Welcome back! In my previous post, I introduced the basics of Ant Colony Optimization (ACO). On this installment, we’ll delve into implementing the ACO algorithm from scratch to tackle two distinct problem types.
The issues we’ll be addressing are the Traveling Salesman Problem (TSP) and the Quadratic Project Problem (QAP). Why these two? Well, the TSP is a classic challenge, and ACO happens to be an efficient algorithm for locating probably the most cost-efficient path through a graph. However, the Quadratic Project Problem represents a unique class of problems related to optimizing the arrangement of things, and on this post, I aim to display that ACO generally is a worthwhile tool for solving such assignment-related problems as well. This versatility makes the ACO algorithm applicable to a wide selection of problems. Finally, I’ll share some suggestions for achieving improved solutions more rapidly.
TSP is easy to explain but can pose a big challenge find an answer. Here’s the essential definition: you’re tasked with discovering the shortest route that visits all nodes in a graph. This problem falls into the category of NP-hard problems, which means that should you try to explore all possible routes, it could take an impractical period of time to seek out the answer. As an alternative, a more practical approach is to hunt a high-quality solution inside an affordable timeframe, and that’s precisely what we’ll accomplish using ACO.
Problem Definition
With the next code, we will create a TSP instance with a given variety of nodes:
import itertools
import math
import random
from typing import Tupleimport networkx as nx
import networkx.algorithms.shortest_paths.dense as nxalg
class TSP:
"""
Creates a TSP problem with a certain variety of nodes
"""
def __init__(self, nodes: int = 30, dimensions: Tuple[int, int] = (1000, 1000), seed: int = 5):
if seed:
random.seed(seed)
graph = nx.Graph()
nodes_dict = dict()
for i in range(nodes):
nodes_dict[i] =…