Quang Huy Nguyen

In a constraint optimization problem for multiple agents, the agents have conflicting preferences in the final solution and the goal is to find an optimal assignment that maximizes total utilities of all agents. Two major challenges when solving constraint optimization problems for multiple agents are the complexity of finding optimal solution and the incentive compatibility for participating agents. First, computing the optimal solution for large optimization problems are computationally infeasible and it can only be solved approximately by local search algorithms. Second, ensuring honest elicitation among self-interested agents is computationally expensive. It has been shown that the only known mechanism that guarantees truthfulness among agents requires computing optimal solutions, and sub-optimal solutions for such a mechanism will break the incentive compatibility ([2]). The long-term goal of our research is to solve these two challenges by using randomization in local search algorithms to find near-optimal solutions while ensuring incentive compatibility for bounded-rational agents. Our work is based on the observation that in real-world settings, the potential for manipulation is limited by uncertainty and risk. This uncertainty makes it difficult for a manipulator to predict the consequences of his manipulation and thus makes attempts at manipulating it uninteresting.

A cellular genetic algorithm (CGA) is a decentralized form of GA where individuals in a population are usually arranged in a 2D grid and interactions among individuals are restricted to a set neighborhood. In this paper, we extend the notion of cellularity to memetic algorithms (MA), a configuration termed cellular memetic algorithm (CMA). In addition, we propose adaptive mechanisms that tailor the amount of exploration versus exploitation of local solutions carried out by the CMA. We systematically benchmark this adaptive mechanism and provide evidence that the resulting adaptive CMA outperforms other methods both in the quality of solutions obtained and the number of function evaluations for a range of continuous optimization problems.

In recent years, there has been an increase in research activities on Memetic Algorithm (MA). MA works with memes; a meme being defined as "the basic unit of cultural transmission, or imitation" [5]. In this respect, a Memetic Algorithm essentially refers to "an algorithm that mimics the mechanisms of cultural evolution". To date, there has been significant effort in bringing

In this paper, we present a flocking model where agents are equipped with navigational and obstacle avoidance capabilities that conform to user defined paths and formation shape requirements. In particular, we adopt an agent-based paradigm to achieve flexible formation handling at both the individual and flock level. The proposed model is studied under three different scenarios where flexible flock formations are produced automatically via algorithmic means to: 1) navigate around dynamically emerging obstacles, 2) navigate through narrow space and 3) navigate along path with sharp curvatures, hence minimizing the manual effort of human animators. Simulation results showed that the proposed model leads to highly realistic, flexible and real-time reactive flock formations.