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FOA operates through two distinct phases, each managed by a separate swarm, mirroring the Fossa's dual strategy of exploring new territories and exploiting known food sources.",{"type":19,"format":14,"indent":15,"version":16,"children":551,"direction":25,"textStyle":14,"textFormat":15},[552,554,556],{"mode":22,"text":553,"type":24,"style":14,"detail":15,"format":15,"version":16},"In the ",{"mode":22,"text":555,"type":24,"style":14,"detail":15,"format":16,"version":16},"Exploration Phase",{"mode":22,"text":557,"type":24,"style":14,"detail":15,"format":15,"version":16},", a dedicated \"Scout Swarm\" simulates the Fossa's solitary and wide-ranging search for new prey and habitats. This phase emphasizes diversification and global search capabilities, allowing the algorithm to extensively explore the search space and avoid premature convergence to local optima.",{"type":19,"format":14,"indent":15,"version":16,"children":559,"direction":25,"textStyle":14,"textFormat":15},[560,562,564],{"mode":22,"text":561,"type":24,"style":14,"detail":15,"format":15,"version":16},"Conversely, the ",{"mode":22,"text":563,"type":24,"style":14,"detail":15,"format":16,"version":16},"Exploitation Phase",{"mode":22,"text":565,"type":24,"style":14,"detail":15,"format":15,"version":16}," is managed by a \"Hunter Swarm\" that models the Fossa's more focused and cooperative hunting tactics when a prey-rich area is identified. 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I implemented the ",{"mode":22,"text":722,"type":24,"style":14,"detail":15,"format":16,"version":16},"Osprey Optimization Algorithm (OOA)",{"mode":22,"text":724,"type":24,"style":14,"detail":15,"format":15,"version":16},"—a recent nature-inspired metaheuristic—in ",{"mode":22,"text":618,"type":24,"style":14,"detail":15,"format":16,"version":16},{"mode":22,"text":727,"type":24,"style":14,"detail":15,"format":15,"version":16}," to solve this complex, real-world combinatorial problem.",{"type":19,"format":14,"indent":15,"version":16,"children":729,"direction":25,"textStyle":14,"textFormat":15},[730],{"mode":22,"text":731,"type":24,"style":14,"detail":15,"format":15,"version":16},"The algorithm intelligently assigned trash pickup points to vehicles, ensuring optimal route length while respecting vehicle capacity and operational limits. 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The ANN-GWO hybrid enabled rapid evaluation of design alternatives without the need for repeated IESVE simulations, significantly speeding up the optimization process.",{"type":19,"format":14,"indent":15,"version":16,"children":785,"direction":25,"textStyle":14,"textFormat":15},[786],{"mode":22,"text":787,"type":24,"style":14,"detail":15,"format":15,"version":16},"This approach demonstrated the power of surrogate modeling in building energy analysis, providing a scalable and intelligent solution for early-stage design decisions in sustainable architecture.",[789,792,795,798,800],{"id":790,"technology":791},"6832c8bf4ade5d062c099b83","IESVE Simulation Software (for simulation data generation)",{"id":793,"technology":794},"6832c8c44ade5d062c099b85","Python & PyTorch",{"id":796,"technology":797},"6832c8c84ade5d062c099b87","Artificial Neural Networks (ANN)",{"id":799,"technology":427},"6832c8cc4ade5d062c099b89",{"id":801,"technology":802},"6832c8d04ade5d062c099b8b","Pandas, NumPy, and Matplotlib for data handling and visualization",[804],{"id":805,"roleTitle":231},"6832c8d64ade5d062c099b8d","2023-03-08T17:00:00.000Z","2023-05-27T17:00:00.000Z",[],"thermal-parameter-optimization-using-ann-and-grey-wolf-optimizer-with-iesve-integration","2025-07-06T06:28:10.373Z","2025-05-25T07:39:51.417Z",{"id":64,"title":813,"description":814,"technologies":841,"role":853,"startDate":856,"endDate":857,"status":234,"images":858,"projectUrl":118,"repositoryUrl":118,"featured":282,"slug":859,"updatedAt":860,"createdAt":861},"Parametric Glass Installation Design Using Grasshopper and African Vulture Optimization in C#",{"root":815},{"type":13,"format":14,"indent":15,"version":16,"children":816,"direction":25},[817,829,837],{"type":19,"format":14,"indent":15,"version":16,"children":818,"direction":25,"textStyle":14,"textFormat":15},[819,821,823,825,827],{"mode":22,"text":820,"type":24,"style":14,"detail":15,"format":15,"version":16},"This component focused on the parametric design of glass installations to regulate natural light and indoor temperature, contributing to both energy efficiency and occupant comfort. I developed a ",{"mode":22,"text":822,"type":24,"style":14,"detail":15,"format":16,"version":16},"custom Grasshopper plugin for Rhino",{"mode":22,"text":824,"type":24,"style":14,"detail":15,"format":15,"version":16},", written in ",{"mode":22,"text":826,"type":24,"style":14,"detail":15,"format":16,"version":16},"C#",{"mode":22,"text":828,"type":24,"style":14,"detail":15,"format":15,"version":16},", which allowed designers to intuitively manipulate architectural parameters and immediately visualize their impact on environmental performance.",{"type":19,"format":14,"indent":15,"version":16,"children":830,"direction":25,"textStyle":14,"textFormat":15},[831,833,835],{"mode":22,"text":832,"type":24,"style":14,"detail":15,"format":15,"version":16},"To optimize the design, I implemented the ",{"mode":22,"text":834,"type":24,"style":14,"detail":15,"format":16,"version":16},"African Vulture Optimization Algorithm (AVOA)",{"mode":22,"text":836,"type":24,"style":14,"detail":15,"format":15,"version":16},"—a recent nature-inspired metaheuristic known for its strong balance between exploration and exploitation. The algorithm iteratively adjusted parameters such as glazing angle, tint level, and panel placement to maximize daylight utilization while minimizing thermal gain.",{"type":19,"format":14,"indent":15,"version":16,"children":838,"direction":25,"textStyle":14,"textFormat":15},[839],{"mode":22,"text":840,"type":24,"style":14,"detail":15,"format":15,"version":16},"By combining computational design with intelligent optimization, this tool empowered architects and engineers to make data-driven decisions during the early stages of design, ensuring aesthetics and sustainability were seamlessly integrated.",[842,845,848,850],{"id":843,"technology":844},"6832c9be4ade5d062c099b8f","Rhino + Grasshopper (visual programming environment)",{"id":846,"technology":847},"6832c9c34ade5d062c099b91","C# (for custom plugin development)",{"id":849,"technology":834},"6832c9e44ade5d062c099b93",{"id":851,"technology":852},"6832c9e74ade5d062c099b95","Parametric modeling techniques for façade design",[854],{"id":855,"roleTitle":231},"6832c9ee4ade5d062c099b97","2023-03-07T17:00:00.000Z","2023-10-24T17:00:00.000Z",[],"parametric-glass-installation-design-using-grasshopper-and-african-vulture-optimization-in-c","2025-05-25T07:47:00.763Z","2025-05-25T07:47:00.729Z",{"year":863,"projects":864},2022,[865,914],{"id":47,"title":866,"description":867,"technologies":896,"role":905,"startDate":908,"endDate":909,"status":234,"images":910,"projectUrl":118,"repositoryUrl":118,"featured":121,"slug":911,"updatedAt":912,"createdAt":913},"Material Placement Optimization Using Enhanced Ant Lion Optimization in Python",{"root":868},{"type":13,"format":14,"indent":15,"version":16,"children":869,"direction":25},[870,888,892],{"type":19,"format":14,"indent":15,"version":16,"children":871,"direction":25,"textStyle":14,"textFormat":15},[872,874,876,878,879,881,883,885,887],{"mode":22,"text":873,"type":24,"style":14,"detail":15,"format":15,"version":16},"In this phase of the project, I addressed the challenge of optimizing material placement within a set of predefined locations to reduce handling time and improve workflow efficiency. I developed an enhanced version of the ",{"mode":22,"text":875,"type":24,"style":14,"detail":15,"format":16,"version":16},"Ant Lion Optimization (ALO)",{"mode":22,"text":877,"type":24,"style":14,"detail":15,"format":15,"version":16}," algorithm in ",{"mode":22,"text":215,"type":24,"style":14,"detail":15,"format":16,"version":16},{"mode":22,"text":880,"type":24,"style":14,"detail":15,"format":15,"version":16},", integrating two powerful strategies: ",{"mode":22,"text":882,"type":24,"style":14,"detail":15,"format":16,"version":16},"mutation operators",{"mode":22,"text":884,"type":24,"style":14,"detail":15,"format":15,"version":16}," and ",{"mode":22,"text":886,"type":24,"style":14,"detail":15,"format":16,"version":16},"opposite-based learning (OBL)",{"mode":22,"text":605,"type":24,"style":14,"detail":15,"format":15,"version":16},{"type":19,"format":14,"indent":15,"version":16,"children":889,"direction":25,"textStyle":14,"textFormat":15},[890],{"mode":22,"text":891,"type":24,"style":14,"detail":15,"format":15,"version":16},"This hybridization significantly boosted the algorithm's exploration capabilities and convergence reliability. The mutation component introduced diversity into the search space, helping to escape local optima, while opposite-based learning accelerated convergence by evaluating both current and opposite solutions during the search process.",{"type":19,"format":14,"indent":15,"version":16,"children":893,"direction":25,"textStyle":14,"textFormat":15},[894],{"mode":22,"text":895,"type":24,"style":14,"detail":15,"format":15,"version":16},"The resulting system intelligently positioned materials based on frequency of use, access priority, and spatial constraints, delivering tangible improvements in layout efficiency and operational cost.",[897,899,902],{"id":898,"technology":215},"6832c7754ade5d062c099b71",{"id":900,"technology":901},"6832c77b4ade5d062c099b73","Ant Lion Optimization with Mutation and Opposite-Based Learning",{"id":903,"technology":904},"6832c7804ade5d062c099b75","NumPy and Matplotlib for modeling and visualization",[906],{"id":907,"roleTitle":231},"6832c79e4ade5d062c099b77","2022-05-17T17:00:00.000Z","2022-12-29T17:00:00.000Z",[],"material-placement-optimization-using-enhanced-ant-lion-optimization-in-python","2025-07-06T06:31:55.979Z","2025-05-25T07:35:17.361Z",{"id":915,"title":916,"description":917,"technologies":952,"role":955,"startDate":958,"endDate":959,"status":234,"images":960,"projectUrl":118,"repositoryUrl":118,"featured":121,"slug":961,"updatedAt":962,"createdAt":963},13,"Hybrid Salp Swarm Algorithm and Dragonfly Algorithm (SSA-DA) for Optimized Gravitational Sewer System Design",{"root":918},{"type":13,"format":14,"indent":15,"version":16,"children":919,"direction":25},[920,932,948],{"type":19,"format":14,"indent":15,"version":16,"children":921,"direction":25,"textStyle":14,"textFormat":15},[922,924,926,928,930],{"mode":22,"text":923,"type":24,"style":14,"detail":15,"format":15,"version":16},"This project addresses the complex challenge of ",{"mode":22,"text":925,"type":24,"style":14,"detail":15,"format":16,"version":16},"optimizing gravitational sewer system design",{"mode":22,"text":927,"type":24,"style":14,"detail":15,"format":15,"version":16},", focusing on the strategic selection and configuration of ",{"mode":22,"text":929,"type":24,"style":14,"detail":15,"format":16,"version":16},"manholes and pipes",{"mode":22,"text":931,"type":24,"style":14,"detail":15,"format":15,"version":16},". The objective is to achieve a highly efficient and cost-effective sewer network that adheres to all necessary hydraulic and topographical constraints.",{"type":19,"format":14,"indent":15,"version":16,"children":933,"direction":25,"textStyle":14,"textFormat":15},[934,936,938,940,942,944,946],{"mode":22,"text":935,"type":24,"style":14,"detail":15,"format":15,"version":16},"To tackle the multi-faceted nature of this optimization problem, a novel ",{"mode":22,"text":937,"type":24,"style":14,"detail":15,"format":16,"version":16},"hybrid metaheuristic approach",{"mode":22,"text":939,"type":24,"style":14,"detail":15,"format":15,"version":16}," is proposed, combining the strengths of two powerful algorithms: the ",{"mode":22,"text":941,"type":24,"style":14,"detail":15,"format":16,"version":16},"Salp Swarm Algorithm (SSA)",{"mode":22,"text":943,"type":24,"style":14,"detail":15,"format":15,"version":16}," and the ",{"mode":22,"text":945,"type":24,"style":14,"detail":15,"format":16,"version":16},"Dragonfly Algorithm (DA)",{"mode":22,"text":947,"type":24,"style":14,"detail":15,"format":15,"version":16},". This hybridization aims to enhance the search capabilities, improve convergence speed, and avoid local optima, leading to more robust and globally optimal design solutions.",{"type":19,"format":14,"indent":15,"version":16,"children":949,"direction":25,"textStyle":14,"textFormat":15},[950],{"mode":22,"text":951,"type":24,"style":14,"detail":15,"format":15,"version":16},"The SSA-DA hybrid algorithm will be employed to intelligently explore the vast design space, considering factors such as pipe diameters, slopes, manhole depths, and material costs. The project's outcome will be a refined methodology for designing gravitational sewer systems that minimizes construction expenses, operational costs, and environmental impact, thereby contributing to more sustainable and resilient urban infrastructure.",[953],{"id":954,"technology":575},"686a1c7ae612d8cf13219e1d",[956],{"id":957,"roleTitle":231},"686a1c7ce612d8cf13219e1e","2022-03-05T17:00:00.000Z","2022-11-29T17:00:00.000Z",[],"hybrid-salp-swarm-algorithm-and-dragonfly-algorithm-ssa-da-for-optimized-gravitational-sewer-system-design","2025-07-06T06:51:03.706Z","2025-07-06T06:51:03.698Z",{"year":965,"projects":966},2021,[967],{"id":54,"title":968,"description":969,"technologies":991,"role":1003,"startDate":1006,"endDate":1007,"status":234,"images":1008,"projectUrl":118,"repositoryUrl":118,"featured":121,"slug":1009,"updatedAt":1010,"createdAt":1011},"Time–Cost Tradeoff Optimization Using Moth–Flame Optimizer with Modified Adaptive Weights",{"root":970},{"type":13,"format":14,"indent":15,"version":16,"children":971,"direction":25},[972,987],{"type":19,"format":14,"indent":15,"version":16,"children":973,"direction":25,"textStyle":14,"textFormat":15},[974,976,978,980,982,984,985],{"mode":22,"text":975,"type":24,"style":14,"detail":15,"format":15,"version":16},"In this phase, I tackled the classic time–cost management problem in project scheduling by implementing a ",{"mode":22,"text":977,"type":24,"style":14,"detail":15,"format":16,"version":16},"Moth–Flame Optimizer (MFO)",{"mode":22,"text":979,"type":24,"style":14,"detail":15,"format":15,"version":16}," enhanced with a ",{"mode":22,"text":981,"type":24,"style":14,"detail":15,"format":16,"version":16},"Modified Adaptive Weight",{"mode":22,"text":983,"type":24,"style":14,"detail":15,"format":15,"version":16}," strategy in ",{"mode":22,"text":215,"type":24,"style":14,"detail":15,"format":16,"version":16},{"mode":22,"text":986,"type":24,"style":14,"detail":15,"format":15,"version":16},". 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This allowed the algorithm to more effectively navigate the non-linear time–cost surface, identifying Pareto-efficient schedules that reduce overall expenditure without unduly prolonging project timelines.",[992,994,997,1000],{"id":993,"technology":215},"6832c8564ade5d062c099b79",{"id":995,"technology":996},"6832c85d4ade5d062c099b7b","Moth–Flame Optimizer (MFO) with Modified Adaptive Weighting",{"id":998,"technology":999},"6832c8634ade5d062c099b7d","NumPy for numerical computation",{"id":1001,"technology":1002},"6832c8674ade5d062c099b7f","Matplotlib for convergence and Pareto-front visualization",[1004],{"id":1005,"roleTitle":231},"6832c8704ade5d062c099b81","2021-09-30T17:00:00.000Z","2021-12-29T17:00:00.000Z",[],"time-cost-tradeoff-optimization-using-moth-flame-optimizer-with-modified-adaptive-weights","2025-05-25T07:37:03.103Z","2025-05-25T07:37:03.089Z",{"year":1013,"projects":1014},2020,[1015,1063],{"id":42,"title":1016,"description":1017,"technologies":1048,"role":1054,"startDate":1057,"endDate":1058,"status":234,"images":1059,"projectUrl":118,"repositoryUrl":118,"featured":121,"slug":1060,"updatedAt":1061,"createdAt":1062},"Facility Layout Optimization Using Ant Lion Optimization in MATLAB",{"root":1018},{"type":13,"format":14,"indent":15,"version":16,"children":1019,"direction":25},[1020,1037,1041],{"type":19,"format":14,"indent":15,"version":16,"children":1021,"direction":25,"textStyle":14,"textFormat":15},[1022,1024,1026,1028,1030,1032,1033,1034,1035],{"mode":22,"text":1023,"type":24,"style":14,"detail":15,"format":15,"version":16},"This component of the project focused on solving a facility layout optimization problem, where ",{"mode":22,"text":1025,"type":24,"style":14,"detail":15,"format":42,"version":16},"m",{"mode":22,"text":1027,"type":24,"style":14,"detail":15,"format":15,"version":16}," facilities needed to be optimally placed across ",{"mode":22,"text":1029,"type":24,"style":14,"detail":15,"format":42,"version":16},"n",{"mode":22,"text":1031,"type":24,"style":14,"detail":15,"format":15,"version":16}," predefined locations to minimize material handling costs and improve operational flow. I implemented the ",{"mode":22,"text":875,"type":24,"style":14,"detail":15,"format":16,"version":16},{"mode":22,"text":877,"type":24,"style":14,"detail":15,"format":15,"version":16},{"mode":22,"text":618,"type":24,"style":14,"detail":15,"format":16,"version":16},{"mode":22,"text":1036,"type":24,"style":14,"detail":15,"format":15,"version":16}," to tackle this NP-hard combinatorial problem efficiently.",{"type":19,"format":14,"indent":15,"version":16,"children":1038,"direction":25,"textStyle":14,"textFormat":15},[1039],{"mode":22,"text":1040,"type":24,"style":14,"detail":15,"format":15,"version":16},"ALO, inspired by the hunting mechanism of antlions in nature, was used to intelligently explore the solution space and identify optimal or near-optimal layouts based on distance and flow matrices. The algorithm's adaptive search capabilities allowed it to balance exploration and exploitation effectively, making it well-suited for the dynamic constraints of real-world facility planning.",{"type":19,"format":14,"indent":15,"version":16,"children":1042,"direction":25,"textStyle":14,"textFormat":15},[1043,1045,1046],{"mode":22,"text":1044,"type":24,"style":14,"detail":15,"format":15,"version":16},"By leveraging ",{"mode":22,"text":618,"type":24,"style":14,"detail":15,"format":16,"version":16},{"mode":22,"text":1047,"type":24,"style":14,"detail":15,"format":15,"version":16}," for modeling, simulation, and result visualization, I created a solution that not only outperformed traditional heuristics but also offered a practical decision-support tool for layout planning in manufacturing or construction environments.",[1049,1051],{"id":1050,"technology":575},"6832c5d84ade5d062c099b6b",{"id":1052,"technology":1053},"6832c7574ade5d062c099b6f","Ant Lion Optimization Algorithm",[1055],{"id":1056,"roleTitle":231},"6832c7304ade5d062c099b6d","2020-12-16T17:00:00.000Z","2021-04-23T17:00:00.000Z",[],"facility-layout-optimization-using-ant-lion-optimization-in-matlab","2025-07-06T06:55:16.658Z","2025-05-25T07:31:01.736Z",{"id":16,"title":1064,"description":1065,"technologies":1080,"role":1083,"startDate":1086,"endDate":1087,"status":234,"images":1088,"projectUrl":118,"repositoryUrl":118,"featured":121,"slug":1089,"updatedAt":1090,"createdAt":1091},"Demand Ordering Optimization Using Dragonfly and Particle Swarm Algorithms",{"root":1066},{"type":13,"format":14,"indent":15,"version":16,"children":1067,"direction":25},[1068,1072,1076],{"type":19,"format":14,"indent":15,"version":16,"children":1069,"direction":25,"textStyle":14,"textFormat":15},[1070],{"mode":22,"text":1071,"type":24,"style":14,"detail":15,"format":15,"version":16},"In this project, I developed a hybrid metaheuristic solution combining the Dragonfly Algorithm (DA) and Particle Swarm Optimization (PSO) to optimize demand ordering strategies over various Fixed Order Period (FOP) intervals — including 1-day, 3-day, 5-day, and 7-day cycles. Implemented in MATLAB, the algorithm was designed to balance responsiveness and cost-efficiency by dynamically adjusting order schedules based on demand patterns and operational constraints.",{"type":19,"format":14,"indent":15,"version":16,"children":1073,"direction":25,"textStyle":14,"textFormat":15},[1074],{"mode":22,"text":1075,"type":24,"style":14,"detail":15,"format":15,"version":16},"The hybrid approach leveraged the exploration strength of the Dragonfly Algorithm and the convergence speed of PSO, resulting in faster convergence to high-quality solutions. By modeling and minimizing total cost—including holding, shortage, and ordering costs—this system effectively identified optimal ordering frequencies for different time horizons.",{"type":19,"format":14,"indent":15,"version":16,"children":1077,"direction":25,"textStyle":14,"textFormat":15},[1078],{"mode":22,"text":1079,"type":24,"style":14,"detail":15,"format":15,"version":16},"This work demonstrated the potential of bio-inspired algorithms in solving complex, real-world inventory management and supply chain challenges with a high degree of accuracy and adaptability.",[1081],{"id":1082,"technology":575},"6832c5494ade5d062c099b67",[1084],{"id":1085,"roleTitle":231},"6832c5504ade5d062c099b69","2020-05-04T17:00:00.000Z","2020-12-09T17:00:00.000Z",[],"optimization-of-construction-planning-using-metaheuristic-algorithms","2025-05-25T07:24:51.256Z","2025-05-25T07:23:44.778Z",1782535361399]