An ant colony based model to optimize parameters in industrial vision

Abstract

Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful.

Swarm Optimization for Tridimensional Point Cloud Reconstruction Using Supershapes

“…In contrast, population-based metaheuristics manipulate, in their search, a population of solutions to explore the search space, and thus increase the chances of obtaining the best possible approximate solution. Generally, metaheuristics are based in their definition on analogy with physics, with ethology (swarm optimization); for instance, we quote the particle swarm optimization [47] and the ant colony algorithm [48]; or with biology (class of evolutionary algorithms), for example the genetic algorithm [49].…”

Novel adaptive Discrete Cuckoo Search Algorithm for parameter optimization in computer vision

“…Exploring the solution space (or search space) within multiple locations at the time increases diversity and makes these algorithms more robust compared to trajectory-based ones [15]. Many population-based metaheuristics proposed to solve the optimization problems: Genetic Algorithms [16], Ant Colony Optimization (ACO) [17] and others as binary glowworm swarm optimization algorithm [18] or Cuckoo Search [19] and Firefly Algorithm [20], etc. In such approaches, each individual (or agent) in a population starts by building an approximate solution.…”

“…To furthermore attest efficiency of ADCS approach, 100 images of Berkeley database and images of mechanical objects are used in this experience, keeping the same experimental setup described above. Results obtained using the approach proposed in this work, the novel Adaptive Discrete Cukoo Search Algorithm, are confronted to different approaches established in our previous works as discrete particle swarm optimization (DPSO) [34], ant colony optimization (ACO) [17], and another approach in literature such as reinforcement learning (RL) [6]. Fig.…”