With the improvement of people's living standards and quality of life, health and health care as an emerging industry has begun to flourish, and people have begun to pay attention to dietary nutrition issues to improve their physical fitness and quality of life. This article aims to study wireless network multimedia communication and multi-objective evolutionary algorithm and its female athletes' nutritional diet model. This article first introduces the multimedia organization model in wireless networks, and studies the transformation of the planning problem of the multi-objective evolutionary algorithm, and proposes the preferred multi-objective evolutionary algorithm. A flow chart of the realization process of the dietary nutrition model of the multi-objective evolutionary algorithm is given, and then the nutritional diet of athletes is investigated based on wireless network multimedia communication, and the dietary nutritional status of the athletes is studied based on the multi-objective evolutionary algorithm. The experimental research results show that based on wireless network multimedia communication and multi-objective evolutionary algorithm, the research findings of its female athletes' nutritional diet model, basically, only 50% of the athletes’ diets meet the needs of athletes’ sports, which indicates that it is necessary to strengthen the propaganda of athletes’ dietary rules and health, so as to ensure that athletes can compete on the field with sufficient energy.

Jumshid Ulah Khana. Wireless Network Multimedia Communication and Multi-Objective Evolutionary Algorithm and Its Female Athlete's Nutritional Diet Model. International Journal of Sports Technology (2022), Vol. 3, Issue 3: 1-20. https://doi.org/10.38007/IJST.2022.030301.

[1] Pilis K ,  Stec K ,  Pilis A , et al. Body composition and nutrition of female athletes. Roczniki Państwowego Zakładu Higieny, 2019, 70(3):243-251. https://doi.org/10.32394/rpzh.2019.0074

[2] Barrett S L ,  Petrie T A . Female Athletes in Retirement: A Test of a Psychosocial Model of Bulimic Symptomatology. Journal of Sport & Exercise Psychology, 2020, 42(6):490-499. https://doi.org/10.1123/jsep.2020-0023

[3] Kaur H ,  Singla N . A Comparative Study on the Nutritional Profile of Male and Female Sportspersons. Current Research in Nutrition and Food Science Journal, 2017, 5(2):159-167. https://doi.org/10.12944/CRNFSJ.5.2.13

[4] Jake B ,  Jake B ,  Mis N F , et al. Nutritional Status and Cardiovascular Health in Female Adolescent Elite-Level Artistic Gymnasts and Swimmers: A Cross-Sectional Study of 31 Athletes. Journal of Nutrition and Metabolism, 2021, 2021(4):1-15. https://doi.org/10.1155/2021/8810548

[5] Dickey J P ,  Vce B ,  Nolte V W . The Impact of Nutritional Counseling in Conjunction with Co-activeCoaching on Behavior Change of Varsity Female Rowers. Journal of Nutrition Science Research, 2016, 1(3):1-8.

[6] Escalante, Guillermo. Nutritional Considerations for Female Athletes. Strength & Conditioning Journal, 2016, 38(2):57-63.

[7] Cao B ,  Zhao J ,  Lv Z , et al. A Distributed Parallel Cooperative Coevolutionary Multiobjective Evolutionary Algorithm for Large-Scale Optimization. IEEE Transactions on Industrial Informatics, 2017, 13(4):2030-2038. https://doi.org/10.1109/TII.2017.2676000

[8] Wang P ,  Zhang C S ,  Zhang B , et al. A Two-Space-Density Based Multi-objective Evolutionary Algorithm for Multi-objective Optimization. Tien Tzu Hsueh Pao/Acta Electronica Sinica, 2017, 45(10):2343-2347.

[9] Glenn J M ,  Gray M ,  Gualano B , et al. The Ergogenic Effects of Supplemental Nutritional AIDS on Anaerobic Performance in Female Athletes. Strength & Conditioning Journal, 2016, 38(2):105-120.

[10] Santos D D ,  Silveira J ,  Cesar T B . Nutritional intake and overall diet quality of female soccer players before the competition period. Revista De Nutricao-brazilian Journal of Nutrition, 2016, 29(4):555-565.

[11] Shibata S ,  Takemura M ,  Miyakawa S . The influence of differences in neurocognitive function on lower limb kinematics, kinetics, and muscle activity during an unanticipated cutting motion. Physical Therapy Research, 2018, 21(2):44-52. https://doi.org/10.1298/ptr.E9938

[12] NATSUE, KOIKAWA. Potential of Female Athletes from the Perspective of a Female Coach. Juntendo Medical Journal, 2017, 63(2):78-82. https://doi.org/10.14789/jmj.63.78

[13] Kaoru, YANAKA, Mitsuru, et al. Anti-Osteoporotic Effect of Soy Isoflavones Intake on Low Bone Mineral Density Caused by Voluntary Exercise and Food Restriction in Mature Female Rats. Journal of Nutritional Science and Vitaminology, 2019, 65(4):335-342. https://doi.org/10.3177/jnsv.65.335

[14] Aguila M B ,  Ornellas F ,  Mandarim-De-Lacerda C A . Nutritional Research and Fetal Programming: Parental Nutrition Influences the Structure and Function of the Organs. International Journal of Morphology, 2021, 39(1):327-334.

[15] Amjad M . Analyzing The Impacts Of Socioeconomic Factors On Nutritional Diet In Pakistan Using Compositional Data Analysis (Coda). Applied Ecology and Environmental Research, 2019, 17(6):13909-13929.

[16] Zhao S . Research On Scientific Sports Training Of Students Majoring In Physical Education. Revista Brasileira de Medicina do Esporte, 2021, 27(5):460-463.

[17] Jair Escalante H ,  Marin-Castro M ,  Morales-Reyes A , et al. MOPG: a multi-objective evolutionary algorithm for prototype generation. Pattern Analysis and Applications, 2017, 20(1):33-47. https://doi.org/10.1007/s10044-015-0454-6

[18] Gee S B ,  Arokiasami W A ,  Jiang J , et al. Decomposition-based multi-objective evolutionary algorithm for vehicle routing problem with stochastic demands. Soft Computing, 2016, 20(9):3443-3453. https://doi.org/10.1007/s00500-015-1830-2

[19] Yuan S ,  Deng G ,  Feng Q , et al. Multi-objective evolutionary algorithm based on decomposition for energy-aware scheduling in heterogeneous computing systems. Journal of Universal Computer ence, 2017, 23(7):636-651.

[20] Meier C ,  AA  Yassine,  Browning T R , et al. Optimizing time–cost trade-offs in product development projects with a multi-objective evolutionary algorithm. Research in Engineering Design, 2016, 27(4):1-20. https://doi.org/10.1007/s00163-016-0222-7

[21] Sudeng S ,  Wattanapongsakorn N . A knee-based multi-objective evolutionary algorithm: an extension to network system optimization design problem. Cluster Computing, 2016, 19(1):411-425. https://doi.org/10.1007/s10586-015-0492-2

[22] Manupati V K ,  Rajyalakshmi G ,  Chan F , et al. A hybrid multi-objective evolutionary algorithm approach for handling sequence- and machine-dependent set-up times in unrelated parallel machine scheduling problem. Sādhanā, 2017, 42(3):1-13. https://doi.org/10.1007/s12046-017-0611-2

[23] Fang S S ,  Chai Z Y ,  Li Y L . Dynamic multi-objective evolutionary algorithm for IoT services. Applied Intelligence, 2021, 51(1):1-24. https://doi.org/10.1007/s10489-020-01861-7

[24] Wei L ,  Zhang J L ,  Fan R , et al. Covariance matrix adaptive strategy for a multi-objective evolutionary algorithm based on reference point. Journal of Intelligent and Fuzzy Systems, 2020, 39(5):7315-7332. https://doi.org/10.3233/JIFS-200749

[25] Meghwani S S ,  Thakur M . Adaptively weighted decomposition based multi-objective evolutionary algorithm. Applied Intelligence, 2021, 51(4):1-23. https://doi.org/10.1007/s10489-020-01969-w