Evaluation of Ant Colony Optimization Algorithm Compared to Genetic Algorithm, Dynamic Programming and Branch and Bound Algorithm Regarding Travelling Salesman Problem

We use ant colony optimization (ACO) algorithm for solving combinatorial optimization problems such as the traveling salesman problem. Some applications of ACO are: vehicle routing, sequential ordering, graph coloring, routing in communications networks, etc. In this paper, we compare the performance of ACO to that of a few other state-of-the-art algorithms currently in use and thus measure the effectiveness of ACO as one of the major optimization algorithms in regard with a few more algorithms. The performance of the algorithms is measured by observing their capacity to solve a traveling salesman problem (TSP). This paper will help to find the proper algorithm to be used for routing problems in different real-life situations

Model of Optimal Cooperative Reconnaissance and its Solution using Metaheuristic Methods

The model of optimal cooperative reconnaissance as a part of the tactical decision support system to aid commanders in their decision-making processes is presented. The model represents one of the models of military tactics implemented in the system to plan the ground reconnaissance operation for the commander optimally. The main goal of the model is to explore the area of interest by multiple military elements (scouts, UAVs, UGVs) as quickly as possible. A metaheuristic solution to this problem which combines two probabilistic methods: simulated annealing and the ant colony optimisation algorithm is proposed. In the first part of this study, the optimal cooperative reconnaissance problem is formulated. Then, metaheuristic solution, which is composed of three independent steps, is presented. Finally, experiments are conducted to verify the approach to this problem

How Crossover Speeds Up Building-Block Assembly in Genetic Algorithms

We re-investigate a fundamental question: how effective is crossover in Genetic Algorithms in combining building blocks of good solutions? Although this has been discussed controversially for decades, we are still lacking a rigorous and intuitive answer. We provide such answers for royal road functions and OneMax, where every bit is a building block. For the latter we show that using crossover makes every (\mu+\lambda) Genetic Algorithm at least twice as fast as the fastest evolutionary algorithm using only standard bit mutation, up to small-order terms and for moderate \mu and \lambda. Crossover is beneficial because it effectively turns fitness-neutral mutations into improvements by combining the right building blocks at a later stage. Compared to mutation-based evolutionary algorithms, this makes multi-bit mutations more useful. Introducing crossover changes the optimal mutation rate on OneMax from 1/n to (1+\sqrt{5})/2 \cdot 1/n \approx 1.618/n. This holds both for uniform crossover and k-point crossover. Experiments and statistical tests confirm that our findings apply to a broad class of building-block functions

Running Time Analysis of Ant Colony Optimization for Shortest Path Problems

AbstractAnt Colony Optimization (ACO) is a modern and very popular optimization paradigm inspired by the ability of ant colonies to find shortest paths between their nest and a food source. Despite its popularity, the theory of ACO is still in its infancy and a solid theoretical foundation is needed. We present bounds on the running time of different ACO systems for shortest path problems. First, we improve previous results by Attiratanasunthron and Fakcharoenphol [Information Processing Letters 105 (3) (2008) 88–92] for single-destination shortest paths and extend their results from DAGs to arbitrary directed graphs. Our upper bound is asymptotically tight for large evaporation factors, holds with high probability, and transfers to the all-pairs shortest paths problem. There, a simple mechanism for exchanging information between ants with different destinations yields a significant improvement. A comparison with evolutionary and genetic approaches indicates that ACO is among the best known metaheuristics for the all-pairs shortest paths problem