Genetic algorithms and particle swarm optimization – MCQs – EE

1. Genetic algorithms are inspired by:
(A) Bird flocking behavior
(B) Evolution and natural selection
(C) Step response only
(D) Transformer operation

2. In GA, a population consists of:
(A) A single candidate solution
(B) Multiple candidate solutions
(C) Step response only
(D) Only voltage measurements

3. The main operators in GA include:
(A) Inertia, velocity, and position update
(B) Selection, crossover, and mutation
(C) Step response only
(D) Load flow only

4. Crossover in GA is used to:
(A) Combine genetic information from two parents
(B) Update particle velocity
(C) Step response only
(D) Measure RMS voltage

5. Mutation in GA helps to:
(A) Maintain genetic diversity
(B) Step response only
(C) Only selection process
(D) Load flow only

6. Fitness function in GA is used to:
(A) Evaluate the quality of candidate solutions
(B) Step response only
(C) Only particle position
(D) Load flow only

7. Particle swarm optimization is inspired by:
(A) Natural selection
(B) Social behavior of birds and fish
(C) Step response only
(D) Transformer dynamics

8. In PSO, each particle has:
(A) Only position
(B) Position and velocity
(C) Step response only
(D) Load flow only

9. Personal best (pBest) in PSO refers to:
(A) Best solution found by the particle itself
(B) Step response only
(C) Global best among all particles
(D) Load flow only

10. Global best (gBest) in PSO refers to:
(A) Best solution found by the entire swarm
(B) Step response only
(C) Personal best of a particle
(D) Load flow only

11. Inertia weight in PSO controls:
(A) Exploration and exploitation
(B) Step response only
(C) Crossover probability
(D) Mutation rate

12. GA is classified as:
(A) Deterministic search method
(B) Stochastic, population-based search
(C) Step response only
(D) Load flow only

13. PSO updates particle velocity using:
(A) Only gradient information
(B) Cognitive and social components
(C) Step response only
(D) Load flow only

14. Selection in GA ensures:
(A) Better solutions have higher chance to reproduce
(B) Step response only
(C) Only mutation occurs
(D) Load flow only

15. Termination criteria for GA or PSO can be:
(A) Maximum iterations, convergence, or fitness threshold
(B) Step response only
(C) Only particle velocity
(D) Load flow only

16. GA is suitable for:
(A) Only linear problems
(B) Complex, nonlinear, and multi-modal problems
(C) Step response only
(D) Load flow only

17. PSO advantages include:
(A) Easy implementation and fast convergence
(B) Step response only
(C) Requires gradient calculation
(D) Load flow only

18. Crossover rate in GA determines:
(A) Probability of combining parents to produce offspring
(B) Step response only
(C) Particle velocity update
(D) Load flow only

19. Mutation rate in GA helps to:
(A) Avoid premature convergence
(B) Step response only
(C) Only selection
(D) Load flow only

20. PSO particles communicate:
(A) Using global and local best solutions
(B) Step response only
(C) Only mutation
(D) Load flow only

21. GA uses:
(A) Operators like selection, crossover, and mutation
(B) Step response only
(C) Velocity updates
(D) Load flow only

22. PSO is:
(A) Deterministic method
(B) Stochastic, population-based optimizer
(C) Step response only
(D) Load flow only

23. Hybrid GA-PSO algorithms combine:
(A) Strengths of GA and PSO for better performance
(B) Step response only
(C) Only GA operators
(D) Load flow only

24. Encoding in GA involves:
(A) Representing candidate solutions as chromosomes
(B) Step response only
(C) Only particle position
(D) Load flow only

25. PSO is widely used in EE for:
(A) Economic dispatch, design optimization, and control tuning
(B) Step response only
(C) Only load flow
(D) Voltage measurement only

26. Fitness evaluation in GA affects:
(A) Selection probability of individuals
(B) Step response only
(C) Only inertia weight
(D) Load flow only

27. PSO can suffer from:
(A) Premature convergence to local optima
(B) Step response only
(C) Only mutation errors
(D) Load flow only

28. GA mutation is usually:
(A) Small random changes in chromosome
(B) Step response only
(C) Velocity update only
(D) Load flow only

29. PSO swarm size affects:
(A) Convergence speed and solution quality
(B) Step response only
(C) Only mutation rate
(D) Load flow only

30. GA and PSO are examples of:
(A) Metaheuristic optimization techniques
(B) Step response only
(C) Only linear programming methods
(D) Load flow only