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Intelligent Traffic Management Using || AI & Metaheuristics || ~xRay Pixy

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Hybrid Artificial Intelligence and Metaheuristics for Smart City TRafci Management Problem Video Chapters: 00:00 Introduction 00:40 Smart Cities 01:14 Traditional Methods for Traffic Management 02:12 Hybrid Approach AI and Metaheuristics 02:47 STEPS for Hybrid  Traffic Management System 08:40 Advantages of Smart Traffic Management System 09:33 Conclusion

Optimization Engineering - Design Optimization

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 [ CAN Design Optimization using Teaching Learning Based Optimization Algorithm] PROBLEM STATEMENT:  Design a Can to Hold 800ml Liquid. OBJECTIVE: Minimize the CAN Manufacturing Cost, Minimize Amount of Sheet Metal Required.  CONSTRAINTS: For Diameter, It should be no greater than 16 cm and no less than 4.0 cm, For Height, It should be no more than 36 cm and no less than 16 cm.                                                     4.0 <= Diameter <= 16; cm                                                     16<= Height <= 36; cm Constraints to HOLD 800ml Liquid Capacity. OBJECTIVE FUNCTION: COST Function Used to Solve this problem: RESULT: AFTER OPTIMIZATION USING TEACHING LEARNING BASED OPTIMIZATION ALGORITHM Optimal Diameter 4.3679 Optimal Height 34.0069 Best Cost = 207.24 Teaching Learning Based Optimization Algorithm  |  TLBO Numerical Example  | Learn Teaching Learning Based Optimization Algorithm Step-by-Step with Numerical Example. Teaching Learning Based Optimizati

Shark Smell Optimization Algorithm Numerical Example

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SHARK SMELL OPTIMIXATION ALGORITHM [ Numerical Example ] Shark Smell Optimization Algorithm is population based Metaheuristic optimization algorithm. Shark Smell Optimization Algorithm is inspired by the Shark food foraging behavior.   Shark Smell Optimization Algorithm Steps: Initialize Algorithm Parameters Initialize Population for N Sharks in the search space. Evaluate Performance. While (current Iteration < Maximum Iteration) Calculate Shark Velocity Calculate Shark Position based on forward movement. Calculate Shark Position based on rotational movement. Identify Shark next position based on forward and rotational movements. Evaluate Performance. End While Display Best Solution. STEP 01: Initial Important Parameters.          Current_Iteration =1;          Maximum_Iteration = 10;          and other. STEP 02: Initial Population Randomly. Suppose, Population Size = 2; Position(1) = -0.9891 Position(2) = -8.3236 STEP 03: Using Fitness Function Calculate Fitness Values. Fitness(1)

Teaching Learning Based Optimization Algorithm | TLBO Numerical Example|

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Teaching Learning Based Optimization Algorithm  | TLBO Numerical Example | Learn Teaching Learning Based Optimization Algorithm Step-by-Step with Numerical Example. Teaching Learning Based Optimization Algorithm is based on the effect of Teacher on the Learners in the class. Teaching Learning Based Optimization Algorithm is basically inspired by the behavior of learners in the classroom. In Teaching Learning Based Optimization Algorithm 2 main procedure are followed: Teaching Phase: Learners study from the Teacher. Learner Phase: Learners can interact with each other and they can randomly interact with each other. Teaching Learning Based Optimization Algorithm Steps: Population Initialization Phase. Agents Performance Evaluation using Cost Function. Select Best Solution Among all. Calculate Mean Value. Teacher Phase. Mathematical Model Learner Phase MathematicalModel Evaluate New Solutions. Update Current Best Solution. Check Stopping Criteria. Display Best Solution.

Bacterial Foraging Optimization Algorithm | NUMERICAL EXAMPLE | ~xRay Pixy

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Bacterial Foraging Optimization Algorithm   |PART 2 - NUMERICAL EXAMPLE | _______________________________________________________________________________ [ PART 1 ] Learn Bacterial Foraging Optimization Algorithm (BFOA) Step-by-Step Learning ~xRay Pixy https://youtu.be/LXInp4wvpXM

Laptops, Desktops, Printers and Computers Accessories

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