04_Comparison of Met a Heuristic Algorithms
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Transcript of 04_Comparison of Met a Heuristic Algorithms
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4. Comparison of Metaheuristic
In the name of GodIn the name of God
Comparison of Metaheuristic Algorithms
Fall 2010
Instructor: Dr. Masoud Yaghini
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Emergence Time
GA: 1975
ACO: 1991
TS: 1986
SA: 1986
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Innovator
GA: John Holland
ACO: Marco Dorigo
TS: Fred Glover
SA: A. Kirkpatrick
Comparison of Metaheuristic Algorithms
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Source of Inspiration
GA: Evolution Principle
ACO: The foraging behavior of ant colonies
TS: a prohibition imposed by social custom as aprotective measure or something banned as
constituting a risk.
Comparison of Metaheuristic Algorithms
SA: physical annealing
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Originally Purpose
GA: for solving combinatorial problems
ACO: for solving combinatorial problems
TS: for solving combinatorial problems
SA: for solving combinatorial problems
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Population or Single solution orientation
GA: Population-based algorithm
ACO: Population & Single based
TS: Single based
SA: Single solution
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Using Memory
GA: Memory less
ACO: Using memory to store amount of pheromones
TS: Short term (tabu lists), midterm and long termmemory
SA: Memor less
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Generating Initial Solution
GA: Random
ACO: Random / Local search
TS: Local search
SA: Random
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Finding Neighbor Solutions
GA: Random search of neighbouring solutions
using mutation operator
ACO: Random search of neighbouring solutions using random proportional rule
TS: Deterministic/random search of nei hbourin
Comparison of Metaheuristic Algorithms
solutions
SA: Random search of neighbouring solutions
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Number of Neighbour Solutions Considered at Each Move
GA: One neighbour solution
ACO: n neighbour solutions
TS: n neighbour solutions
SA: One neighbour solution
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Finding Local Optimum
GA: Mutation operator
ACO: Accumulation pheromone on better solutions
TS: Based on a local search
SA: Decreasing temperature and limiting search space
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Escaping from Local Optimum
GA: Random search of search space
Using crossover operator
ACO: Evaporation mechanism TS: Deterministic acceptance of non-improving
solutions & jump to unvisited search space
Comparison of Metaheuristic Algorithms
Using tabu lists and diversification
SA: Probabilistic acceptance of non-improving
solutions
based on acceptance function and temperature parameter
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Comparison of Metaheuristic Algorithms