Ant Colony Optimization (ACO) is a bioinspired metaheuristic based on ants foraging used to solve different classes of problems. In this paper, we show how, using a Two-Stage approach the quality of the solutions of ACO is improved. The Two-Stage approach can be applied to different ACO. The performance of this new approach is studied in the Traveling Salesman Problem and Quadratic Assignment P...

Ant Colony Optimisation (ACO) has in the past proved suitable to solve many optimisation problems. This research explores the ability of the ACO algorithm to balance two quality metrics (length and cost) in its decision making process. Results are given for a preliminary investigation based on a series of shortest path problems. It is concluded that, for these problems at least, the solution qu...

An ACO-PSO Hybrid Algorithm Solving Path Planning Problem based on Swarm Intelligence (SI) is proposed. The problem first is described and some corresponding definitions are presented. Ant colony optimization (ACO) is used to establish the corresponding solution, and some material algorithm steps are set out. Particle swarm optimization (PSO) is applied to optimize the parameters in ACO, and pa...

Ant Colony Optimization (ACO) was first proposed to solve the Traveling Salesman Problem, and later applied to solve more problems of a combinatorial nature. Some research based on ACO to tackle continuous problems has been published, but this has not followed the original ACO metaheuristic exactly. Recently, ACOR has been proposed to solve continuous function optimization problems. We have tak...

Two novel extensions for the well known Ant Colony Optimization (ACO) framework are introduced here, which allow the solution of Mixed Integer Nonlinear Programs (MINLP). Furthermore, a hybrid implementation (ACOmi) based on this extended ACO framework, specially developed for complex non-convex MINLPs, is presented together with numerical results. These extensions on the ACO framework have bee...

In any software project management, developing third party software tools and scheduling tasks are challenging and important. Any software development projects are influenced by a large number of activities, which can greatly change the project plan. These activities may form groups of correlated tasks or event chains. Assessment planning is a crucial challenge in software engineering whose maj...

Limited amount of time and computational resources in industrial domain makes Ant Colony Optimization (ACO) a useful approach to find near optimal solutions in polynomial time for Nondeterministic Polynomial time (NP) problems. For dynamically changing graphs, such as in case of network routing and urban transportation systems which are based on Travelling Salesman Problem (TSP), the ant colony...

We propose an algorithm based on the Ant Colony Optimization (ACO) meta-heuristic for solving the Multidimensional Knapsack Problem (MKP), the goal of which is to find a subset of objects that maximizes a given objective function while satisfying some resource constraints. The proposed ACO algorithm is generic, and we propose three different instantiations, corresponding to three different ways...

The Ant Colony Optimization algorithms (ACO) are computational models inspired by the collective foraging behavior of ants. By looking at the strengths of ACO, they are the most appropriate for scheduling of tasks in soft real-time systems. In this paper, ACO based scheduling algorithm for real-time operating systems (RTOS) has been proposed. During simulation, results are obtained with periodi...

The rooted delay-constrained minimum spanning tree problem is an NP-hard combinatorial optimization problem arising for example in the design of centralized broadcasting networks where quality of service constraints are of concern. We present two new approaches to solve this problem heuristically following the concepts of ant colony optimization (ACO) and variable neighborhood search (VNS). The...