Towards a multilevel ant colony optimization

Masteroppgave i Informasjons- og kommunikasjonsteknologi IKT590 Universitetet i Agder 2014Ant colony optimization is a metaheuristic approach for solving combinatorial optimization problems which belongs to swarm intelligence techniques. Ant colony optimization algorithms are one of the most successful strands of swarm intelligence which has already shown very good performance in many combinatorial problems and for some real applications. This thesis introduces a new multilevel approach for ant colony optimization to solve the NP-hard problems shortest path and traveling salesman. We have reviewed different elements of multilevel algorithm which helped us in construction of our proposed multilevel ant colony optimization solution. We for comparison purposes implemented our own multi-threaded variant Dijkstra for solving shortest path to compare it with single level and multilevel ant colony optimization and reviewed different techniques such as genetic algorithms and Dijkstra’s algorithm. Our proposed multilevel ant colony optimization was developed based on the single level ant colony optimization which we both implemented. We have applied the novel multilevel ant colony optimization to solve the shortest path and traveling salesman problem. We show that the multilevel variant of ant colony optimization outperforms single level. The experimental results conducted demonstrate the overall performance of multilevel in comparison to the single level ant colony optimization, displaying a vast improvement when employing a multilevel approach in contrast to the classical single level approach. These results gave us a better understanding of the problems and provide indications for further research

Ant Colony Algorithm for Evolutionary Design of Arithmetic Circuits

Absfruct-Evolutionary computation is a niw field of research in which hardware design is pursued by deriving inspiration from biological organisms. This new paradigm is expected to radically change the synthesis procedures in a way that allows discovering ‘novel designs andlor more efficient circuits. In this paper, a multi objective optimization strategy for design of arithmetic circuits based on Ant Colony optimization algorithm is presented. Results are compared with those obtained using other techniques. Index Terms- Logic Design, Evolutionary Computation, Ant Colony Optimization, Multiobjective Optimization, Fuzzy Logic

Ant Colony Algorithm for Evolutionary Design of Arithmetic Circuits

Absfruct-Evolutionary computation is a niw field of research in which hardware design is pursued by deriving inspiration from biological organisms. This new paradigm is expected to radically change the synthesis procedures in a way that allows discovering ‘novel designs andlor more efficient circuits. In this paper, a multi objective optimization strategy for design of arithmetic circuits based on Ant Colony optimization algorithm is presented. Results are compared with those obtained using other techniques. Index Terms- Logic Design, Evolutionary Computation, Ant Colony Optimization, Multiobjective Optimization, Fuzzy Logic

Cognitive Ant Colony Optimization : a new framework in swarm intelligence

Ant Colony Optimization (ACO) algorithms which belong to metaheuristic algorithms and swarm intelligence algorithms have been the focus of much attention in the quest to solve optimization problems. These algorithms are inspired by colonies of ants foraging for food from their nest and have been considered state-of-art methods for solving both discrete and continuous optimization problems. One of the most important phases of ACO algorithms is the construction phase during which an ant builds a partial solution and develops a state transition strategy. There have been a number of studies on the state transition strategy. However, most of the research studies look at how to improve pheromone updates rather than at how the ant itself makes a decision to move from a current position to the next position.The aim of this research is to develop a novel state transition strategy for Ant Colony Optimization algorithms that can improve the overall performance of the algorithms. The research has shown that the state transition strategy in ACO can be improved by introducing non-rational decision-making.The new proposed algorithm is called Cognitive Ant Colony Optimization and uses a new concept of decision-making taken from cognitive behaviour theory. In this proposed algorithm, the ACO has been endowed with non-rational behaviour in order to improve the overall optimization behaviour of ants during the process. This new behaviour will use a non-rational model named prospect theory (Kahneman & Tversky, 1979) to select the transition movements of the ants in the colony in order to improve the overall search capability and the convergence of the algorithm. The new Cognitive Ant Colony Optimization framework has been tested on the Travelling Salesman Problem (TSP), Water Distribution System and Continuous optimization problems. The results obtained show that our algorithm improved the performance of previous ACO techniques considerably

Orthogonal methods based ant colony search for solving continuous optimization problems

Research into ant colony algorithms for solving continuous optimization problems forms one of the most significant and promising areas in swarm computation. Although traditional ant algorithms are designed for combinatorial optimization, they have shown great potential in solving a wide range of optimization problems, including continuous optimization. Aimed at solving continuous problems effectively, this paper develops a novel ant algorithm termed "continuous orthogonal ant colony" (COAC), whose pheromone deposit mechanisms would enable ants to search for solutions collaboratively and effectively. By using the orthogonal design method, ants in the feasible domain can explore their chosen regions rapidly and e±ciently. By implementing an "adaptive regional radius" method, the proposed algorithm can reduce the probability of being trapped in local optima and therefore enhance the global search capability and accuracy. An elitist strategy is also employed to reserve the most valuable points. The performance of the COAC is compared with two other ant algorithms for continuous optimization of API and CACO by testing seventeen functions in the continuous domain. The results demonstrate that the proposed COAC algorithm outperforms the others