Optimization techniques (GA, PSO, ANN) – MCQs – EE

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1. What does GA stand for in optimization techniques?



2. The Genetic Algorithm (GA) is inspired by which natural process?



3. In GA, each potential solution is represented as a:



4. What is the main objective of Particle Swarm Optimization (PSO)?



5. In GA, the crossover operator is used to:



6. In PSO, a particle’s movement depends on:



7. ANN stands for:



8. Which of the following is NOT a component of GA?



9. The fitness function in GA is used to:



10. In PSO, each particle represents:



11. The learning in ANN is achieved by:



12. Which of the following optimization methods is population-based?



13. In PSO, the term inertia weight controls:



14. In GA, the mutation operator helps to:



15. The activation function in ANN is responsible for:



16. The PSO algorithm starts by:



17. Which of the following is an advantage of GA?



18. In PSO, the velocity update equation includes:



19. ANN models are trained using algorithms such as:



20. In GA, the selection process is used to:



21. PSO is most suitable for problems that:



22. ANN-based optimization is effective when:



23. Which of the following describes exploration in optimization algorithms?



24. In PSO, the cognitive component represents:



25. Which optimization method mimics human brain learning?



26. In GA, increasing mutation rate excessively can lead to:



27. Which method uses neurons, weights, and biases?



28. The global best (gbest) in PSO represents:



29. The main goal of optimization techniques like GA, PSO, and ANN is to:



30. In electrical engineering, these optimization algorithms are often used for:



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