FINAL MATHS AEM PROJECT PRESENTATION.pptx

Computer program for solving a unconstrained Nonlinear Programming problem through any of the metaheuristic algorithm

1. MATHS PRESENTATION PRESENTED BY:- GAURAV DEV PIYUSH PAREWA VAIBHAV HARSH LAVKUSH JATAV DEPARTMENT OF ELECTRICAL ENGINEERING IN THE GUIDANCE OF :- Dr. PRIYANKA ASHOK HARJULE [ Ass.proffessor (mathematics )]

2. CONENT :- 1] Introduction 2] How genetic algorithm works? 3] Problem specification 4] Source code 5] Advantages and disadvantages of genetic algorithm 6] Conclusion

3. PROJECT DESCRIPTION Computer program for solving a unconstrained Nonlinear Programming problem through any of the metaheuristic algorithm.

4. Complexity in optimization problems Optimization problems of today life are becoming complex . Conventional methods consumes too much time to solve these problems . Some examples are :- 1.Airline scheduling problems. 2.Electric power and gas distribution. 3.Travelling salesman problem.

5. What is Genetic Algorithm? • The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals from the current population to be parents and uses them to produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution. •

6. HOW GENETIC ALGORITHM WORKS ?

7. PROBLEM SPECIFICATION : OPTIMIZE THE NON LINEAR PROGRAMMING PROBLEM USING GENETIC ALGORITHM . F(x) = x^2

8. SOURCE CODE :

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10. OUTPUT :-

11. ADVANTAGES OF GENETIC ALGORITHM 1. EXPLORATION OF SEARCH SPACE 2. FLEXIBILITY 3. ADAPTABILITY 4.PARALLEL PROCESSING 5.GLOBAL OPTIMISATION

12. DISADVANTAGES OF GENETIC ALGORITHM 1. COMPUTATIONAL COMPLEXITY 2. DIFFICULTY IN TUNING PARAMETERS 3. DEPENDENCE ON RANDOMNESS 4. RISK OF PREMATURE CONVERGENCE 5. LIMITED UNDERSTANDING OF RESULTS

13. CONCLUSION :- GENETIC ALGORITHMS ARE A VERSATILE OPTIMIZATION TECHNIQUE THAT CAN FIND SOLUTIONS TO A WIDE RANGE OF COMPLEX PROBLEMS . THEY ARE PARTICULARLY USEFUL WHEN OTHER OPTIMIZATION METHODS MAY NOT BE SUITABLE OR WHEN AN ANALYTICAL SOLUTION IS NOT READILY AVAILABLE.

14. THANK YOU