Meta heuristics Final exam: Due May 9th
1) Solve the TSP with GA. Distance in hundreds of miles. Generate an initial population of
size 3. Use one point cross over and 1 mutation per iteration. Perform at least 5 iterations
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Metaheuristics for Spring 2012
OR 649
Prereq: OR 541 or permission of instructor
Instructor: Dr. Rajesh Ganesan, rganesan@gmu.edu, office hrs MW 3-4 PM
This course covers both basic and advanced topics on the theory and practice of metaheuristic
approache

Population-based metaheuristics
Nature-inspired
Initialize a population
A new population of solutions is generated
Integrate the new population into the current one using
one these methods by replacement which is a
selection process from the new and curre

Reservation Systems
Parallel machine environment with n jobs and m
machines
The processing time of the job has to fit within a time
window and there may or may not be slack
In an assignment problem there is no time window concept and
typically there are e

Population-based metaheuristics
Nature-inspired
Initialize a population
A new population of solutions is generated
Integrate the new population into the current one using
one these methods by replacement which is a
selection process from the new and curre

Population-based metaheuristics
Nature-inspired
Initialize a population
A new population of solutions is generated
Integrate the new population into the current one using
one these methods by replacement which is a
selection process from the new and curre

Population-based metaheuristics
Nature-inspired
Initialize a population
A new population of solutions is generated
Integrate the new population into the current one using
one these methods by replacement which is a
selection process from the new and curre

Escaping local optimas
Accept nonimproving neighbors
Iterating with different initial solutions
Multistart local search, greedy randomized adaptive search
procedure (GRASP), iterative local search
Changing the neighborhood
Tabu search and simulated anneal

Escaping local optimas
Accept nonimproving neighbors
Iterating with different initial solutions
Multistart local search, greedy randomized adaptive search
procedure (GRASP), iterative local search
Changing the neighborhood
Tabu search and simulated anneal

Escaping local optimas
Accept nonimproving neighbors
Iterating with different initial solutions
Multistart local search, greedy randomized adaptive search
procedure (GRASP), iterative local search
Changing the neighborhood
Tabu search and simulated anneal

Escaping local optimas
Accept nonimproving neighbors
Iterating with different initial solutions
Multistart local search, greedy randomized adaptive search
procedure (GRASP), iterative local search
Changing the neighborhood
Tabu search and simulated anneal

Neighborhood
Representation of solutions
Vector of Binary values 0/1 Knapsack, 0/1 IP problems
Vector of discrete values- Location , and assignment problems
Vector of continuous values on a real line continuous,
parameter optimization
Permutation sequenci

Metaheuristics
The idea: search the solution space directly. No math
models, only a set of algorithmic steps, iterative method.
Find a feasible solution and improve it. A greedy solution
may be a good starting point.
Goal: Find a near optimal solution in

Metaheuristics
Meta- Greek word for upper level methods
Heuristics Greek word heuriskein art of discovering
new strategies to solve problems.
Exact and Approximate methods
Exact
Approximate
Math programming LP, IP, NLP, DP
Heuristics
Metaheuristics used f

Midterm Metaheuristics
1.4)
Practice problems 1-4
5) For a 2 machine flow shop (jobs flow from machine 1 to 2), and total weighted tardiness
as the objective to be minimized, find a schedule.
Apply the ATC heuristic with K=1
Apply any local search. Compar