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Journal articles on the topic 'Diversity objective'

Author: Grafiati

Published: 10 September 2021

Last updated: 29 July 2025

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1

Toffolo, Andrea, and Ernesto Benini. "Genetic Diversity as an Objective in Multi-Objective Evolutionary Algorithms." Evolutionary Computation 11, no. 2 (2003): 151–67. http://dx.doi.org/10.1162/106365603766646816.

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A key feature of an efficient and reliable multi-objective evolutionary algorithm is the ability to maintain genetic diversity within a population of solutions. In this paper, we present a new diversity-preserving mechanism, the Genetic Diversity Evaluation Method (GeDEM), which considers a distance-based measure of genetic diversity as a real objective in fitness assignment. This provides a dual selection pressure towards the exploitation of current non-dominated solutions and the exploration of the search space. We also introduce a new multi-objective evolutionary algorithm, the Genetic Dive

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Casas-Martínez, Pedro, Alejandra Casado-Ceballos, Jesús Sánchez-Oro, and Eduardo G. Pardo. "Multi-Objective GRASP for Maximizing Diversity." Electronics 10, no. 11 (2021): 1232. http://dx.doi.org/10.3390/electronics10111232.

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This work presents a novel greedy randomized adaptive search procedure approach for dealing with the maximum diversity problem from a multi-objective perspective. In particular, five of the most extended diversity metrics were considered, with the aim of maximizing all of them simultaneously. The metrics considered have been proven to be in conflict, i.e., it is not possible to optimize one metric without deteriorating another one. Therefore, this results in a multi-objective optimization problem where a set of efficient solutions that are diverse with respect to all the metrics at the same ti

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Wang, Handing, Yaochu Jin, and Xin Yao. "Diversity Assessment in Many-Objective Optimization." IEEE Transactions on Cybernetics 47, no. 6 (2017): 1510–22. http://dx.doi.org/10.1109/tcyb.2016.2550502.

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Ceccarelli, Salvatore, and Stefania Grando. "Diversity as a Plant Breeding Objective." Agronomy 14, no. 3 (2024): 550. http://dx.doi.org/10.3390/agronomy14030550.

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This review paper addresses the importance of increasing agrobiodiversity to cope with climate change and, at the same time, providing a sufficient amount of healthy food. This is in agreement with the messages from ecology and medicine indicating the advantages of biodiversity in general and agrobiodiversity in particular for the planet and for our health. Plant breeding is considered to be one of the causes of the decline in agrobiodiversity, and therefore, this paper illustrates alternatives to the commonly used approach based on centralized selection. The first alternative is decentralized

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He, Zhenan, and Gary G. Yen. "Many-Objective Evolutionary Algorithm: Objective Space Reduction and Diversity Improvement." IEEE Transactions on Evolutionary Computation 20, no. 1 (2016): 145–60. http://dx.doi.org/10.1109/tevc.2015.2433266.

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Gong, Dunwei, Yiping Liu, and Gary G. Yen. "A Meta-Objective Approach for Many-Objective Evolutionary Optimization." Evolutionary Computation 28, no. 1 (2020): 1–25. http://dx.doi.org/10.1162/evco_a_00243.

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Pareto-based multi-objective evolutionary algorithms experience grand challenges in solving many-objective optimization problems due to their inability to maintain both convergence and diversity in a high-dimensional objective space. Exiting approaches usually modify the selection criteria to overcome this issue. Different from them, we propose a novel meta-objective (MeO) approach that transforms the many-objective optimization problems in which the new optimization problems become easier to solve by the Pareto-based algorithms. MeO converts a given many-objective optimization problem into a

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Qu, Bo-yang, and Ponnuthurai-Nagaratnam Suganthan. "Multi-objective differential evolution with diversity enhancement." Journal of Zhejiang University SCIENCE C 11, no. 7 (2010): 538–43. http://dx.doi.org/10.1631/jzus.c0910481.

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8

Adra, Salem F., and Peter J. Fleming. "Diversity Management in Evolutionary Many-Objective Optimization." IEEE Transactions on Evolutionary Computation 15, no. 2 (2011): 183–95. http://dx.doi.org/10.1109/tevc.2010.2058117.

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Colmenar, J. M., R. Martí, and A. Duarte. "Heuristics for the Bi-Objective Diversity Problem." Expert Systems with Applications 108 (October 2018): 193–205. http://dx.doi.org/10.1016/j.eswa.2018.05.013.

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Bazargan Lari, Kimia, and Ali Hamzeh. "An evolutionary based framework for many-objective optimization problems." Engineering Computations 35, no. 4 (2018): 1805–28. http://dx.doi.org/10.1108/ec-08-2017-0296.

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Purpose Recently, many-objective optimization evolutionary algorithms have been the main issue for researchers in the multi-objective optimization community. To deal with many-objective problems (typically for four or more objectives) some modern frameworks are proposed which have the potential of achieving the finest non-dominated solutions in many-objective spaces. The effectiveness of these algorithms deteriorates greatly as the problem’s dimension increases. Diversity reduction in the objective space is the main reason of this phenomenon. Design/methodology/approach To properly deal with t

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Chaiyaratana, Nachol, Theera Piroonratana, and Nuntapon Sangkawelert. "Effects of diversity control in single-objective and multi-objective genetic algorithms." Journal of Heuristics 13, no. 1 (2006): 1–34. http://dx.doi.org/10.1007/s10732-006-9003-1.

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12

Harris, Scott R. "What Is Family Diversity? Objective and Interpretive Approaches." Journal of Family Issues 29, no. 11 (2008): 1407–25. http://dx.doi.org/10.1177/0192513x08318841.

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This article differentiates two ways of understanding family diversity— objectively and interpretively. The search for objective diversity is rooted in the assumption that there are many different kinds of families in the United States and around the world; the search for interpretive diversity is rooted in the assumption that any given “family” may be described in different, often contradictory ways. These divergent assumptions can lead relatively objective or interpretive scholars to produce divergent analyses, even as they use seemingly identical concepts to address similar explanatory conc

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13

Pires, Eduardo, José Machado, and Paulo de Moura Oliveira. "Entropy Diversity in Multi-Objective Particle Swarm Optimization." Entropy 15, no. 12 (2013): 5475–91. http://dx.doi.org/10.3390/e15125475.

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Bazargan Lari, Kimia, and Ali Hamzeh. "A diversity control mechanism in many objective optimizations." Applied Intelligence 45, no. 4 (2016): 953–75. http://dx.doi.org/10.1007/s10489-016-0800-4.

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15

Chen, Guoyu, and Junhua Li. "A diversity ranking based evolutionary algorithm for multi-objective and many-objective optimization." Swarm and Evolutionary Computation 48 (August 2019): 274–87. http://dx.doi.org/10.1016/j.swevo.2019.03.009.

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Chen, Wenqing, Jidong Tian, Caoyun Fan, Yitian Li, Hao He, and Yaohui Jin. "Preference-Controlled Multi-Objective Reinforcement Learning for Conditional Text Generation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 12662–72. http://dx.doi.org/10.1609/aaai.v37i11.26490.

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Conditional text generation is to generate text sequences conditioning on linguistic or non-linguistic data. The main line of existing work proposed deterministic models to improve the fidelity of the generated text but often ignored the diversity. Another line relied on conditional variational auto-encoders (CVAEs), which increased the diversity over their deterministic backbones. However, CVAEs regard diversity as an implicit objective and may not be optimal. In this paper, we raise two questions: i) Can diversity be further improved with an explicit objective? ii) Since fidelity and diversi

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Liu, Junhua, Wei Zhang, Mengnan Tian, Hong Ji, and Baobao Liu. "A double association-based evolutionary algorithm for many-objective optimization." Mathematical Biosciences and Engineering 20, no. 9 (2023): 17324–55. http://dx.doi.org/10.3934/mbe.2023771.

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<abstract><p>In this paper, a double association-based evolutionary algorithm (denoted as DAEA) is proposed to solve many-objective optimization problems. In the proposed DAEA, a double association strategy is designed to associate solutions with each subspace. Different from the existing association methods, the double association strategy takes the empty subspace into account and associates it with a promising solution, which can facilitate the exploration of unknown areas. Besides, a new quality evaluation scheme is developed to evaluate the quality of each solution in subspace,

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18

Ye, Na, Cai Dai, and Xingsi Xue. "A Two-Archive Many-Objective Optimization Algorithm Based on D-Domination and Decomposition." Algorithms 15, no. 11 (2022): 392. http://dx.doi.org/10.3390/a15110392.

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Decomposition-based evolutionary algorithms are popular with solving multi-objective optimization problems. It uses weight vectors and aggregate functions to keep the convergence and diversity. However, it is hard to balance diversity and convergence in high-dimensional objective space. In order to discriminate solutions and equilibrate the convergence and diversity in high-dimensional objective space, a two-archive many-objective optimization algorithm based on D-dominance and decomposition (Two Arch-D) is proposed. In Two Arch-D, the method of D-dominance and adaptive strategy adjusting para

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19

Ebrahimi, Seyed Babak, Mostafa Abdollahi Moghadam, and Nasser Safaie. "The Quantitative Diversity Index in Multi-Objective Portfolio Model." Iranian Journal of Finance 5, no. 1 (2021): 122–46. http://dx.doi.org/10.30699/ijf.2021.125101.

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Chen, Fei, Yanmin Liu, Jie Yang, Meilan Yang, Qian Zhang, and Jun Liu. "Multi-objective particle swarm optimization with reverse multi-leaders." Mathematical Biosciences and Engineering 20, no. 7 (2023): 11732–62. http://dx.doi.org/10.3934/mbe.2023522.

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<abstract> <p>Despite being easy to implement and having fast convergence speed, balancing the convergence and diversity of multi-objective particle swarm optimization (MOPSO) needs to be further improved. A multi-objective particle swarm optimization with reverse multi-leaders (RMMOPSO) is proposed as a solution to the aforementioned issue. First, the convergence strategy of global ranking and the diversity strategy of mean angular distance are proposed, which are used to update the convergence archive and the diversity archive, respectively, to improve the convergence and diversi

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Peng, Cheng, Cai Dai, and Xingsi Xue. "A Many-Objective Evolutionary Algorithm Based on Dual Selection Strategy." Entropy 25, no. 7 (2023): 1015. http://dx.doi.org/10.3390/e25071015.

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In high-dimensional space, most multi-objective optimization algorithms encounter difficulties in solving many-objective optimization problems because they cannot balance convergence and diversity. As the number of objectives increases, the non-dominated solutions become difficult to distinguish while challenging the assessment of diversity in high-dimensional objective space. To reduce selection pressure and improve diversity, this article proposes a many-objective evolutionary algorithm based on dual selection strategy (MaOEA/DS). First, a new distance function is designed as an effective di

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22

Liu, Dr Wei Jun, and Yiming Chen. "Dynamic Multi-Objective Recommendation Via Discrete Soft Actor-Critic." International Research Journal of Library and Information Sciences 2, no. 5 (2025): 1–5. https://doi.org/10.55640/irjlis-v02i05-01.

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Modern recommender systems often need to optimize multiple conflicting objectives, such as accuracy, diversity, and novelty. This paper proposes a novel approach, Dynamic Multi-Objective Recommendation via Discrete Soft Actor-Critic (DMOR-DSAC), to address this challenge. DMOR-DSAC employs a reinforcement learning framework with a discrete action space, utilizing the Soft Actor-Critic algorithm to learn a policy that dynamically balances these objectives. Experimental results on benchmark datasets demonstrate the effectiveness of DMOR-DSAC in achieving superior multi-objective performance comp

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23

Ingmar, Linnea, Maria Garcia de la Banda, Peter J. Stuckey, and Guido Tack. "Modelling Diversity of Solutions." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 02 (2020): 1528–35. http://dx.doi.org/10.1609/aaai.v34i02.5512.

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For many combinatorial problems, finding a single solution is not enough. This is clearly the case for multi-objective optimization problems, as they have no single “best solution” and, thus, it is useful to find a representation of the non-dominated solutions (the Pareto frontier). However, it also applies to single objective optimization problems, where one may be interested in finding several (close to) optimal solutions that illustrate some form of diversity. The same applies to satisfaction problems. This is because models usually idealize the problem in some way, and a diverse pool of so

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Gao, Yue Lin, and Fan Fan Lei. "Multi-Objective Particle Swarm Optimization with Dynamic Crowding Entropy-Based Diversity Measure." Advanced Engineering Forum 1 (September 2011): 9–15. http://dx.doi.org/10.4028/www.scientific.net/aef.1.9.

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A multi-objective particle swarm optimization with dynamic crowding entropy-based diversity measure is proposed in this paper. Firstly, the elitist strategy is used in external archive in order to improve the convergence of this algorithm. Then the new diversity strategy called dynamic crowding entropy strategy and the global optimization update strategy are used to ensure sufficient diversity and uniform distribution amongst the solution of the non-dominated fronts. The results show that the proposed algorithm is able to find better spread of solutions with the better convergence to the Paret

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25

Quan, Jiang, Caihua Chen, Ruoyu Hu, Wei Zeng, Honghui Wang, and Guangle Yao. "TwoArchRH: Enhanced Two-Archive Algorithm for Many-Objective Optimization." Symmetry 17, no. 4 (2025): 572. https://doi.org/10.3390/sym17040572.

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Multi-objective optimization is a cornerstone of modern engineering and management, tackling challenges in complex system design, resource allocation, and financial portfolio optimization. Effective multi-objective optimization algorithms must strike a balance between convergence and diversity, a process that inherently reflects the symmetry in objectives and their trade-offs. However, real-world complexities introduce significant hurdles: the exponential increase in Pareto optimal solutions diminishes the effectiveness of dominance-based selection, while escalating problem complexity heighten

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Xia, Yizhang, Jianzun Huang, Xijun Li, Yuan Liu, Jinhua Zheng, and Juan Zou. "A Many-Objective Evolutionary Algorithm Based on Indicator and Decomposition." Mathematics 11, no. 2 (2023): 413. http://dx.doi.org/10.3390/math11020413.

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In the field of many-objective evolutionary optimization algorithms (MaOEAs), how to maintain the balance between convergence and diversity has been a significant research problem. With the increase of the number of objectives, the number of mutually nondominated solutions increases rapidly, and multi-objective evolutionary optimization algorithms, based on Pareto-dominated relations, become invalid because of the loss of selection pressure in environmental selection. In order to solve this problem, indicator-based many-objective evolutionary algorithms have been proposed; however, they are no

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27

TOSHEVA, DILFUZA FARKHODOVNA, and DILBAR FARKHODOVNA TOSHEVA. "Production of Objective Tourist Routes and Infrastructure of Historical Architectural Objects of the Bukhara Region." International Journal of Academic Research in Business, Arts & Science ( IJARBAS.COM ) 3, no. 6 (2021): 54–63. https://doi.org/10.5281/zenodo.4972670.

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The article examines all existing historical monuments in the Bukhara region, comprehensive measures to diversify tourism in the national economy, accelerate regional development, create new jobs, increase income and living standards, increase the country's investment attractiveness as one of the proposals of strategic sectors for implementation.   <strong>Keywords:</strong> tourist areas, historical monuments, diversity, seven shrines of Pira, national economy, heritage sites.  

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Désiré, Mugisha Alain, and Nguendo Yongsi H. Blaise. "Déterminants de la Diversité Alimentaire des Ménages des Provinces de Muyinga et de Karusi au Burundi." European Scientific Journal, ESJ 20, no. 5 (2024): 26. http://dx.doi.org/10.19044/esj.2024.v20n5p26.

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Contexte : Tout comme la plupart des pays africains, la situation du Burundi en matière de l’alimentation n’est pas assez satisfaisante. Le taux de pauvreté monétaire touche plus de la moitié de la population, 51,4% selon les résultats de l’Enquête Intégrée sur les Conditions de vie des ménages de 2020. En d’autres termes, un peu plus de la moitié de la population résidant au Burundi ne parvient pas à satisfaire quotidiennement leurs besoins de base alimentaires et non-alimentaires. Objectifs : L’objectif principal est d’identifier les déterminants de la diversité alimentaire à Karusi et à Muy

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Qodriyah, Khodijatul, Masykuri Bakri, and Djunaidi Ghony. "The Construction of Objective Consciousness in Multicultural Society Based on Pesantren." Asian Journal of Education and Social Studies 50, no. 4 (2024): 136–49. http://dx.doi.org/10.9734/ajess/2024/v50i41319.

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The discourse on multicultural education remains relevant in contemporary societies, particularly in countries with diverse ethnic, religious, and cultural backgrounds. The challenges posed by information disruption further emphasize the need to revisit and discuss multicultural education within such contexts. The failure to implement a humanistic religious philosophy exacerbates unity issues within the national framework. This research aims to understand the forms of democratic citizenship in multicultural societies based on pesantren, arising from an objective awareness of diversity. The stu

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Zhiyong Li, Ransikarn Ngambusabongsopa, Esraa Mohammed, and Ndatinya Eustache. "A Novel Diversity Guided Particle Swarm Multi-objective Optimization Algorithm." International Journal of Digital Content Technology and its Applications 5, no. 1 (2011): 269–78. http://dx.doi.org/10.4156/jdcta.vol5.issue1.29.

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31

Martí, Luis, Eduardo Segredo, Nayat Sánchez-Pi, and Emma Hart. "Selection methods and diversity preservation in many-objective evolutionary algorithms." Data Technologies and Applications 52, no. 4 (2018): 502–19. http://dx.doi.org/10.1108/dta-01-2018-0009.

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Purpose One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms, is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversity in the solution set provided. In the current work, an exhaustive study that involves the comparison of several selection mechanisms with different features is performed. Particularly, Pareto-based and indicator-based selection schemes, whic

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Yuan, Yuan, Hua Xu, Bo Wang, Bo Zhang, and Xin Yao. "Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers." IEEE Transactions on Evolutionary Computation 20, no. 2 (2016): 180–98. http://dx.doi.org/10.1109/tevc.2015.2443001.

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33

Villalobos-Arias, Mario Alberto, Gregorio Toscano Pulido, and Carlos A. Coello Coello. "A new mechanism to maintain diversity in multi-objective metaheuristics." Optimization 61, no. 7 (2012): 823–54. http://dx.doi.org/10.1080/02331934.2010.534476.

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Li, Miqing, Shengxiang Yang, and Xiaohui Liu. "Diversity Comparison of Pareto Front Approximations in Many-Objective Optimization." IEEE Transactions on Cybernetics 44, no. 12 (2014): 2568–84. http://dx.doi.org/10.1109/tcyb.2014.2310651.

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35

Wang, Xinzi, Huimin Wang, Zhen Tian, Wenxiao Wang, and Junming Chen. "Angle-Based Dual-Association Evolutionary Algorithm for Many-Objective Optimization." Mathematics 13, no. 11 (2025): 1757. https://doi.org/10.3390/math13111757.

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As the number of objectives increases, the comprehensive processing performance of multi-objective optimization problems significantly declines. To address this challenge, this paper proposes an Angle-based dual-association Evolutionary Algorithm for Many-Objective Optimization (MOEA-AD). The algorithm enhances the exploration capability of unknown regions by associating empty subspaces with the solutions of the highest fitness through an angle-based bi-association strategy. Additionally, a novel quality assessment scheme is designed to evaluate the convergence and diversity of solutions, intr

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Santos, Thiago, and S. Xavier. "A Convergence Indicator for Multi-Objective Optimisation Algorithms." TEMA (São Carlos) 19, no. 3 (2018): 437. http://dx.doi.org/10.5540/tema.2018.019.03.437.

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The algorithms of multi-objective optimisation had a relative growth in the last years. Thereby, it's requires some way of comparing the results of these. In this sense, performance measures play a key role. In general, it's considered some properties of these algorithms such as capacity, convergence, diversity or convergence-diversity. There are some known measures such as generational distance (GD), inverted generational distance (IGD), hypervolume (HV), Spread($\Delta$), Averaged Hausdorff distance ($\Delta_p$), R2-indicator, among others. In this paper, we focuses on proposing a new indica

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Likhitha, T., and Sini Raj Pulari. "Diversity in Recommender Systems: A Closer Look." Journal of Computational and Theoretical Nanoscience 17, no. 1 (2020): 222–27. http://dx.doi.org/10.1166/jctn.2020.8654.

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Recommender Systems are widely used in numerous areas where there is enormous data. It helps to analyse such vague information and present to the end user in a simplified form. Although there are many objectives of the recommender systems like diversity, accuracy, serendipity, novelty etc., accuracy is mostly used in many cases to generate an effective system. But system will provide more desirable result by including more objectives along with accuracy. In this paper we discuss about the objective diversity and explored the existing diversity techniques and its applications in various fields.

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Heong, K. L., and G. B. Aquino. "Arthropod Diversity in Tropical Rice Ecosystems." International Rice Research Newsletter 15, no. 1 (1990): 31–32. https://doi.org/10.5281/zenodo.7179081.

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This article 'Arthropod Diversity in Tropical Rice Ecosystems' appeared in the International Rice Research Newsletter series, created by the International Rice Research Institute (IRRI). The primary objective of this publication was to expedite communication among scientists concerned with the development of improved technology for rice and for rice based cropping systems. This publication will report what scientists are doing to increase the production of rice in as much as this crop feeds the most densely populated and land scarce nations in the world.

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LARAQI, HASNAA, and BENNANI YASMINA BENNIS. "What relationship between diversity in the workplace and organizational performance: a conceptual framework." African scientific journal Vol 3, N° 9 (2021): 304. https://doi.org/10.5281/zenodo.5807565.

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Résumé Cet article examine les recherches précédentes traitant l'influence de la diversité en milieu de travail sur la performance organisationnelle. Il fournit un cadre conceptuel de l'influence de la diversité sur la performance, et passe en revue la littérature sur les avantages potentiels de la diversité sur la performance et les problèmes potentiels de la diversité. L'objectif de cet article est de fournir aux praticiens et aux universitaires un cadre qui leur permettra de concevoir des initiatives de divers

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Sun, Bao, Na Guo, Lijing Zhang, and Zhanlong Li. "A Hybrid Strategy Guided Multi-Objective Artificial Physical Optimizer Algorithm." Information Technology and Control 53, no. 1 (2024): 128–45. http://dx.doi.org/10.5755/j01.itc.53.1.33456.

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Artificial physical optimizer (APO), as a new heuristic stochastic algorithm, is difficult to balance convergence and diversity when dealing with complex multi-objective problems. This paper introduces the advantages of R2 indicator and target space decomposition strategy, and constructs the candidate solution of external archive pruning technology selection based on APO algorithm. A hybrid strategy guided multi-objective artificial physical optimizer algorithm (HSGMOAPO) is proposed. Firstly, R2 indicator is used to select the candidate solutions that have great influence on the convergence o

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Guo, Wei, Lanju Kong, Xudong Lu, and Lizhen Cui. "An Intelligent Genetic Scheme for Multi-Objective Collaboration Services Scheduling." Symmetry 14, no. 10 (2022): 2037. http://dx.doi.org/10.3390/sym14102037.

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The optimization of collaborative service scheduling is the main bottleneck restricting the efficiency and cost of collaborative service execution. It is helpful to reduce the cost and improve the efficiency to deal with the scheduling problem correctly and effectively. The traditional genetic algorithm can solve the multi-objective problem more comprehensively than the optimization algorithm, such as stochastic greedy algorithm. But in the actual situation, the traditional algorithm is still one-sided. The intelligent genetic scheme (IGS) proposed in this paper enhances the expansibility and

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Zhu, Shuwei, Liusheng Zeng, and Meiji Cui. "Symmetrical Generalized Pareto Dominance and Adjusted Reference Vector Cooperative Evolutionary Algorithm for Many-Objective Optimization." Symmetry 16, no. 11 (2024): 1484. http://dx.doi.org/10.3390/sym16111484.

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In Pareto-based many-objective evolutionary algorithms, performance usually degrades drastically as the number of objectives increases due to the poor discriminability of Pareto optimality. Although some relaxed Pareto domination relations have been proposed to relieve the loss of selection pressure, it is hard to maintain good population diversity, especially in the late phase of evolution. To solve this problem, we propose a symmetrical Generalized Pareto Dominance and Adjusted Reference Vectors Cooperative (GPDARVC) evolutionary algorithm to deal with many-objective optimization problems. T

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Ünal, Ali Nadi, and Gülgün Kayakutlu. "Multi-objective particle swarm optimization with random immigrants." Complex & Intelligent Systems 6, no. 3 (2020): 635–50. http://dx.doi.org/10.1007/s40747-020-00159-y.

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Abstract Complex problems of the current business world need new approaches and new computational algorithms for solution. Majority of the issues need analysis from different angles, and hence, multi-objective solutions are more widely used. One of the recently well-accepted computational algorithms is Multi-objective Particle Swarm Optimization (MOPSO). This is an easily implemented and high time performance nature-inspired approach; however, the best solutions are not found for archiving, solution updating, and fast convergence problems faced in certain cases. This study investigates the pre

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Yang, Wusi, Li Chen, Yi Wang, and Maosheng Zhang. "Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism." Computational Intelligence and Neuroscience 2020 (February 20, 2020): 1–26. http://dx.doi.org/10.1155/2020/5132803.

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The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed θ-dominance is adopted to further enhance the performanc

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Bui, Chi Buu. "Genetic Diversity in Rice Oryza sativa L." International Rice Research Newsletter 14, no. 6 (1989): 5. https://doi.org/10.5281/zenodo.7155932.

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This article 'Genetic Diversity in Rice Oryza sativa L' appeared in the International Rice Research Newsletter series, created by the International Rice Research Institute (IRRI). The primary objective of this publication was to expedite communication among scientists concerned with the development of improved technology for rice and for rice based cropping systems. This publication will report what scientists are doing to increase the production of rice in as much as this crop feeds the most densely populated and land scarce nations in the world.

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Segredo, Eduardo, Carlos Segura, Coromoto León, and Emma Hart. "A fuzzy logic controller applied to a diversity-based multi-objective evolutionary algorithm for single-objective optimisation." Soft Computing 19, no. 10 (2014): 2927–45. http://dx.doi.org/10.1007/s00500-014-1454-y.

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Askr, Heba, M. A. Farag, Aboul Ella Hassanien, Václav Snášel, and Tamer Ahmed Farrag. "Many-objective African vulture optimization algorithm: A novel approach for many-objective problems." PLOS ONE 18, no. 5 (2023): e0284110. http://dx.doi.org/10.1371/journal.pone.0284110.

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Several optimization problems can be abstracted into many-objective optimization problems (MaOPs). The key to solving MaOPs is designing an effective algorithm to balance the exploration and exploitation issues. This paper proposes a novel many-objective African vulture optimization algorithm (MaAVOA) that simulating the African vultures’ foraging and navigation behaviours to solve the MaOPs. MaAVOA is an updated version of the African Vulture Optimization Algorithm (AVOA), which was recently proposed to solve the MaOPs. A new social leader vulture for the selection process is introduced and i

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Wang, Xiaohui, and Shengpu Li. "Multi-Objective Optimization Using Cooperative Garden Balsam Optimization with Multiple Populations." Applied Sciences 12, no. 11 (2022): 5524. http://dx.doi.org/10.3390/app12115524.

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Traditional multi-objective evolutionary algorithms (MOEAs) consider multiple objectives as a whole when solving multi-objective optimization problems (MOPs). In this paper, the hybridization of garden balsam optimization (GBO) is presented to solve multi-objective optimization, applying multiple populations for multiple objectives individually. Moreover, in order to improve the diversity of the solutions, both crowding distance computations and epsilon dominance relations are adopted when updating the archive. Furthermore, an efficient selection procedure called co-evolutionary multi-swarm ga

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GENG, Huantong, Jun GAO, Tingting JIA, and Zhengxue WU. "Multi-objective particle swarm optimization method with balanced diversity and convergence." Journal of Computer Applications 33, no. 7 (2013): 1926–29. http://dx.doi.org/10.3724/sp.j.1087.2013.01926.

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Pan, Linqiang, Cheng He, Ye Tian, Yansen Su, and Xingyi Zhang. "A region division based diversity maintaining approach for many-objective optimization." Integrated Computer-Aided Engineering 24, no. 3 (2017): 279–96. http://dx.doi.org/10.3233/ica-170542.

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