Many real-world optimization problems are subject to dynamic environments that require an optimization algorithm to track the optimum during changes. Ant colony optimization (ACO) algorithms have proved to be powerful methods to address combinatorial dynamic optimization problems (DOPs), once they are enhanced properly. The integration of ACO algorithms with immigrants schemes showed promising ...

INTRODUCTION Acute coronary occlusion (ACO) may also present as non-ST elevation myocardial infarction (NSTEMI) and thus veil the real threat. AIM Based on combined analysis of electrocardiography and echocardiography findings, we aimed to describe profile of NSTEMI patients at increased risk of ACO. MATERIAL AND METHODS It was a retrospective study that included patients referred for cardi...

The crowding population based ant colony optimization algorithm (CP-ACO) uses a different pheromone update in comparison to other ACO algorithms. In this paper, crowding population based ant colony optimization algorithm is proposed to solve service restoration problem. The most notable achievement featured in this paper is run time reduction of algorithm to solve the service restoration task. ...

  In this paper an Ant Colony (ACO) algorithm is developed to solve aircraft recovery while considering disrupted passengers as part of objective function cost. By defining the recovery scope, the solution always guarantees a return to the original aircraft schedule as soon as possible which means least changes to the initial schedule and ensures that all downline affects of the disruption are ...

In this paper, we prompt a new multi-dimensional algoithm to solve the traveling salesman problem based on the ant colony optimization algorithm and genetic algorithm. Ant Colony Optimization (ACO) is a heuristic algorithm which has been proven a successful technique and applied to a number of combinatorial optimization (CO) problems. The traveling salesman problem (TSP) is one of the most impo...

This paper presents the design of two Ant Colony Optimization (ACO) approaches and their improved variants on the degree-constrained minimum spanning tree (d-MST) problem. The first approach, which we call p-ACO, uses the vertices of the construction graph as solution components, and is motivated by the well-known Prim’s algorithm for constructing MST. The second approach, known as k-ACO, uses ...

Mobile ad hoc network (MANET) is a collection of wireless mobile nodes. It dynamically forms a temporary network without using any pre existing network infrastructure or centralized administration i.e. with minimal prior planning. All nodes have routing capabilities and forward data packets to other nodes in multi-hop fashion. As the network is dynamic, the network topology continuously experie...

Ant systems are flexible to implement and give possibility to scale because they are based on multi agent cooperation. The aim of this publication is to show the universal character of that solution and potentiality in implementing it in wide areas of applications. The increase of demand for effective methods of large document collections management is a sufficient stimulus to place the researc...

This study deals with performance-based design optimization (PBDO) of steel moment frames employing four different metaheuristics consisting of genetic algorithm (GA), ant colony optimization (ACO), harmony search (HS), and particle swarm optimization (PSO). In order to evaluate the seismic capacity of the structures, nonlinear pushover analysis is conducted (PBDO). This method is an iterative ...

We describe an Ant Colony Optimization (ACO) algorithm, ANT-MPE, for the most probable explanation problem in Bayesian network inference. After tuning its parameters settings, we compare ANTMPE with four other sampling and local search-based approximate algorithms: Gibbs Sampling, Forward Sampling, Multistart Hillclimbing, and Tabu Search. Experimental results on both artificial and real networ...