Adaptive Restart Hybrid Genetic Algorithm for Constrained Optimization Problems (Case study 2: MBF)

In this video, I’m going to show you my innovative version of genetic algorithm called adaptive re-start hybrid genetic algorithm for constrained optimization problems in Matlab. This innovative genetic algorithm is much more powerful than the traditional genetic algorithm because of the re-start mechanism and the local search. I have developed a general template code in Matlab, so that new users can quickly and easily customize and update this Matlab code to solve new optimization problems in their fields. No need to waste your time building or typing this powerful genetic algorithm from scratch - you can build up a powerful genetic algorithm from this general template code. I’m going to demonstrate how to use this general template code to solve the Mishra's Bird function.
This is the second case study in a series of videos demonstrating the power of my adaptive re-start hybrid genetic algorithm for constrained optimization problems
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Download the Matlab code: bit.ly/2OtUIQA
HERE ARE 6 LISTS OF MY VIDEOS YOU MAY BE INTERESTED IN:
1. Optimization Using Genetic Algorithm:
• Optimization Using Gen...
2. Optimization Using Particle Swarm Optimization:
• Optimization Using Par...
3. Optimization Using Simulated Annealing Algorithm:
• Optimization Using Sim...
4. Optimization Using Optimization Solvers:
• Optimization Using Opt...
5. Optimization Using Matlab:
• Optimization Using Matlab
6. Optimization Using Python:
• Optimization Using Python
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[@ngr5147]

This is fantastic! How do I modify the code to handle more than one constraint?

[@SolvingOptimizationProblems]

That's an interesting but complex question. I will make that video. Thanks for your suggestion.

[@ngr5147]

Thanks! I can’t wait to share it with my colleagues, we greatly enjoy your videos.

[@SolvingOptimizationProblems]

Many thanks for your support!

[@ngr5147]

Could you explain how to choose a penalty parameter when dealing with more than one constraint?

[@SolvingOptimizationProblems]

Selecting the right penalty coefficient is not easy. This is the main drawback of the method. Try and error method can be used.

[@tanmayatapaswinitripathy285]

How to implement search history, trajectory of 1st particle of any optimization algorithm in matlab?

[@SolvingOptimizationProblems]

I don't know the search history

[@farmad100]

Please prepare a video on multiobjective gravitational search algo with matlab code

[@SolvingOptimizationProblems]

Noted. Thank you for the suggestion.