Hypercube FrameWork for ACO applied to timetabling

We present a resolution technique of the University course Timetabling problem (UCTP), this technique is based in the implementation of Hypercube framework using the Max-Min Ant System. We presented the structure of the problem and the design of resolution using this framework. A simplification of the UCTP problem is used, involving three types of hard restrictions and three types of soft restrictions. We solve experimental instances and competition instances the results are presented of comparative form to other techniques. We presented an appropriate construction graph and pheromone matrix representation. A representative instance is solved in addition to the schedules of the school of Computer science engineering of the Catholic University of Valparaiso. The results obtained for this instance appear. Finally the conclusions are given.IFIP International Conference on Artificial Intelligence in Theory and Practice - Evolutionary ComputationRed de Universidades con Carreras en Informática (RedUNCI

Ant Colony Optimization. A Computational Intelligence Technique

Ant colony optimization (ACO) is a novel computational technique inspired by a foraging behavior of ants has been successfully applied for solving real world optimization problems. This behavioral pattern inspires artificial ants for the search of solutions to the various types of optimization problems. ACO is a probabilistic search approach founded on the idea of evolutionary process. In this paper, we present an overview of ant colony optimization and ACO variants up to now. we also summarize various types of applications. Finally we focus on some research efforts directed at receiving a dipper understanding of the ant colony optimization algorithms

Hypercube FrameWork for ACO applied to timetabling

We present a resolution technique of the University course Timetabling problem (UCTP), this technique is based in the implementation of Hypercube framework using the Max-Min Ant System. We presented the structure of the problem and the design of resolution using this framework. A simplification of the UCTP problem is used, involving three types of hard restrictions and three types of soft restrictions. We solve experimental instances and competition instances the results are presented of comparative form to other techniques. We presented an appropriate construction graph and pheromone matrix representation. A representative instance is solved in addition to the schedules of the school of Computer science engineering of the Catholic University of Valparaiso. The results obtained for this instance appear. Finally the conclusions are given.IFIP International Conference on Artificial Intelligence in Theory and Practice - Evolutionary ComputationRed de Universidades con Carreras en Informática (RedUNCI

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 (“efficient”) 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 “quickly” (reasonable run-times), with “high” probability, provable “good” 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

An improved ant system algorithm for maximizing system reliability in the compatible module

This paper presents an improved Ant System (AS) algorithm called AS-2Swap for solving one of the reliability optimization problems. The objective is to selection a compatible module in order to maximize the system reliability and subject to budget constraints. This problem is NP-hard and formulated as a binary integer-programming problem with a nonlinear objective function. The proposed algorithm is based on the original AS algorithm and the improvement, focused on choosing the feasible solutions, neighborhood search with Swap technique for each loop of finding the solution. The implementation was tested by the five groups of data sets from the existing meta-heuristic found in the literature. The computational results show that the proposed algorithm can find the global optimal solution and is more accurate for larger problems

Optimal route planning of agricultural field operations using ant colony optimization

Farming operations efficiency is a crucial factor that determines the overall operational cost in agricultural production systems.  Improved efficiency can be achieved by implementing advanced planning methods for the execution of field operations dealing, especially with the routing and area coverage optimisation aspects. Recently, a new type of field area coverage patterns, the B-patterns, has been introduced.  B-patterns are the result of a combinatorial optimisation process that minimizes operational criterions such as, the operational time, non-working travelled distance, fuel consumption etc.  In this paper an algorithmic approach for the generation of B-patterns based on ant colony optimisation is presented.  Ant colony optimization metaheuristic was chosen for the solution of the graph optimisation problem inherent in the generation of B-patterns.  Experimental results on two selected fields were presented for the demonstration of the effectiveness of the proposed approach. Based on the results, it was shown that it is feasible to use ant colony optimization for the generation of optimal routes for field area coverage while tests made on the resulting routes indicated that they can be followed by any farm machine equipped with auto-steering and navigation systems

ACOustic: A nature-inspired exploration indicator for ant colony optimization

A statistical machine learning indicator, ACOustic, is proposed to evaluate the exploration behavior in the iterations of ant colony optimization algorithms. This idea is inspired by the behavior of some parasites in their mimicry to the queens’ acoustics of their ant hosts.The parasites’ reaction results from their ability to indicate the state of penetration.The proposed indicator solves the problem of robustness that results from the difference of magnitudes in the distance’s matrix, especially when combinatorial optimization problems with rugged fitness landscape are applied.The performance of the proposed indicator is evaluated against the existing indicators in six variants of ant colony optimization algorithms.Instances for travelling salesman problem and quadratic assignment problem are used in the experimental evaluation.The analytical results showed that the proposed indicator is more informative and more robust