Quantum Dynamic Mechanism-based Parallel Ant Colony Optimization Algorithm

Abstract

A novel Parallel Ant Colony Optimization Algorithm based on Quantum dynamic mechanism for traveling salesman problem (PQACO) is proposed. The use of the improved 3-opt operator provides this methodology with superior local search ability; several antibody diversification schemes were incorporated into the PQACO in order to improve the balance between exploitation and exploration. We describe the quantum dynamic mechanism and analysis the technology of improving performance, the efficiency of the approach has been illustrated by applying to TSP benchmark instances Chn144.

Imitation of Honeybee Aggregation with Collective Behavior of Swarm Robots

“…Ant colony optimization and particle swarm optimization are the most successful applications of swarm in problem solving that have been used in variety of applications. 7,8,9 Aggregation is a natural behavior of social insects and animals to find food or path. 10 Usually, environmental cues are used as a marker for an optimal zone such as humidity for sow bugs or light and temperature for flies.…”

Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm

“…Li et al [16] proposed a quantum ant colony algorithm (QACA) that combined quantum computing and the ant colony algorithm for continuous space optimization. You et al [17] proposed a novel parallel ant colony optimization algorithm based on a quantum dynamics mechanism (PQACO). An improved quantum ant colony algorithm was proposed for the optimization of evacuation paths from dangerous areas to safe areas [18].…”

“…The quantum bit (Q-bit) is used to encode the pheromone in the ACA to obtain the quantum pheromone, and the ant movement is determined based on the concentration of the quantum pheromone on the path. Compared to the existing QACA [16][17][18][19], the phase of the quantum ant colony is transformed by an adaptive quantum rotation gate, and the quantum pheromone is updated by local and global update rules in the IQACA.…”

Improving exploration in Ant Colony Optimisation with antennation

“…ACO adaptations designed to improve either exploration or exploitation include: resetting pheromone levels [17,22], introducing multiple colonies [24,27], using an 'opposing' pheromone map [11,14], hybridising ACO with other metaheuristics [10,21,25] and allowing both the best and worst solutions to change pheromone levels during the update process [28].…”