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Python Code || Path Planning with Grey Wolf Optimization (GWO) ~xRay Pixy

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Learn how to implement an obstacle-avoiding path planning for a robot using the Grey Wolf Optimization (GWO) in a static environment. #optimization #algorithm #metaheuristic #robotics #deeplearning #ArtificialIntelligence #MachineLearning #computervision #research #projects #thesis #Python
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Solved Constrained Engineering Optimization Problems using Metaheuristic...

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Constrained Engineering Optimization Problems


In this video, we applied different Metaheuristic Optimization Algorithms on 3 different Constrained Engineering Design Optimization Problems E01, E02 and E03.
E01: Welded beam design problem. E02: Speed Reducer design optimization problem. E03: Tension/Compression spring design optimization problem.

All constrained engineering optimization problems have different Objective function, Decision variables and Constraints. We did not try to optimize SSA parameters, for each problem constraints are directly handled [it means IF Solution can not satisfy the constraints – we will consider it Infeasible Solution]. Three engineering problems are solved using Sparrow Search Algorithm (SSA). We also compared the results with respect to 3 Metaheuristic Algorithms: Particle Swarm Optimization Algorithm (PSO), Grey Wolf Optimization Algorithm (GWO) and Teaching Leaning Based Optimization Algorithm (TLBO).

When we compared SSA with other algorithms, the performance of SSA is better as compared to other. SSA algorithm obtained OPTIMAL value for each constrained engineering optimization problem in each run. That's why we considered SSA suitable for solving constrained optimization problem [because SSA is simple, Fast, reliable and provide accurate results].

Result Analysis: The result obtained by SSA is Compared with different metaheuristic optimization algorithms. We selected three constrained engineering design problems for the evaluation of SSA. For Swarm Size (6), we performed independent run for each problem. It means that we run the code only once and note down Best and Worst Values obtained in each run [for each algorithm SSA, PSO, GWO and TLBO].

Video Timestamps: Introduction: 00:00 Welded beam design optimization: 02:02 Speed Reducer design optimization problem: 03:27 Tension/compression spring design optimization problem: 03:52 Optimization Algorithms used: 04:22 Project Result Analysis and Comparison: 04:39 MATLAB Code: 10:13

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