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‎README

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JONATAS RENAN CAMILO ALVES
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[email protected]
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===========================
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Exemplos de execução para o problema de LP ACO na pasta scripts
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Documentação completa em documentation/tp2-jonatasrenan.pdf
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Parameters:
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--min_runs value : valor mínimo de runs
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--max_runs value : valor máximo de runs
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--step_runs value : tamanho do passo de runs
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--min_ants value : valor mínimo de ants
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--max_ants value : valor máximo de ants
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--step_ants value : tamanho do passo de ants
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--iterations value : número de iterações
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--initial_pheromone_amount value : valor de feromônio inicial
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--ant_pheromone_deposit_amount value : valor do padrão do depósito de feromônio quando a formiga caminha
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--pheromone_evaporation_rate value : taxa de evaporação do feromônio
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--daemon_pheromone_deposit_amount value : valor de depósito da melhor formiga
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--max_pheromone_threshold value : valor máximo de feromônio na aresta
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--min_pheromone_threshold value : valor mínimo de feromônio na aresta
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--alpha value : Parâmetro da função de seleção de aresta pela formiga relacionado ao feromônio
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--beta value : Parâmetro da função de seleção de aresta pela formiga relacionado ao peso da aresta
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--exploitation_factor value
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--input_file_path file
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--optimum_cost value
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--output_header
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--output_file value
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--description description

‎aco.rb

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require 'solution'
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require 'ant'
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class ACO
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attr_accessor :adjacency_matrix, :pheromone_matrix,
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:neighbour_count,
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:alpha, :beta, :total_vertices,
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:initial_pheromone_amount, :ant_pheromone_deposit_amount,
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:daemon_pheromone_deposit_amount,
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:pheromone_evaporation_rate,
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:min_pheromone_threshold, :max_pheromone_threshold,
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:global_best_solution,
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:num_iterations, :num_ants,
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:ants, :start_node, :destination_vertex, :exploitation_factor
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def initialize(input, num_iterations, num_ants, initial_pheromone_amount, ant_pheromone_deposit_amount,
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pheromone_evaporation_rate, daemon_pheromone_deposit_amount, max_pheromone_threshold, min_pheromone_threshold, alpha, beta,
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exploitation_factor)
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@num_iterations = num_iterations
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@num_ants = num_ants
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@initial_pheromone_amount = initial_pheromone_amount
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@ant_pheromone_deposit_amount = ant_pheromone_deposit_amount
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@pheromone_evaporation_rate = pheromone_evaporation_rate
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@daemon_pheromone_deposit_amount = daemon_pheromone_deposit_amount
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@max_pheromone_threshold = max_pheromone_threshold
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@min_pheromone_threshold = min_pheromone_threshold
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@alpha = alpha
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@beta = beta
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@exploitation_factor = exploitation_factor
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@ants = Array.new
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@neighbour_count = Hash.new
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@pheromone_matrix = Hash.new{ |h,k| h[k] = Array.new(&h.default_proc) }
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@total_vertices = input.num_vertexes
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@start_node = input.start_node
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@end_node = input.end_node
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@adjacency_matrix = input.nodes
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#vetor de número de vizinhos do vertice
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#inicializa matriz de feromonio com o valor inicial previsto
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@total_vertices.times do |i|
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@neighbour_count[i]=0
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@total_vertices.times do |j|
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if @adjacency_matrix[i][j] > 0
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@neighbour_count[i]+=1
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@pheromone_matrix[i][j] = initial_pheromone_amount
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else
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@pheromone_matrix[i][j] = 0
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end
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end
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end
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@global_best_solution = Solution.new (@adjacency_matrix)
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end
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def run
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@num_iterations.times do |iteration|
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local_best_solution = Solution.new(@adjacency_matrix)
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@num_ants.times do |num_ant|
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ant = Ant.new(@adjacency_matrix, @pheromone_matrix, @alpha, @beta,
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@ant_pheromone_deposit_amount, @neighbour_count,
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@exploitation_factor, @start_node, @end_node)
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ant.find_solution(@start_node, true)
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if ant.solution.valid_solution?(@start_node, @end_node)
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ant.deposit_pheromone(@max_pheromone_threshold)
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if (ant.solution.cost > local_best_solution.cost)
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local_best_solution = ant.solution
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end
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global_pheromone_update(local_best_solution)
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end
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end
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if local_best_solution.valid_solution?(@start_node, @end_node)
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if (local_best_solution.cost > @global_best_solution.cost) then @global_best_solution = local_best_solution end
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end
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end
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end
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def global_pheromone_update (local_best_solution)
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@total_vertices.times do |i|
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@total_vertices.times do |j|
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if i!=j
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value = @pheromone_matrix[i][j]
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value = value * (1-@pheromone_evaporation_rate)
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if value > @min_pheromone_threshold
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@pheromone_matrix[i][j]=value
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end
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end
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end
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end
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#depositar mais feromonio na melhor solução
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local_best_solution.node_list.size-1.times do |i|
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v1 = pheromone_matrix[i][i+1]
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v2 = pheromone_matrix[i+1][i]
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v1 += daemon_pheromone_deposit_amount
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v2 += daemon_pheromone_deposit_amount
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if (v1 < max_pheromone_threshold)
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pheromone_matrix[local_best_solution.node_list[i]][local_best_solution.node_list[i+1]] = v1
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pheromone_matrix[local_best_solution.node_list[i+1]][local_best_solution.node_list[i]] = v2
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end
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end
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end
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end

‎ant.rb

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class Ant
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attr_accessor :visited_states, :neighbour_count,
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:solution,
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:adjacency_matrix, :pheromone_matrix,
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:alpha, :beta,
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:pheromone_deposit_amount,
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:exploitation_factor,
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:start_node, :end_node
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def initialize(adjacency_matrix, pheromone_matrix, alpha, beta,
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pheromone_deposit_amount, neighbour_count, exploitation_factor,
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start_node, end_node)
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@adjacency_matrix = adjacency_matrix
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@pheromone_matrix = pheromone_matrix
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@solution = Solution.new(adjacency_matrix)
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@alpha = alpha
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@beta = beta
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@pheromone_deposit_amount = pheromone_deposit_amount
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@neighbour_count = neighbour_count
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@exploitation_factor = exploitation_factor
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@start_node = start_node
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@end_node = end_node
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@visited_states = Array.new(@adjacency_matrix.size){false}
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end
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def size
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return @adjacency_matrix.size
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end
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def find_available_neighbours (current_node)
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neighbours = Array.new
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@adjacency_matrix.size.times do |neighbour|
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if @adjacency_matrix[current_node][neighbour] > 0
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if @visited_states[neighbour] == false
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neighbours.insert(-1,neighbour)
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end
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end
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end
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return neighbours
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end
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def get_random_node_index(probability_distribution)
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cumulative_distribution = Array.new
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index = 0
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sum = 0.0
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cumulative_distribution.insert(-1,0.0)
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probability_distribution.each do |p|
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sum += p
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cumulative_distribution.insert(-1,sum)
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end
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r = rand(0.0 .. 1.0)
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cumulative_distribution.size.times do |i|
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if r > cumulative_distribution[i] and r <= cumulative_distribution[i+1]
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index = i
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break
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end
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end
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return index
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end
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def get_next_node(current_node, neighbours)
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st_max_neighbour = Struct.new(:id, :value)
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max_neighbour = st_max_neighbour.new(0,0)
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sum = 0
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probability_distribution = Array.new
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#se somente um vizinho, retorna ele
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if (neighbours.size == 1) then return neighbours[0] end
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neighbours.each do |neighbour|
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edge_cost = @adjacency_matrix[current_node][neighbour]
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pheromone = @pheromone_matrix[current_node][neighbour]
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product = pheromone**alpha * edge_cost**beta
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if product > max_neighbour.value then max_neighbour = st_max_neighbour.new(neighbour, product) end
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sum +=product
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end
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if (rand (0.0 .. 1.0)) <= exploitation_factor then return max_neighbour.id end
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neighbours.each do |neighbour|
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edge_cost = @adjacency_matrix[current_node][neighbour]
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pheromone = @pheromone_matrix[current_node][neighbour]
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probability = pheromone**alpha * edge_cost**beta / sum
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probability_distribution.insert(-1,probability)
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end
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return neighbours[get_random_node_index(probability_distribution)]
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end
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def update_visited
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@solution.node_list.each { |node| visited_states[node] = true }
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end
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def find_solution(current_node, first_call)
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@visited_states[current_node] = true
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@solution.append_node(current_node)
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possible_neighbours = find_available_neighbours(current_node)
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if possible_neighbours.empty? then return end
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if current_node == @end_node then return end
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next_node = get_next_node(current_node, possible_neighbours)
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find_solution(next_node, false)
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if first_call
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possible_neighbours = find_available_neighbours(current_node)
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if possible_neighbours.empty? then return end
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next_node= get_next_node(current_node, possible_neighbours)
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@solution.reverse_order
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find_solution(next_node, false)
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@solution.reverse_order
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end
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end
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def deposit_pheromone(max_pheromone_threshold)
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@solution.size-1.times do |i|
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v1 = @pheromone_matrix[@solution.node_list[i]][@solution.node_list[i+1]]
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v2 = @pheromone_matrix[@solution.node_list[i+1]][@solution.node_list[i]]
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v1 += @pheromone_deposit_amount
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v2 += @pheromone_deposit_amount
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if v1<max_pheromone_threshold then @pheromone_matrix[@solution.node_list[i]][@solution.node_list[i+1]] =v1 end
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if v2<max_pheromone_threshold then @pheromone_matrix[@solution.node_list[i+1]][@solution.node_list[i]] =v2 end
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end
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end
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end
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‎documentation/.texpadtmp/img1.aux

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\bibcite{}{1}
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\bibcite{}{4}

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