Metaheuristics

ossein jain
ossein jainStudent à mangalayatan university
Ossein Jain
Mtech (CS)
By-
What is ?
In simple words:
• It is a repetitive process used to solve a
problem that even the toughest algorithm,
with greatest optimization powers, fail to be
effective or efficient.
Metaheuristics
Artificial Neural Networks
• Most Basic Technique of AI-“ANN (Artificial neural
Networks)” , based on Human nervous system
• Processes information with the help of
– nodes
– synapses(inputs)
– weights(signals)
– mathematical formulas (calculating neuron output signal,
scalar product i.e. net ).
•Neuron Communicating through
input, hidden and output layers
and weighted connections
(feedforward, feedback, lateral and
time-delayed).
ANN continued....
• An ANN problems is solved through
• Training
• Generalization (Testing)
• Implementation.
• ANN is applied in Robotics, Data Processing, Functional
approximation and pattern sequence recognition.
• Disadvantage is that because the network finds out how to solve
the problem by itself and its operation can be unpredictable
Tabu Search (TS)
• Taking a solution as starting point (or local minima), uses a
Tabu list for recent moves and a Tabu memory to prevent it
from repeating, making a “Solution space - Diversification”.
When we need better version of better
Initialize
solution
Populate
candidate
list of
solutions
Evaluate
solutions
Choose
best
admissible
solution
Stopping
conditions
satisfied?
Update
aspiration
conditions
Update
tabu Final
soution
yes
no
Tabu search
• Many factors play a role like size, adaptability of TS memory and
list, local search procedure, form of moves and stopping rule.
Evolutionary Algorithm
• When we have a bundle of solutions and we know not to give up, we
use Evolutionary Algorithm
• Methods like Genetic programming, Evolutionary strategies, Genetic
Algorithms are used.
• Unlike TS it has many “local optimas”.
• EAs are used in like wire routing, scheduling, image processing, game
playing, Knapsack problem, etc.
mixing and matching the best part of each solution
Genetic Algorithm
• GA is an adaptive search and optimization using
random searches to find “local optimal
solutions”, so as to safeguard some critical info.
•Mutate, then select
the fittest solution and
repeat until the best
one is found.
• Popular for
Bankruptcy prediction,
residual estimation,
vehicle routing, etc.
Differential Evolution
• It makes a trial vectors using existing solutions and
mixes it with successful ones, further improved by
mutation, crossover and selection operators.
• DE Algorithm:
Advanced Version of GA that focuses on Mutation
• Best for numerical problems, used to find
approximate solutions where problems are non
linear, non-differentiable with many local minima and
constraints
Simulated Annealing
• A worse variation is accepted as the new solution with a
probability that decreases as the computation proceeds.
• The slower the cooling schedule, or rate of decrease, the
more likely the algorithm is to find an optimal or near-
optimal solution
• It is Useful in zoning, routing, facility layout problems.
When in need to find random variations of a present
solution, accepting the worst one
Swarm Intelligence
• Swarm Intelligence (SI), follows 5 principles:
proximity, quality, diverse response, stability
and adaptability.
Inspired from insects and their coordinated interactive
teamwork
Particle Swarm Optimization (PSO)
• Particle Swarm Optimization (PSO) is based
on population on concept of bird flocking.
• It is easy parallelization for concurrent
processing, derivative free and solve
convergence is very effective.
• Implemented in Parkinson’s disease
identification, electric power distribution,
biometrics, processing biochemistry, etc
Ant colony Optimization (ACO)
• Ant colony Optimization (ACO) is inspired by foraging and
colonization of ants.
• It includes trailing like ants, making progressive solutions,
using attractiveness and trail levels.
• ACO algorithms like Ant system, Ant colony system, ma-
min ant system, rank based ant system and best-worst
ant system are summarised.
• In Ant System, the contributions by ants depends
on quality of solution and better the trail contribution,
better the solution.
Ant colony Optimization (ACO)…
• To improve the algorithmic quality, performance and
behaviour ACS, enhanced AS through pseudo-random
proportion rule, updating pheromone trail offline through
daemon, hence not every ant follows the same ant.
• MMASas best enhancement of AS, ranking ant in decreasing
order of respective solutions.
• Pheromone which restricts them was influenced by rand and
quality of solution, so was the connection.
• BWAS using the transition rule and Pheromone
evaporation techniques, was also a good extension to
AS.
• ACO was largely useful in assignment, scheduling,
vehicle routing, travelling salesman and energy
forecasting
FUTURE RESEARCH DIRECTIONS
• However useful these techniques were, they did not guarantee an
optimal solution, overhead being , complex function , parameters and
constraints, also lack of standards of testing and comparison of
methods makes it rather in need for improvement.
• As in latest 5 years, Metaheuristic have gained importance through
textbooks, conferences, success in application many real-world
problems.
• ANNs and GAs softwares being available are more popular.
• As the better optimal solution are much needed for the scarcity of
time, money and resources, so will just keep on increasing in near
future .Thus the number of methods for Metaheuristic.
• All we need to do is, develop softwares for all such useful methods to
be better applied in the real-world.
Metaheuristics
1 sur 17

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Metaheuristics

  • 2. What is ? In simple words: • It is a repetitive process used to solve a problem that even the toughest algorithm, with greatest optimization powers, fail to be effective or efficient.
  • 4. Artificial Neural Networks • Most Basic Technique of AI-“ANN (Artificial neural Networks)” , based on Human nervous system • Processes information with the help of – nodes – synapses(inputs) – weights(signals) – mathematical formulas (calculating neuron output signal, scalar product i.e. net ). •Neuron Communicating through input, hidden and output layers and weighted connections (feedforward, feedback, lateral and time-delayed).
  • 5. ANN continued.... • An ANN problems is solved through • Training • Generalization (Testing) • Implementation. • ANN is applied in Robotics, Data Processing, Functional approximation and pattern sequence recognition. • Disadvantage is that because the network finds out how to solve the problem by itself and its operation can be unpredictable
  • 6. Tabu Search (TS) • Taking a solution as starting point (or local minima), uses a Tabu list for recent moves and a Tabu memory to prevent it from repeating, making a “Solution space - Diversification”. When we need better version of better Initialize solution Populate candidate list of solutions Evaluate solutions Choose best admissible solution Stopping conditions satisfied? Update aspiration conditions Update tabu Final soution yes no
  • 7. Tabu search • Many factors play a role like size, adaptability of TS memory and list, local search procedure, form of moves and stopping rule.
  • 8. Evolutionary Algorithm • When we have a bundle of solutions and we know not to give up, we use Evolutionary Algorithm • Methods like Genetic programming, Evolutionary strategies, Genetic Algorithms are used. • Unlike TS it has many “local optimas”. • EAs are used in like wire routing, scheduling, image processing, game playing, Knapsack problem, etc. mixing and matching the best part of each solution
  • 9. Genetic Algorithm • GA is an adaptive search and optimization using random searches to find “local optimal solutions”, so as to safeguard some critical info. •Mutate, then select the fittest solution and repeat until the best one is found. • Popular for Bankruptcy prediction, residual estimation, vehicle routing, etc.
  • 10. Differential Evolution • It makes a trial vectors using existing solutions and mixes it with successful ones, further improved by mutation, crossover and selection operators. • DE Algorithm: Advanced Version of GA that focuses on Mutation • Best for numerical problems, used to find approximate solutions where problems are non linear, non-differentiable with many local minima and constraints
  • 11. Simulated Annealing • A worse variation is accepted as the new solution with a probability that decreases as the computation proceeds. • The slower the cooling schedule, or rate of decrease, the more likely the algorithm is to find an optimal or near- optimal solution • It is Useful in zoning, routing, facility layout problems. When in need to find random variations of a present solution, accepting the worst one
  • 12. Swarm Intelligence • Swarm Intelligence (SI), follows 5 principles: proximity, quality, diverse response, stability and adaptability. Inspired from insects and their coordinated interactive teamwork
  • 13. Particle Swarm Optimization (PSO) • Particle Swarm Optimization (PSO) is based on population on concept of bird flocking. • It is easy parallelization for concurrent processing, derivative free and solve convergence is very effective. • Implemented in Parkinson’s disease identification, electric power distribution, biometrics, processing biochemistry, etc
  • 14. Ant colony Optimization (ACO) • Ant colony Optimization (ACO) is inspired by foraging and colonization of ants. • It includes trailing like ants, making progressive solutions, using attractiveness and trail levels. • ACO algorithms like Ant system, Ant colony system, ma- min ant system, rank based ant system and best-worst ant system are summarised. • In Ant System, the contributions by ants depends on quality of solution and better the trail contribution, better the solution.
  • 15. Ant colony Optimization (ACO)… • To improve the algorithmic quality, performance and behaviour ACS, enhanced AS through pseudo-random proportion rule, updating pheromone trail offline through daemon, hence not every ant follows the same ant. • MMASas best enhancement of AS, ranking ant in decreasing order of respective solutions. • Pheromone which restricts them was influenced by rand and quality of solution, so was the connection. • BWAS using the transition rule and Pheromone evaporation techniques, was also a good extension to AS. • ACO was largely useful in assignment, scheduling, vehicle routing, travelling salesman and energy forecasting
  • 16. FUTURE RESEARCH DIRECTIONS • However useful these techniques were, they did not guarantee an optimal solution, overhead being , complex function , parameters and constraints, also lack of standards of testing and comparison of methods makes it rather in need for improvement. • As in latest 5 years, Metaheuristic have gained importance through textbooks, conferences, success in application many real-world problems. • ANNs and GAs softwares being available are more popular. • As the better optimal solution are much needed for the scarcity of time, money and resources, so will just keep on increasing in near future .Thus the number of methods for Metaheuristic. • All we need to do is, develop softwares for all such useful methods to be better applied in the real-world.

Notes de l'éditeur

  1. heuristic is a technique designed for finding an approximate solution when classic methods fail to find any exact solution. Metaheuristics; learning strategies are used to structure information in order to find efficiently near-optimal solutions.
  2. These basically consist of inputs (like synapses), which are multiplied by weights (strength of the respective signals), and then computed by a mathematical function which determines the activation of the neuron . There are three types of neuron layers: input, hidden and output layers. Two layers of neuron communicate via a weight conection network. There are four types of weighted connections: feedforward, feedback, lateral, and time-delayed connections. lateral - winners-takes-all circuit, which serves the important role of selecting the winner . time-delayed)- more suitable for temporal pattern recognitions.
  3. disadvantage ;is that because the network finds out how to solve the problem by itself and its operation can be unpredictable
  4. "that restaurant's menu lacks diversification; every day it is the same
  5. they involve a search from a “population” of solutions, not from a single point.
  6. Mutate; change. Bankruptcy; Someone who has insufficient assets to cover their debts. failure Residual; indicating a remainder
  7. Annealing; Hardening something by heat treatment. Simulated;Reproduce someone's behavior or looks. algorithm is a technique to find a good solution of an optimization problem using a random variation of the current solution. A worse variation is accepted as the new solution with a probability that decreases as the computation proceeds. The slower the cooling schedule, or rate of decrease, the more likely the algorithm is to find an optimal or near-optimal solution