Presented at Universiti Teknologi Petronas
The Simulated Kalman Filter: A New Metaheuristic Approach to Optimization
Universiti Teknologi Petronas
6 April 2017
Abstract - In this talk, a novel estimation-based metaheuristic optimization algorithm, named as Simulated Kalman Filter (SKF) and its variants will be presented for global optimization problems. This new algorithm is inspired by the estimation capability of the Kalman Filter. In principle, the state estimation problem is regarded as an optimization problem and each agent in SKF acts as a Kalman Filter. An agent in the population finds a solution to the optimization problem using a standard Kalman Filter framework, which includes a simulated measurement process and a best-so-far solution as a reference. It is found that the SKF algorithm is a promising approach and competitive against some well-known metaheuristic algorithms, such as Genetic Algorithm, Particle Swarm Optimization, and Gravitational Search Algorithms. Finally, hybridization of SKF with other metaheuristic will be presented as well as the extension of SKF for solving combinatorial optimization problems.