|
| Algorithm |
|
| Items | GA | ACO | PSO | GSO |
| Year | 1975 | 1992 | 1995 | 2005 |
| Author | John Holland | Marco Dorigo | James Kennedy and Russell Eberhart | K.N. Krishnanand and Debasish Ghose |
| Optimization | Discrete optimization | Meta-heuristic optimization | Stochastic optimization | Meta-heuristic optimization |
| Parameters | Reproduction, crossover, mutation | Construct ant solutions, daemon actions (optional), and update pheromones | Current velocity, personal best, neighborhood best | Initialization, updating luciferin, movement, updating the local- decision range |
| Purpose | Find the best among others | Find the shortest path | Reach target with minimal duration | Find the local finest solution |
| Advantages | (1) Efficient means of investigating large combinatorial problems and can solve them
(2) Many orders of magnitude faster than exhaustive „brute force‟ searches | (1) Inherent parallelism
(2) Positive feedback accounts for rapid discovery of good solutions
(3) Efficient for traveling salesman problem and similar problems
(4) can be used in dynamic application (adapts to changes such as new distances, etc) | (1) PSO can be applied into both scientific research and engineering use
(2) It has no overlapping and mutation calculation
(3) The search can be carried out by the speed of the particle
(4) PSO adopts the real number code, and it is decided directly by the solution | (1) GSO can deal with highly nonlinear, multimodal optimization problems naturally and efficiently
(2) GSO does not use velocities, and there is no problem as that associated with velocity in PSO
(3) The speed of convergence of GSO is very high in probability of finding the global optimized answer |
| Disadvantages | (1) Computationally expensive
(2) Some problems require many days or weeks to run
(3) However often still faster than force
(4) Blind, to direct a GA towards optimal solution area if know | (1) Theoretical analysis is difficult
(2) Sequences of random decisions (not independent)
(3) Probability distribution changes by iteration
(4) Research is experimental rather than theoretical | (1) Tendency to a fast and premature convergence in midoptimum points
(2) The method cannot work out the problems of scattering and optimization
(3) Slow convergence in refined search step | (1) GSO is poor in high dimensional problems
(2) In GSO, the dynamic change of decision domains in the method of glowworms moving, the algorithm slows convention speed and has poor local search ability delayed in the iteration |
| Medical field | Genetic algorithm outperformed optimizes the artificial neural networks among others | ACO also optimizes the artificial neural networks for applications in medical image processing | (1) Detection of brain tumor using image segmentation (MRI)
(2) PSO used for optimize the artificial neural networks for applications in medical image processing | GSO will present new methods for future selection problems |
|
