM),is proposed in this paper. This modification makes the particle look tothe near area before moving toward the best solution of the group.This modification can be applied to any population algorithm. The basic philosophy of PPM is explained in detail. To evaluate the performance ofPPM, it is applied to Particle Swarm Optimization (PSO) algorithm and Teaching Learning Based Optimization (TLBO) algorithm then tested using23 standard benchmark functions. The effectiveness of these modificationsare compared with the other un

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3

Folly, Komla A. "An Improved Population-Based Incremental Learning Algorithm." International Journal of Swarm Intelligence Research 4, no. 1 (2013): 35–61. http://dx.doi.org/10.4018/jsir.2013010102.

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Population-Based Incremental Learning (PBIL) is a relatively new class of Evolutionary Algorithms (EA) that has been recently applied to a range of optimization problems in engineering with promising results. PBIL combines aspects of Genetic Algorithm with competitive learning. The learning rate in the standard PBIL is generally fixed which makes it difficult for the algorithm to explore the search space effectively. In this paper, a PBIL with adapting learning rate is proposed. The Adaptive PBIL (APBIL) is able to thoroughly explore the search space at the start of the run and maintain the di

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4

Poudel, Yam, Jeewan Phuyal, and Rajiv Kumar. "Comprehensive Study of Population Based Algorithms." American Journal of Computer Science and Technology 7, no. 4 (2024): 195–217. https://doi.org/10.11648/j.ajcst.20240704.17.

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The exponential growth of industrial enterprise has highly increased the demand for effective and efficient optimization solutions. Which is resulting to the broad use of meta heuristic algorithms. This study explores eminent bio-inspired population based optimization techniques, including Particle Swarm Optimization (PSO), Spider Monkey Optimization (SMO), Grey Wolf Optimization (GWO), Cuckoo Search Optimization (CSO), Grasshopper Optimization Algorithm (GOA), and Ant Colony Optimization (ACO). These methods which are inspired by natural and biological phenomena, offer revolutionary problems

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Wang, Pin, Yongming Li, Bohan Chen, et al. "Proportional Hybrid Mechanism for Population Based Feature Selection Algorithm." International Journal of Information Technology & Decision Making 16, no. 05 (2017): 1309–38. http://dx.doi.org/10.1142/s0219622014500096.

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Feature selection is an important research field for pattern classification, data mining, etc. Population-based optimization algorithms (POA) have high parallelism and are widely used as search algorithm for feature selection. Population-based feature selection algorithms (PFSA) involve compromise between precision and time cost. In order to optimize the PFSA, the feature selection models need to be improved. Feature selection algorithms broadly fall into two categories: the filter model and the wrapper model. The filter model is fast but less precise; while the wrapper model is more precise b

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6

Chen, Chang Huang. "Fusing Multiple Strategies in Population-Based Optimization Algorithm." Applied Mechanics and Materials 764-765 (May 2015): 1407–11. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.1407.

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A multi-strategy based population optimization, referred to MSPO, is proposed in this paper. The algorithm is developed by hybridizing four different population-based algorithms, bare bone particle swarm optimization, quantum-behaved particle swarm optimization, differential evolution and opposition-based learning. It aims at enhancing the exploration and exploitation capability of population based algorithm for general optimization problem. These four options are randomly selected with equal probability during the search process. The proposed algorithm is validated against test functions and

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Liu, Zijian, Chunbo Luo, Peng Ren, Tingwei Wang, and Geyong Min. "Population based optimization via differential evolution and adaptive fractional gradient descent." Filomat 34, no. 15 (2020): 5173–85. http://dx.doi.org/10.2298/fil2015173l.

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We propose a differential evolution algorithm based on adaptive fractional gradient descent (DE-FGD) to address the defects of existing bio-inspired algorithms, such as slow convergence speed and local optimum. The crossover and selection processes of the differential evolution algorithm are discarded and the adaptive fractional gradients are adopted to enhance the global searching capability. For the benchmark functions, our proposed algorithm Specifically, our method has higher searching accuracy than several state of the art bio-inspired algorithms. Furthermore, we apply our method to speci

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8

Wang, Jun Wei, and Jing Hao. "Population Adaptive Immune Algorithm Based Trustworthy QoS Routing Algorithm." Applied Mechanics and Materials 411-414 (September 2013): 647–52. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.647.

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The trustworthiness of data transmission is a very important parameter in the future network. In this paper, considering the optimal ability of population adaptive immune and the trustworthiness demand of the network, a trustworthy QoS routing algorithm is proposed. The interval is used to describe the user requirement in order to adapt to the fuzziness of the user QoS and trust demand, and the sliding window is adopted to implement the trust evaluation and control mechanism. With satisfaction degree function introduced, it tries to find the optimal path which satisfies the user requirement ba

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9

FENG, Yanhong, Jianqin LIU, and Yichao HE. "Chaos-based dynamic population firefly algorithm." Journal of Computer Applications 33, no. 3 (2013): 796–99. http://dx.doi.org/10.3724/sp.j.1087.2013.00796.

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10

Feng, Xiaoqin, Rong Xie, Junyang Sheng, and Shuo Zhang. "Population Statistics Algorithm Based on MobileNet." Journal of Physics: Conference Series 1237 (June 2019): 022045. http://dx.doi.org/10.1088/1742-6596/1237/2/022045.

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11

Elroby, M. M. H., S. F. Mekhamer, H. E. A. Talaat, and M. A. Moustafa Hassan. "Population based optimization algorithms improvement using the predictive particles." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (2020): 3261. http://dx.doi.org/10.11591/ijece.v10i3.pp3261-3274.

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A new efficient improvement, called Predictive Particle Modification (PPM), is proposed in this paper. This modification makes the particle look to the near area before moving toward the best solution of the group. This modification can be applied to any population algorithm. The basic philosophy of PPM is explained in detail. To evaluate the performance of PPM, it is applied to Particle Swarm Optimization (PSO) algorithm and Teaching Learning Based Optimization (TLBO) algorithm then tested using 23 standard benchmark functions. The effectiveness of these modifications are compared with the ot

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12

Zhu, Yunxiang, Fengting Yan, Jeng-Shyang Pan, et al. "Mutigroup-Based Phasmatodea Population Evolution Algorithm with Mutistrategy for IoT Electric Bus Scheduling." Wireless Communications and Mobile Computing 2022 (January 31, 2022): 1–16. http://dx.doi.org/10.1155/2022/1500646.

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The Phasmatodea population evolution algorithm (PPE) is a novel metaheuristic algorithm proposed in recent years, which simulates the evolutionary trend of stick insect population. In this article, a multigroup-based Phasmatodea population evolution algorithm with mutistrategy (MPPE) is proposed to further improve the overall performance of PPE. During the initialization period, the stick insect population is divided into multiple groups, and the step factor of the flower pollen algorithm is introduced into the population growth model of no more than half of the groups. This makes the populati

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13

Huang, Yawei, Xuezhong Qian, and Wei Song. "Improving Dual-Population Differential Evolution Based on Hierarchical Mutation and Selection Strategy." Electronics 13, no. 1 (2023): 62. http://dx.doi.org/10.3390/electronics13010062.

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The dual-population differential evolution (DDE) algorithm is an optimization technique that simultaneously maintains two populations to balance global and local search. It has been demonstrated to outperform single-population differential evolution algorithms. However, existing improvements to dual-population differential evolution algorithms often overlook the importance of selecting appropriate mutation and selection operators to enhance algorithm performance. In this paper, we propose a dual-population differential evolution (DPDE) algorithm based on a hierarchical mutation and selection s

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14

Łapa, Krystian, Krzysztof Cpałka, Marek Kisiel-Dorohinicki, Józef Paszkowski, Maciej Dębski, and Van-Hung Le. "Multi-Population-Based Algorithm with an Exchange of Training Plans Based on Population Evaluation." Journal of Artificial Intelligence and Soft Computing Research 12, no. 4 (2022): 239–53. http://dx.doi.org/10.2478/jaiscr-2022-0016.

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Abstract Population Based Algorithms (PBAs) are excellent search tools that allow searching space of parameters defined by problems under consideration. They are especially useful when it is difficult to define a differentiable evaluation criterion. This applies, for example, to problems that are a combination of continuous and discrete (combinatorial) problems. In such problems, it is often necessary to select a certain structure of the solution (e.g. a neural network or other systems with a structure usually selected by the trial and error method) and to determine the parameters of such stru

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Tejani, Ghanshyam, Vimal Savsani, and Vivek Patel. "Modified Sub-Population Based Heat Transfer Search Algorithm for Structural Optimization." International Journal of Applied Metaheuristic Computing 8, no. 3 (2017): 1–23. http://dx.doi.org/10.4018/ijamc.2017070101.

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In this study, a modified heat transfer search (MHTS) algorithm is proposed by incorporating sub-population based simultaneous heat transfer modes viz. conduction, convection, and radiation in the basic HTS algorithm. However, the basic HTS algorithm considers only one of the modes of heat transfer for each generation. The multiple natural frequency constraints in truss optimization problems can improve the dynamic behavior of the structure and prevent undesirable vibrations. However, shape and size variables subjected to frequency constraints are difficult to handle due to the complexity of i

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16

Li, Xiaoyu, Lei Wang, Qiaoyong Jiang, and Qingzheng Xu. "An adaptive multitasking optimization algorithm based on population distribution." Mathematical Biosciences and Engineering 21, no. 2 (2024): 2432–57. http://dx.doi.org/10.3934/mbe.2024107.

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<abstract> <p>Evolutionary multitasking optimization (EMTO) handles multiple tasks simultaneously by transferring and sharing valuable knowledge from other relevant tasks. How to effectively identify transferred knowledge and reduce negative knowledge transfer are two key issues in EMTO. Many existing EMTO algorithms treat the elite solutions in tasks as transferred knowledge between tasks. However, these algorithms may not be effective enough when the global optimums of the tasks are far apart. In this paper, we study an adaptive evolutionary multitasking optimization algorithm ba

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17

Kenekayoro, Patrick, Promise Mebine, and Bodouowei Godswill Zipamone. "Population Based Techniques for Solving the Student Project Allocation Problem." International Journal of Applied Metaheuristic Computing 11, no. 2 (2020): 192–207. http://dx.doi.org/10.4018/ijamc.2020040110.

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The student project allocation problem is a well-known constraint satisfaction problem that involves assigning students to projects or supervisors based on a number of criteria. This study investigates the use of population-based strategies inspired from physical phenomena (gravitational search algorithm), evolutionary strategies (genetic algorithm), and swarm intelligence (ant colony optimization) to solve the Student Project Allocation problem for a case study from a real university. A population of solutions to the Student Project Allocation problem is represented as lists of integers, and

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18

Lei, Jiaxing, Yaosong Guo, Dashi Luo, Zhongyuan Xu, and Rui Wang. "Fault Location of Distribution Network Based on Multi-population Particle Swarm Optimization Algorithm." Journal of Physics: Conference Series 2360, no. 1 (2022): 012024. http://dx.doi.org/10.1088/1742-6596/2360/1/012024.

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Fault location of the distribution network is an important direction in the construction of distribution automation. For the problem of slow convergence of intelligent optimization algorithms and easy to fall into local optimality, the multi-population particle swarm optimization algorithm is proposed. The algorithm is compared with single population particle swarm algorithm on IEEE69 node model, it is proved that the new algorithm can find fault location faster. Then the effectiveness of the algorithm in a variety of distribution network fault location scenarios is verified, including single

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19

Zhao, Lei, Zhicheng Jia, Lei Chen, and Yanju Guo. "Improved Backtracking Search Algorithm Based on Population Control Factor and Optimal Learning Strategy." Mathematical Problems in Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/3017608.

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Backtracking search algorithm (BSA) is a relatively new evolutionary algorithm, which has a good optimization performance just like other population-based algorithms. However, there is also an insufficiency in BSA regarding its convergence speed and convergence precision. For solving the problem shown in BSA, this article proposes an improved BSA named COBSA. Enlightened by particle swarm optimization (PSO) algorithm, population control factor is added to the variation equation aiming to improve the convergence speed of BSA, so as to make algorithm have a better ability of escaping the local o

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20

Fei Peng, Ke Tang, Guoliang Chen, and Xin Yao. "Population-Based Algorithm Portfolios for Numerical Optimization." IEEE Transactions on Evolutionary Computation 14, no. 5 (2010): 782–800. http://dx.doi.org/10.1109/tevc.2010.2040183.

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21

Omran, Mahamed G., and Andries Engelbrecht. "Time Complexity of Population-Based Metaheuristics." MENDEL 29, no. 2 (2023): 255–60. http://dx.doi.org/10.13164/mendel.2023.2.255.

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This paper is a brief guide aimed at evaluating the time complexity of metaheuristic algorithms both mathematically and empirically. Starting with the mathematical foundational principles of time complexity analysis, key notations and fundamental concepts necessary for computing the time efficiency of a metaheuristic are introduced. The paper then applies these principles on three well-known metaheuristics, i.e. differential evolution, harmony search and the firefly algorithm. A procedure for the empirical analysis of metaheuristics' time efficiency is then presented. The procedure is then use

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22

Liu, Jin Yang, Xing Wei Wang, and Min Huang. "A Trustworthy QoS Unicast Routing Scheme Based on Population Adaptive Based Immune Algorithm." Applied Mechanics and Materials 602-605 (August 2014): 3084–87. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.3084.

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In this paper, a trustworthy QoS unicast routing scheme based on trustworthy network is proposed. First, a cognitive network model is designed, referring to the intelligence of artificial immune system. Then, trustworthiness of each node is evaluated by deploying sliding window mechanism and analyzing the behavior record of each node. Finally, a population adaptive based immune of trustworthy QoS unicast routing algorithm is proposed, referring to the adaptive process of antibody identifying antigen. We verify the effectiveness of our scheme by using large-scale computer emulation experiments

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23

Liao, Xin, and Khoi D. Hoang. "A Population-Based Search Approach to Solve Continuous Distributed Constraint Optimization Problems." Applied Sciences 14, no. 3 (2024): 1290. http://dx.doi.org/10.3390/app14031290.

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Distributed Constraint Optimization Problems (DCOPs) are an efficient framework widely used in multi-agent collaborative modeling. The traditional DCOP framework assumes that variables are discrete and constraint utilities are represented in tabular forms. However, the variables are continuous and constraint utilities are in functional forms in many practical applications. To overcome this limitation, researchers have proposed Continuous DCOPs (C-DCOPs), which can model DCOPs with continuous variables. However, most of the existing C-DCOP algorithms rely on gradient information for optimizatio

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24

Gallagher, Marcus, and Marcus Frean. "Population-Based Continuous Optimization, Probabilistic Modelling and Mean Shift." Evolutionary Computation 13, no. 1 (2005): 29–42. http://dx.doi.org/10.1162/1063656053583478.

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Evolutionary algorithms perform optimization using a population of sample solution points. An interesting development has been to view population-based optimization as the process of evolving an explicit, probabilistic model of the search space. This paper investigates a formal basis for continuous, population-based optimization in terms of a stochastic gradient descent on the Kullback-Leibler divergence between the model probability density and the objective function, represented as an unknown density of assumed form. This leads to an update rule that is related and compared with previous the

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25

Kim, Yong-Hyuk, Zong Woo Geem, and Yourim Yoon. "Population-Based Redundancy Control in Genetic Algorithms: Enhancing Max-Cut Optimization." Mathematics 13, no. 9 (2025): 1409. https://doi.org/10.3390/math13091409.

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The max-cut problem is a well-known topic in combinatorial optimization, with a wide range of practical applications. Given its NP-hard nature, heuristic approaches—such as genetic algorithms, tabu search, and harmony search—have been extensively employed. Recent research has demonstrated that harmony search can outperform genetic algorithms by effectively avoiding redundant searches, a strategy similar to tabu search. In this study, we propose a modified genetic algorithm that integrates tabu search to enhance solution quality. By preventing repeated exploration of previously visited solution

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26

Devika., K., and Jeyakumar G. "Theoretical Analysis and Empirical Comparison of Different Population Initialization Techniques for Evolutionary Algorithms." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 1 (2018): 87–94. https://doi.org/10.11591/ijeecs.v12.i1.pp87-94.

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Evolutionary Algorithms (EAs) are the potential tools for solving optimization problems. The EAs are the population based algorithms and they search for the optimal solution(s) from a initial set of candidates solutions known as population. This population is to be initialized at first before the evolution of the algorithm starts. There exists different ways to initialize this population. Understanding and choosing the right population initialization technique for the given problem is a difficult task for the researchers and problem solvers. To alleviate this issue, this paper is framed with t

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Konieczka, Maria, Alicja Poturała, Jarosław Arabas, and Stanisław Kozdrowski. "A Modification of the PBIL Algorithm Inspired by the CMA-ES Algorithm in Discrete Knapsack Problem." Applied Sciences 11, no. 19 (2021): 9136. http://dx.doi.org/10.3390/app11199136.

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The subject of this paper is the comparison of two algorithms belonging to the class of evolutionary algorithms. The first one is the well-known Population-Based Incremental Learning (PBIL) algorithm, while the second one, proposed by us, is a modification of it and based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. In the proposed Covariance Matrix Adaptation Population-Based Incremental Learning (CMA-PBIL) algorithm, the probability distribution of population is described by two parameters: the covariance matrix and the probability vector. The comparison of algo

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Rodzin, S. I., and O. N. Rodzina. "RECOMMENDER SYSTEMS: PREDICTING WITH MACHINE LEARNING BASED ON POPULATION-BASED ALGORITHM." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 187 (2020): 48–56. http://dx.doi.org/10.14489/vkit.2020.01.pp.048-056.

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The article considers the formulation of the forecasting problem as well as such problems of recommender systems as data sparsity, cold start, scalability, synonymy, fraud, diversity, white crows. Combining the results of collaborative and content filtering gives us two possibilities. On the one hand, to weigh the results according to the content data. On the other hand, to shift these weights towards collaborative filtering as soon as data about a particular user appears. In turn, this improves the accuracy of the recommendations. The authors propose a hybrid model of a recommender system. Su

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29

Sadeghi, Ali, Sajjad Amiri Doumari, Mohammad Dehghani, Zeinab Montazeri, Pavel Trojovský, and Hamid Jafarabadi Ashtiani. "A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems." Applied Sciences 11, no. 10 (2021): 4382. http://dx.doi.org/10.3390/app11104382.

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Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups

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30

Han, Song, Shanshan Chen, Fengting Yan, Jengshyang Pan, and Yunxiang Zhu. "A Hybrid Parallel Balanced Phasmatodea Population Evolution Algorithm and Its Application in Workshop Material Scheduling." Entropy 25, no. 6 (2023): 848. http://dx.doi.org/10.3390/e25060848.

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The phasmatodea population evolution algorithm (PPE) is a recently proposed meta-heuristic algorithm based on the evolutionary characteristics of the stick insect population. The algorithm simulates the features of convergent evolution, population competition, and population growth in the evolution process of the stick insect population in nature and realizes the above process through the population competition and growth model. Since the algorithm has a slow convergence speed and falls easily into local optimality, in this paper, it is mixed with the equilibrium optimization algorithm to make

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31

Zhang, Yang, Jiacheng Li, and Lei Li. "A Reward Population-Based Differential Genetic Harmony Search Algorithm." Algorithms 15, no. 1 (2022): 23. http://dx.doi.org/10.3390/a15010023.

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To overcome the shortcomings of the harmony search algorithm, such as its slow convergence rate and poor global search ability, a reward population-based differential genetic harmony search algorithm is proposed. In this algorithm, a population is divided into four ordinary sub-populations and one reward sub-population, for each of which the evolution strategy of the differential genetic harmony search is used. After the evolution, the population with the optimal average fitness is combined with the reward population to produce a new reward population. During an experiment, tests were conducte

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32

Reyes, Fernández de Bulnes Darian. "Multi-objective optimization approach based on Minimum Population Search algorithm." GECONTEC: Revista Internacional de Gestión del Conocimiento y la Tecnología 7, no. 2 (2019): 1–19. https://doi.org/10.5281/zenodo.7517649.

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Minimum Population Search is a recently developed metaheuristic for optimization of monoobjective continuous problems, which has proven to be a very effective optimizing large scale and multi-modal problems. One of its key characteristic is the ability to perform an efficient exploration of large dimensional spaces. We assume that this feature may prove useful when optimizing multi-objective problems, thus this paper presents a study of how it can be adapted to a multi-objective approach. We performed experiments and comparisons with five multi-objective selection processes and we test the eff

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Abdul Aziz, Nor Hidayati, Nor Azlina Ab. Aziz, Badaruddin Muhammad, et al. "A Tutorial on Population-based Simulated Kalman Filter." Mekatronika 1, no. 2 (2019): 23–32. http://dx.doi.org/10.15282/mekatronika.v1i2.4894.

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    Simulated Kalman Filter (SKF) is an estimation-based optimization algorithm which is established based on the Kalman filtering framework. Even since the SKF algorithm is introduced in 2015, there is no tutorial been published on SKF. One may find that the equations and flowchart of the algorithm is not easy to understand. Hence, this paper provides a tutorial on SKF algorithm that emphasizes on a numerical example for easy and intuitive explanations. This tutorial would be important to those who work on the fundamentals and applications of SKF as well as to students

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Zuo Jing, 左静, and 巴玉林 Ba Yulin. "Population-Depth Counting Algorithm Based on Multiscale Fusion." Laser & Optoelectronics Progress 57, no. 24 (2020): 241502. http://dx.doi.org/10.3788/lop57.241502.

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Akay, Rustu, Alper Basturk, Adem Kalinli, and Xin Yao. "Parallel population-based algorithm portfolios: An empirical study." Neurocomputing 247 (July 2017): 115–25. http://dx.doi.org/10.1016/j.neucom.2017.03.061.

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Ventresca, Mario, and Hamid R. Tizhoosh. "A diversity maintaining population-based incremental learning algorithm." Information Sciences 178, no. 21 (2008): 4038–56. http://dx.doi.org/10.1016/j.ins.2008.07.005.

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Yanxiang Geng, Liyi Zhang, Jianing Guo, and Yin Jiang. "Adaptive Bat Algorithm Based on Historical Population Control." Automatic Control and Computer Sciences 56, no. 5 (2022): 438–46. http://dx.doi.org/10.3103/s0146411622050042.

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Newman, Timothy R., Rakesh Rajbanshi, Alexander M. Wyglinski, Joseph B. Evans, and Gary J. Minden. "Population Adaptation for Genetic Algorithm-based Cognitive Radios." Mobile Networks and Applications 13, no. 5 (2008): 442–51. http://dx.doi.org/10.1007/s11036-008-0079-8.

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Dehghani, Mohammad, Zeinab Montazeri, and Štěpán Hubálovský. "GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems." Mathematics 9, no. 11 (2021): 1190. http://dx.doi.org/10.3390/math9111190.

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There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimiz

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Zhang, Yingxin, Gaige Wang, and Hongmei Wang. "NSGA-II/SDR-OLS: A Novel Large-Scale Many-Objective Optimization Method Using Opposition-Based Learning and Local Search." Mathematics 11, no. 8 (2023): 1911. http://dx.doi.org/10.3390/math11081911.

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Recently, many-objective optimization problems (MaOPs) have become a hot issue of interest in academia and industry, and many more many-objective evolutionary algorithms (MaOEAs) have been proposed. NSGA-II/SDR (NSGA-II with a strengthened dominance relation) is an improved NSGA-II, created by replacing the traditional Pareto dominance relation with a new dominance relation, termed SDR, which is better than the original algorithm in solving small-scale MaOPs with few decision variables, but performs poorly in large-scale MaOPs. To address these problems, we added the following improvements to

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Li, Yu, and Yan Zhang. "A Reinforcement Learning-Based Bi-Population Nutcracker Optimizer for Global Optimization." Biomimetics 9, no. 10 (2024): 596. http://dx.doi.org/10.3390/biomimetics9100596.

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The nutcracker optimizer algorithm (NOA) is a metaheuristic method proposed in recent years. This algorithm simulates the behavior of nutcrackers searching and storing food in nature to solve the optimization problem. However, the traditional NOA struggles to balance global exploration and local exploitation effectively, making it prone to getting trapped in local optima when solving complex problems. To address these shortcomings, this study proposes a reinforcement learning-based bi-population nutcracker optimizer algorithm called RLNOA. In the RLNOA, a bi-population mechanism is introduced

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Qin, Yufang, Junzhong Ji, and Chunnian Liu. "An Entropy-Based Multiobjective Evolutionary Algorithm with an Enhanced Elite Mechanism." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/682372.

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Multiobjective optimization problem (MOP) is an important and challenging topic in the fields of industrial design and scientific research. Multi-objective evolutionary algorithm (MOEA) has proved to be one of the most efficient algorithms solving the multi-objective optimization. In this paper, we propose an entropy-based multi-objective evolutionary algorithm with an enhanced elite mechanism (E-MOEA), which improves the convergence and diversity of solution set in MOPs effectively. In this algorithm, an enhanced elite mechanism is applied to guide the direction of the evolution of the popula

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Li, Kangshun, Fahui Gu, Wei Li, and Ying Huang. "A Dual-Population Evolutionary Algorithm Adapting to Complementary Evolutionary Strategy." International Journal of Pattern Recognition and Artificial Intelligence 33, no. 01 (2018): 1959004. http://dx.doi.org/10.1142/s0218001419590043.

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Optimization problems widely exist in scientific research and engineering practice, which have been one of the research hotshots and difficulties in intelligent computing. The single swarm intelligence optimization algorithms often show such defects as searching stagnation, low accuracy of convergence, part optimum and poor generalization ability when facing the increasingly sophisticated optimization problems. In the study of multiple population, the choice of evolution strategy often has great influence on the performance of the algorithm, and this paper puts forward a kind of dual-populatio

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Meenachi, Loganathan, and Srinivasan Ramakrishnan. "Random Global and Local Optimal Search Algorithm Based Subset Generation for Diagnosis of Cancer." Current Medical Imaging Formerly Current Medical Imaging Reviews 16, no. 3 (2020): 249–61. http://dx.doi.org/10.2174/1573405614666180720152838.

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Background: Data mining algorithms are extensively used to classify the data, in which prediction of disease using minimal computation time plays a vital role. Objective: The aim of this paper is to develop the classification model from reduced features and instances. Methods: In this paper we proposed four search algorithms for feature selection the first algorithm is Random Global Optimal (RGO) search algorithm for searching the continuous, global optimal subset of features from the random population. The second is Global and Local Optimal (GLO) search algorithm for searching the global and

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Das, Bikash, V. Mukherjee, and Debapriya Das. "Student psychology based optimization algorithm: A new population based optimization algorithm for solving optimization problems." Advances in Engineering Software 146 (August 2020): 102804. http://dx.doi.org/10.1016/j.advengsoft.2020.102804.

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Zalasiński, Marcin, Krystian Łapa, Krzysztof Cpałka, Krzysztof Przybyszewski, and Gary G. Yen. "On-Line Signature Partitioning Using a Population Based Algorithm." Journal of Artificial Intelligence and Soft Computing Research 10, no. 1 (2020): 5–13. http://dx.doi.org/10.2478/jaiscr-2020-0001.

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AbstractThe on-line signature is a biometric attribute which can be used for identity verification. It is a very useful characteristic because it is commonly accepted in societies across the world. However, the verification process using this particular biometric feature is a rather difficult one. Researchers working on identity verification involving the on-line signature might face various problems, including the different discriminative power of signature descriptors, the problem of a large number of descriptors, the problem of descriptor generation, etc. However, population-based algorithm

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Ruchi, Mishra*1 &. Neha Mishra2. "BUTTERFLY CURVE BASED BIOGEOGRAPHY BASED OPTIMIZATION ALGORITHM." Global Journal of Engineering Science and Researches 7, no. 6 (2020): 1–13. https://doi.org/10.5281/zenodo.3885830.

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Biogeography based optimization (BBO) is a population-based evolutionary optimization algorithm inspired by the science of biogeography. To enhance the convergence rate of the algorithm towards the optimal solution a new strategy is introduced named as a butterfly curve based BBO (BFBBO) algorithm. In this strategy, a new phase is introduced in which the rotated butterfly curve equation is incorporated to balance the step size. This proposed algorithm is also tested over 20 benchmark problems. The results are also compared with BBO, gbest inspired biogeography based optimization (GBBO), and pa

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Zhang, Yu, Li Hua Wu, and Zi Qiang Luo. "Catastrophe-Based Antibody Clone Algorithm." Applied Mechanics and Materials 121-126 (October 2011): 4415–20. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.4415.

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In solving complex optimization problems, intelligent optimization algorithms such as immune algorithm show better advantages than traditional optimization algorithms. Most of these immune algorithms, however, have disadvantages in population diversity and preservation of elitist antibodies genes, which will lead to the degenerative phenomenon, the zigzag phenomenon, poor global optimization, and low convergence speed. By introducing the catastrophe factor into the ACAMHC algorithm, we propose a novel catastrophe-based antibody clone algorithm (CACA) to solve the above problems. CACA preserves

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Pang, Xue Liang, and C. S. Lin. "Calibration a Magnetometer Based on Multiple Population Genetic Algorithm." Applied Mechanics and Materials 743 (March 2015): 840–44. http://dx.doi.org/10.4028/www.scientific.net/amm.743.840.

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It is imperative that the magnetometer is properly calibrated for sensor errors caused by the bias, scaling factors and non-orthogonality. It is difficult for the three-axis magnetometer to be used directly to measure the magnetic field magnitude. In this paper, we present a method based on Multiple Genetic Algorithm that can be used to estimate calibration model parameters. The Genetic Algorithm put the parameters of the calibration model as the evolutionary population; According to the fitness, the poor individual is eliminated step by step and the optimal individual is obtained after crossi

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Huang, Kang, Yongquan Zhou, Xiuli Wu, and Qifang Luo. "A Cuckoo Search Algorithm With Elite Opposition-Based Strategy." Journal of Intelligent Systems 25, no. 4 (2016): 567–93. http://dx.doi.org/10.1515/jisys-2015-0041.

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AbstractIn this paper, a cuckoo search (CS) algorithm using elite opposition-based strategy is proposed. The opposite solution of the elite individual in the population is generated by an opposition-based strategy in the proposed algorithm and form an opposite search space by constructing the opposite population that locates inside the dynamic search boundaries, then, the search space of the algorithm is guided to approximate the space in which the global optimum is included by simultaneously evaluating the current population and the opposite one. This approach is helpful to obtain a tradeoff

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