1,247 research outputs found
Anti-pheromone as a tool for better exploration of search space
Many animals use chemical substances known as pheromones to induce behavioural changes in other members of the same species. The use of pheromones by ants in particular has lead to the development of a number of computational analogues of ant colony behaviour including Ant Colony Optimisation. Although many animals use a range of pheromones in their communication, ant algorithms have typically focused on the use of just one, a substance that encourages succeeding generations of (artificial) ants to follow the same path as previous generations. Ant algorithms for multi-objective optimisation and those employing multiple colonies have made use of more than one pheromone, but the interactions between these different pheromones are largely simple extensions of single criterion, single colony ant algorithms. This paper investigates an alternative form of interaction between normal pheromone and anti-pheromone. Three variations of Ant Colony System that apply the anti-pheromone concept in different ways are described and tested against benchmark travelling salesman problems. The results indicate that the use of anti-pheromone can lead to improved performance. However, if anti-pheromone is allowed too great an influence on ants' decisions, poorer performance may result
Optimal power harness routing for small-scale satellites
This paper presents an approach to optimal power harness design based on a modified ant colony optimisation algorithm. The optimisation of the harness routing topology is formulated as a constrained multi-objective optimisation problem in which the main objectives are to minimise the length (and therefore the mass) of the harness. The modified ant colony optimisation algorithm automatically routes different types of wiring, creating the optimal harness layout. During the optimisation the length, mass and bundleness of the cables are computed and used as cost functions. The optimisation algorithm works incrementally on a finite set of waypoints, forming a tree, by adding and evaluating one branch at a time, utilising a set of heuristics using the cable length and cable bundling as criteria to select the optimal path. Constraints are introduced as forbidden waypoints through which digital agents (hereafter called ants) cannot travel. The new algorithm developed will be applied to the design of the harness of a small satellite, with results highlighting the capabilities and potentialities of the code
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Combinatorial optimization and metaheuristics
Today, combinatorial optimization is one of the youngest and most active areas of discrete mathematics. It is a branch of optimization in applied mathematics and computer science, related to operational research, algorithm theory and computational complexity theory. It sits at the intersection of several fields, including artificial intelligence, mathematics and software engineering. Its increasing interest arises for the fact that a large number of scientific and industrial problems can be formulated as abstract combinatorial optimization problems, through graphs and/or (integer) linear programs. Some of these problems have polynomial-time (âefficientâ) algorithms, while most of them are NP-hard, i.e. it is not proved that they can be solved in polynomial-time. Mainly, it means that it is not possible to guarantee that an exact solution to the problem can be found and one has to settle for an approximate solution with known performance guarantees. Indeed, the goal of approximate methods is to find âquicklyâ (reasonable run-times), with âhighâ probability, provable âgoodâ solutions (low error from the real optimal solution). In the last 20 years, a new kind of algorithm commonly called metaheuristics have emerged in this class, which basically try to combine heuristics in high level frameworks aimed at efficiently and effectively exploring the search space. This report briefly outlines the components, concepts, advantages and disadvantages of different metaheuristic approaches from a conceptual point of view, in order to analyze their similarities and differences. The two very significant forces of intensification and diversification, that mainly determine the behavior of a metaheuristic, will be pointed out. The report concludes by exploring the importance of hybridization and integration methods
Using 2-Opt based evolution strategy for travelling salesman problem
Harmony search algorithm that matches the (”+ 1) evolution strategy, is a heuristic method simulated by the process of music improvisation. In this paper, a harmony search algorithm is directly used for the travelling salesman problem. Instead of conventional selection operators such as roulette wheel, the transformation of real number values of harmony search algorithm to order index of vertex representation and improvement of solutions are obtained by using the 2-Opt local search algorithm. Then, the obtained algorithm is tested on two different parameter groups of TSPLIB. The proposed method is compared with classical 2-Opt which randomly started at each step and best known solutions of test instances from TSPLIB. It is seen that the proposed algorithm offers valuable solutions
Travel route scheduling based on userâs preferences using simulated annealing
Nowadays, traveling has become a routine activity for many people, so that many researchers have developed studies in the tourism domain, especially for the determination of tourist routes. Based on prior work, the problem of determining travel route is analogous to finding the solution for travelling salesman problem (TSP). However, the majority of works only dealt with generating the travel route within one day and also did not take into account several userâs preference criteria. This paper proposes a model for generating a travel route schedule within a few days, and considers some user needs criteria, so that the determination of a travel route can be considered as a multi-criteria issue. The travel route is generated based on several constraints, such as travel time limits per day, opening/closing hours and the average length of visit for each tourist destination. We use simulated annealing method to generate the optimum travel route. Based on evaluation result, the optimality of the travel route generated by the system is not significantly different with ant colony result. However, our model is far more superior in running time compared to Ant Colony method
MRF-based image segmentation using Ant Colony System
In this paper, we propose a novel method for image segmentation that we call ACS-MRF method. ACS-MRF is a hybrid ant colony system coupled with a local search. We show how a colony of cooperating ants are able to estimate the labels field and minimize the MAP estimate. Cooperation between ants is performed by exchanging information through pheromone updating. The obtained results show the efficiency of the new algorithm, which is able to compete with other stochastic optimization methods like Simulated annealing and Genetic algorithm in terms of solution quality
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