Ant colony optimization (ACO) is nowadays one of the most promising metaheuristics, and an increasing amount of research has been devoted to its empirical and theoretical analysis. Some authors believe that the performance of ant colony optimization depends somehow on the scale of the problem instance under analysis. The issue has been recently raised explicitly [1] and the hyper-cube framework...

This research merges the hierarchical reinforcement learning (HRL) domain and the ant colony optimization (ACO) domain. The merger produces a HRL ACO algorithm capable of generating solutions for both domains. This research also provides two specific implementations of the new algorithm: the first a modification to Dietterich’s MAXQ-Q HRL algorithm, the second a hierarchical ACO algorithm. Thes...

In this article we intend to show the use of well-known evolutionary computation techniques Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in an indoor propagation problem. Although these algorithms employ different strategies and computational efforts, they also share certain similarities. Their performance is compared with a genetic algorithm (GA), which is used as refere...

In this paper, a multi-objective reconfiguration problem has been solved simultaneously by a modified ant colony optimization algorithm. Two objective functions, real power loss and energy not supplied index (ENS), were utilized. Multi-objective modified ant colony optimization algorithm has been generated by adding non-dominated sorting technique and changing the pheromone updating rule of ori...

This paper proposes a Reinforcement Self-Organizing Fuzzy Control method using Ant Colony Optimization (RSOFCACO). Only reinforcement signals are required when using the RSOFC-ACO for fuzzy controller design. There are no fuzzy rules initially in RSOFC-ACO. An online fuzzy clustering method is used to generate fuzzy rules automatically during control process. The fuzzy clustering method flexibl...

The research outlined in this paper aims the development of a methodology to arrive at critical path calculations in construction networks using Ant Colony Optimization (ACO) algorithms. Ant Colony Optimization is a population-based, artificial multiagent, general-search technique for the solution of difficult combinatorial problems. The method’s theoretical roots are based on the behaviour of ...

An Ant Colony Optimization (ACO) algorithm is proposed for optimal tree-structured natural gas distribution network. Design of pipelines, facilities, and equipment systems are necessary tasks to configure an optimal natural gas network. A mixed integer programming model is formulated to minimize the total cost in the network. The aim is to optimize pipe diameter sizes so that the location-alloc...

Regression Testing is an inevitable and a very costly activity to be performed, often in a time and resource constrained environment. Thus we use techniques like Test Case Selection and Prioritization, to select and prioritize a subset from the complete test suite, fulfilling some chosen criteria. Ant Colony Optimization (ACO) is a technique based on the real life behavior of ants. This paper p...

Data Allocation Problem (DAP) incorporates Allocation and Replication methodologies which are involved during the phase of Distributed Database design. Accessibility and availability are the thriving factors for better design of Distributed Databases. High degree of Accessibility and Availability are the outcomes of effective methodologies for Allocation and Replication. This paper proposes a m...

The problem of optimal planning of multiple sources of distributed generation (DG) in distribution networks is treated in this paper using an improved Ant Colony Optimization algorithm (ACO). This objective of this problem is to determine the DG optimal size and location that in order to minimize the network real power losses. Considering the multiple sources of DG, both size and location are s...