Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Successfully reported this slideshow.

Like this presentation? Why not share!

- Nature-inspired metaheuristic algor... by Xin-She Yang 3452 views
- Algorithms in nature by Sagie Davidovich 1937 views
- Analysis of Nature-Inspried Optimiz... by Xin-She Yang 2338 views
- Nature-inspired algorithms by Lars Marius Garshol 510 views
- Metaheuristic Algorithms: A Critica... by Xin-She Yang 616 views
- Recent Advances in Swarm Intelligen... by Xin-She Yang 426 views

No Downloads

Total views

3,434

On SlideShare

0

From Embeds

0

Number of Embeds

1

Shares

0

Downloads

149

Comments

0

Likes

2

No embeds

No notes for slide

- 1. SURVEILLANCE SYSTEM SUBMITTED BY: Suraj Singh Parihar (10103612) Gaurav Goel (10103591) SUBMITTED TO: Dr. Shikha Mehta (Assistant Professor – JIIT)
- 2. INTRODUCTION Biologically inspired algorithms are becoming powerful in modern numerical optimization, among these biology-derived algorithms, the multi-agent meta- heuristic algorithms such as particle swarm optimization form hot research topics in the start-of-the-art algorithm development in optimization and other application. Particle swarm optimization has many similarities with genetic algorithms, but it is much simpler because it does not use mutation/crossover operators. Instead, it uses the real-number randomness and the global communication among the swarming particles. The Genetic Algorithm transforms a population of individual objects, each with an associated fitness value, into a new generation of the population using the Darwin principle of individual of reproduction and survival of the fittest and naturally occurring genetic operation such as a cross over (recombination) and mutation. Each individual in the population represents a possible solution to a given problem. The firefly algorithm (FA) is a meta-heuristic algorithm, inspired by the flashing behaviour of fireflies .
- 3. PROBLEM STATEMENT To do a comparative study of nature inspired algorithm in between Firefly Algorithm and the Particle Swarm Optimization using Michalewicz function for the numerical optimization. Thereafter we have compared Particle swarm optimization, Genetic Algorithm and Ant Colony Optimization using the real world benchmark problem that is Travelling salesman problem
- 4. BENEFITS/NOVELITY OF THE APPLICATION Nature-inspired algorithms are among the most powerful algorithms for optimization. The PSO algorithm searches the space of the objective functions by adjusting the trajectories of individual agents, called particles, as the piecewise paths formed by positional vectors in a quasi-stochastic manner. The Fireﬂy Algorithm can be modiﬁed to solve multi objective optimization problems. In addition, the application of ﬁreﬂy algorithms in combination with other algorithms may form an exciting area for further research. Evolutionary algorithm optimizers are global optimization methods and scale well to higher dimensional problems. They are robust with respect to noisy evaluation functions, and the handling of evaluation functions which do not yield a sensible result in given period of time is straightforward.
- 5. ARCHITECTURE
- 6. USE CASE DIAGRAM
- 7. CONTROL FLOW DIAGRAM
- 8. TESTING REQUIRED Type of Test Will it be EXPLANATIONS Software Component performed? Requirement Yes Requirement testing is testing the Manual work, need to Testing requirements whether they are plan out all the software feasible or not. Because a project requirements, time needed depends on a number of factors like to develop, technology to time, resources, budget etc. Before be used etc. we start working on a project it‘s important to test these requirements. Unit Yes Testing by which individual units of Manual check is required source code are tested to determine if they are fit for use. Integration Yes Testing wherein individual Compiling various classes components are combined and tested and testing them as one as a group. single code.
- 9. LIMITATION OF THE APPLICATION Genetic algorithm applications in controls which are performed in real time are limited because of random solutions and convergence, in other words this means that the entire population is improving, but this could not be said for an individual within this population. Application doesn’t consider the camera angle. Firefly algorithm suffers greatly from diminishing returns once the swarm size grows past a certain point, or when the solution space grows immensely large. Any extra installation cost is not considered in the final cost displayed.
- 10. FINDINGS Firefly Algorithm : Parallelization - The parts of computation that cannot be parallized are the portions that determine the next movement of a firefly. They are dependent on the current position of the firefly. As a stochastic algorithm, it is impossible to predict future moves of a firefly so there is no opportunity to allow pre-cacheing of values. Scalability : The primary drawback to FA is the required communication between fireflies. This algorithm requires every firefly in the swarm to know the fitness of every other firefly in the swarm at the end of each iteration of moving and updates. However, if we use a central node to act as a communications hub, we can then drastically reduce the number of communications required at the end of each generation of the algorithm. Particle Swarm Optimization : Parallelization : A particle will only have to touch its own information and the stand- alone nature of the algorithm is only broken by the communication between a particle and coordinating node to update the Swarm Best Fitness. Scalability : The only information that is required to be disseminated to the rest of the swarm is the swarm's best fitness and associated parameters. That will then allow the swarm particles to independently update their own positions and velocity with no other outside information required
- 11. CONCLUSION We studied new ﬁreﬂy algorithm and analysed its similarities and diﬀerences with particle swarm optimization. We then implemented and compared these algorithms. Our simulation results for ﬁnding the global optima of various test functions suggest that particle swarm often outperforms traditional algorithms such as genetic algorithms, while the new ﬁreﬂy algorithm is superior to PSO in terms of both eﬃciency and success rate. This implies that FA is potentially more powerful in solving NP-hard problems which will be investigated further in future studies. The basic ﬁreﬂy algorithm is very eﬃcient, but we can see that the solutions are still changing as the optima are approaching. It is possible to improve the solution quality by reducing the randomness gradually. A further improvement on the convergence of the algorithm is to vary the randomization parameter α so that it decreases gradually as the optima are approaching. These could form important topics for further research. Furthermore, as a relatively straightforward extension, the Fireﬂy Algorithm can be modiﬁed to solve multi objective optimization problems. In addition, the application of ﬁreﬂy algorithms in combination with other algorithms may form an exciting area for further research. This time we implemented genetic algorithm, and other nature inspired algorithms which are particle swarm optimisation and ant colony optimisation on real-time problem ,TRAVELLING SALESMAN PROBLEM which is a NP hard problem and many algorithms have been implemented and we found out the PSO is the best out of all three implemented.
- 12. FUTURE WORK Use more varied functions for comparing the efficiency of different evolutionary algorithms. Implement more different evolutionary algorithms like SFLA and Ant colony optimization and compare the accuracy of results. Try to implement these algorithms using more datasets.
- 13. REFERENCES [1] Bonabeau E., Dorigo M., Theraulaz G., Swarm Intelligence: From Natural to Artiﬁcial Systems. Oxford University Press, (1999) [2] Deb. K., Optimisation for Engineering Design, Prentice-Hall, New Delhi, (1995). [3] Gazi K., and Passino K. M., Stability analysis of social foraging swarms, IEEE Trans. Sys. Man. Cyber. Part B - Cybernetics, 34, 539-557 (2004). [4] Goldberg D. E., Genetic Algorithms in Search, Optimisation and Machine Learning, Reading, Mass.: Addison Wesley (1989). [5] Kennedy J. and Eberhart R. C.: Particle swarm optimization. Proc. of IEEE International Conference on Neural Networks, Piscataway, NJ. pp. 1942-1948 (1995). [6] Kennedy J., Eberhart R., Shi Y.: Swarm intelligence, Academic Press, (2001). [7] Passino K. M., Biomimicrt of Bacterial Foraging for Distributed Optimization, University Press, Princeton, New Jersey (2001). [8] Shilane D., Martikainen J., Dudoit S., Ovaska S. J., A general framework for statistical performance comparison of evolutionary computation algo- rithms, Information Sciences: an Int. Journal, 178, 2870-2879 (2008).

No public clipboards found for this slide

Be the first to comment